cytoBand Chromosome Band Chromosome Bands Mapping and Sequencing Description This track shows chromosome bands annotated by FlyBase (D. melanogaster version 6.02). Credits Thanks to FlyBase for providing these annotations. 
cytoBandIdeo Chromosome Band (Low-res) Chromosome Bands (Low-resolution for Chromosome Ideogram) Mapping and Sequencing 
cons27way Conservation Multiz Alignment & Conservation (27 Species) Comparative Genomics Description This track shows multiple alignments of 27 species and measurements of evolutionary conservation using two methods (phastCons and phyloP) from the PHAST package, for all 27 species. The multiple alignments were generated using multiz and other tools in the UCSC/Penn State Bioinformatics comparative genomics alignment pipeline. Conserved elements identified by phastCons are also displayed in this track. PhastCons (which has been used in previous Conservation tracks) is a hidden Markov model-based method that estimates the probability that each nucleotide belongs to a conserved element, based on the multiple alignment. It considers not just each individual alignment column, but also its flanking columns. By contrast, phyloP separately measures conservation at individual columns, ignoring the effects of their neighbors. As a consequence, the phyloP plots have a less smooth appearance than the phastCons plots, with more "texture" at individual sites. The two methods have different strengths and weaknesses. PhastCons is sensitive to "runs" of conserved sites, and is therefore effective for picking out conserved elements. PhyloP, on the other hand, is more appropriate for evaluating signatures of selection at particular nucleotides or classes of nucleotides (e.g., third codon positions, or first positions of miRNA target sites). Another important difference is that phyloP can measure acceleration (faster evolution than expected under neutral drift) as well as conservation (slower than expected evolution). In the phyloP plots, sites predicted to be conserved are assigned positive scores (and shown in blue), while sites predicted to be fast-evolving are assigned negative scores (and shown in red). The absolute values of the scores represent -log p-values under a null hypothesis of neutral evolution. The phastCons scores, by contrast, represent probabilities of negative selection and range between 0 and 1. Both phastCons and phyloP treat alignment gaps and unaligned nucleotides as missing data. Missing sequence in the assemblies is highlighted in the track display by regions of yellow when zoomed out and Ns displayed at base level (see Gap Annotation, below). SpeciesRelease dateUCSC versionalignment type D. melanogasterAug. 2014BDGP Release 6 + ISO1 MT/dm6reference D. simulansApr. 2005WUGSC 1.0/droSim1syntenic D. sechelliaOct. 2005Broad/droSec1syntenic D. yakubaJun. 2006Flybase dyak_caf1/droYak3syntenic D. erectaFeb. 2006Agencourt CAF1/droEre2syntenic D. biarmipesMar. 2013BCM Dbia_2.0/droBia2syntenic D. suzukiiSep. 2013BGI Dsuzukii.v01/droSuz1syntenic D. ananassaeFeb. 2006Agencourt CAF1/droAna3syntenic D. bipectinataMar. 2013BCM Dbip_2.0/droBip2syntenic D. eugracilisMar. 2013modENCODE Deug_2.0/droEug2syntenic D. elegansMar. 2013BCM Dele_2.0/droEle2syntenic D. kikkawaiMar. 2013BCM Dkik_2.0/droKik2syntenic D. takahashiiMar. 2013BCM Dtak_2.0/droTak2syntenic D. rhopaloaFeb. 2013modENCODE Drho_2.0/droRho2syntenic D. ficusphilaMar. 2013BCM Dfic_2.0/droFic2syntenic D. pseudoobscuraApr. 2013BCM Dpse_3.0/droPse3syntenic D. persimilisOct. 2005Broad/droPer1syntenic D. mirandaApr. 2013U.C. Berkeley DroMir_2.2/droMir2syntenic D. willistoniAug. 2006JCVI dwil_caf1/droWil2syntenic D. virilisFeb. 2006Agencourt CAF1/droVir3syntenic D. mojavensisFeb. 2006Agencourt CAF1/droMoj3syntenic D. albomicansMay. 2012Kunming DroAlb_1.0/droAlb1syntenic D. grimshawiFeb. 2006Agencourt CAF1/droGri2syntenic Musca domesticaApr. 2013Glossina M_domestica-2.0.2/musDom2net Anopheles gambiaeFeb. 2003IAGEC MOZ2/anoGam1net Apis melliferaNov. 2010BCM Amel_4.5/apiMel4net Tribolium castaneumSep. 2005BCM 2.0/triCas2net Table 1. Genome assemblies included in the 27-way Conservation track. Downloads for data in this track are available: Multiz alignments (MAF format), and phylogenetic trees PhyloP conservation (WIG format) PhastCons conservation (WIG format) Display Conventions and Configuration The track configuration options allow the user to display the three different sets of scores, all, birds or vertebrate, individually or all simultaneously. In full and pack display modes, conservation scores are displayed as a wiggle track (histogram) in which the height reflects the value of the score. The conservation wiggles can be configured in a variety of ways to highlight different aspects of the displayed information. Click the Graph configuration help link for an explanation of the configuration options. Pairwise alignments of each species to the D. melanogaster genome are displayed below the conservation histogram as a grayscale density plot (in pack mode) or as a wiggle (in full mode) that indicates alignment quality. In dense display mode, conservation is shown in grayscale using darker values to indicate higher levels of overall conservation as scored by phastCons. Checkboxes on the track configuration page allow selection of the species to include in the pairwise display. Configuration buttons are available to select all of the species (Set all), deselect all of the species (Clear all), or use the default settings (Set defaults). Note that excluding species from the pairwise display does not alter the the conservation score display. To view detailed information about the alignments at a specific position, zoom the display in to 30,000 or fewer bases, then click on the alignment. Gap Annotation The Display chains between alignments configuration option enables display of gaps between alignment blocks in the pairwise alignments in a manner similar to the Chain track display. The following conventions are used: Single line: No bases in the aligned species. Possibly due to a lineage-specific insertion between the aligned blocks in the D. melanogaster genome or a lineage-specific deletion between the aligned blocks in the aligning species. Double line: Aligning species has one or more unalignable bases in the gap region. Possibly due to excessive evolutionary distance between species or independent indels in the region between the aligned blocks in both species. Pale yellow coloring: Aligning species has Ns in the gap region. Reflects uncertainty in the relationship between the DNA of both species, due to lack of sequence in relevant portions of the aligning species. Genomic Breaks Discontinuities in the genomic context (chromosome, scaffold or region) of the aligned DNA in the aligning species are shown as follows: Vertical blue bar: Represents a discontinuity that persists indefinitely on either side, e.g. a large region of DNA on either side of the bar comes from a different chromosome in the aligned species due to a large scale rearrangement. Green square brackets: Enclose shorter alignments consisting of DNA from one genomic context in the aligned species nested inside a larger chain of alignments from a different genomic context. The alignment within the brackets may represent a short misalignment, a lineage-specific insertion of a transposon in the D. melanogaster genome that aligns to a paralogous copy somewhere else in the aligned species, or other similar occurrence. Base Level When zoomed-in to the base-level display, the track shows the base composition of each alignment. The numbers and symbols on the Gaps line indicate the lengths of gaps in the D. melanogaster sequence at those alignment positions relative to the longest non-D. melanogaster sequence. If there is sufficient space in the display, the size of the gap is shown. If the space is insufficient and the gap size is a multiple of 3, a "*" is displayed; other gap sizes are indicated by "+". Codon translation is available in base-level display mode if the displayed region is identified as a coding segment. To display this annotation, select the species for translation from the pull-down menu in the Codon Translation configuration section at the top of the page. Then, select one of the following modes: No codon translation: The gene annotation is not used; the bases are displayed without translation. Use default species reading frames for translation: The annotations from the genome displayed in the Default species to establish reading frame pull-down menu are used to translate all the aligned species present in the alignment. Use reading frames for species if available, otherwise no translation: Codon translation is performed only for those species where the region is annotated as protein coding. Use reading frames for species if available, otherwise use default species: Codon translation is done on those species that are annotated as being protein coding over the aligned region using species-specific annotation; the remaining species are translated using the default species annotation. Codon translation uses the following gene tracks as the basis for translation, depending on the species chosen (Table 2). Gene TrackSpecies RefSeq GenesD. melanogaster Ensembl Genes v68D. erecta, D. ananassae, Anopheles gambiae Table 2. Gene tracks used for codon translation. Methods Pairwise alignments with the D. melanogaster genome were generated for each species using lastz from repeat-masked genomic sequence. Pairwise alignments were then linked into chains using a dynamic programming algorithm that finds maximally scoring chains of gapless subsections of the alignments organized in a kd-tree. The scoring matrix and parameters for pairwise alignment and chaining were tuned for each species based on phylogenetic distance from the reference. High-scoring chains were then placed along the genome, with gaps filled by lower-scoring chains, to produce an alignment net. For more information about the chaining and netting process and parameters for each species, see the description pages for the Chain and Net tracks. An additional filtering step was introduced in the generation of the 27-way conservation track to reduce the number of paralogs and pseudogenes from the high-quality assemblies and the suspect alignments from the low-quality assemblies: the pairwise alignments of the bird assemblies were filtered based on synteny; those for the human and mouse genomes were filtered to retain only alignments of best quality in both the target and query ("reciprocal best"). The resulting best-in-genome pairwise alignments were progressively aligned using multiz/autoMZ, following the tree topology diagrammed above, to produce multiple alignments. The multiple alignments were post-processed to add annotations indicating alignment gaps, genomic breaks, and base quality of the component sequences. The annotated multiple alignments, in MAF format, are available for bulk download. An alignment summary table containing an entry for each alignment block in each species was generated to improve track display performance at large scales. Framing tables were constructed to enable visualization of codons in the multiple alignment display. Phylogenetic Tree Model Both phastCons and phyloP are phylogenetic methods that rely on a tree model containing the tree topology, branch lengths representing evolutionary distance at neutrally evolving sites, the background distribution of nucleotides, and a substitution rate matrix. The all species tree model for this track was generated using the phyloFit program from the PHAST package (REV model, EM algorithm, medium precision) using multiple alignments of 4-fold degenerate sites extracted from the 27-way alignment (msa_view). The 4d sites were derived from the Xeno RefSeq gene set, filtered to select single-coverage long transcripts. This same tree model was used in the phyloP calculations, however their background frequencies were modified to maintain reversibility. The resulting tree model for all species. PhastCons Conservation The phastCons program computes conservation scores based on a phylo-HMM, a type of probabilistic model that describes both the process of DNA substitution at each site in a genome and the way this process changes from one site to the next (Felsenstein and Churchill 1996, Yang 1995, Siepel and Haussler 2005). PhastCons uses a two-state phylo-HMM, with a state for conserved regions and a state for non-conserved regions. The value plotted at each site is the posterior probability that the corresponding alignment column was "generated" by the conserved state of the phylo-HMM. These scores reflect the phylogeny (including branch lengths) of the species in question, a continuous-time Markov model of the nucleotide substitution process, and a tendency for conservation levels to be autocorrelated along the genome (i.e., to be similar at adjacent sites). The general reversible (REV) substitution model was used. Unlike many conservation-scoring programs, phastCons does not rely on a sliding window of fixed size; therefore, short highly-conserved regions and long moderately conserved regions can both obtain high scores. More information about phastCons can be found in Siepel et al. 2005. The phastCons parameters used were: expected-length=45, target-coverage=0.3, rho=0.3. PhyloP Conservation The phyloP program supports several different methods for computing p-values of conservation or acceleration, for individual nucleotides or larger elements ( http://compgen.cshl.edu/phast/). Here it was used to produce separate scores at each base (--wig-scores option), considering all branches of the phylogeny rather than a particular subtree or lineage (i.e., the --subtree option was not used). The scores were computed by performing a likelihood ratio test at each alignment column (--method LRT), and scores for both conservation and acceleration were produced (--mode CONACC). Conserved Elements The conserved elements were predicted by running phastCons with the --viterbi option. The predicted elements are segments of the alignment that are likely to have been "generated" by the conserved state of the phylo-HMM. Each element is assigned a log-odds score equal to its log probability under the conserved model minus its log probability under the non-conserved model. The "score" field associated with this track contains transformed log-odds scores, taking values between 0 and 1000. (The scores are transformed using a monotonic function of the form a * log(x) + b.) The raw log odds scores are retained in the "name" field and can be seen on the details page or in the browser when the track's display mode is set to "pack" or "full". Credits This track was created using the following programs: Alignment tools: lastz (formerly blastz) and multiz by Minmei Hou, Scott Schwartz and Webb Miller of the Penn State Bioinformatics Group Chaining and Netting: axtChain, chainNet by Jim Kent at UCSC Conservation scoring: phastCons, phyloP, phyloFit, tree_doctor, msa_view and other programs in PHAST by Adam Siepel at Cold Spring Harbor Laboratory (original development done at the Haussler lab at UCSC). MAF Annotation tools: mafAddIRows by Brian Raney, UCSC; mafAddQRows by Richard Burhans, Penn State; genePredToMafFrames by Mark Diekhans, UCSC Tree image generator: phyloPng by Galt Barber, UCSC Conservation track display: Kate Rosenbloom, Hiram Clawson (wiggle display), and Brian Raney (gap annotation and codon framing) at UCSC The phylogenetic tree is based on Murphy et al. (2001) and general consensus in the vertebrate phylogeny community as of March 2007. References Phylo-HMMs, phastCons, and phyloP: Felsenstein J, Churchill GA. A Hidden Markov Model approach to variation among sites in rate of evolution. Mol Biol Evol. 1996 Jan;13(1):93-104. PMID: 8583911 Pollard KS, Hubisz MJ, Rosenbloom KR, Siepel A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 2010 Jan;20(1):110-21. PMID: 19858363; PMC: PMC2798823 Siepel A, Bejerano G, Pedersen JS, Hinrichs AS, Hou M, Rosenbloom K, Clawson H, Spieth J, Hillier LW, Richards S, et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 2005 Aug;15(8):1034-50. PMID: 16024819; PMC: PMC1182216 Siepel A, Haussler D. Phylogenetic Hidden Markov Models. In: Nielsen R, editor. Statistical Methods in Molecular Evolution. New York: Springer; 2005. pp. 325-351. Yang Z. A space-time process model for the evolution of DNA sequences. Genetics. 1995 Feb;139(2):993-1005. PMID: 7713447; PMC: PMC1206396 Chain/Net: Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D. Evolution's cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11484-9. PMID: 14500911; PMC: PMC208784 Multiz: Blanchette M, Kent WJ, Riemer C, Elnitski L, Smit AF, Roskin KM, Baertsch R, Rosenbloom K, Clawson H, Green ED, et al. Aligning multiple genomic sequences with the threaded blockset aligner. Genome Res. 2004 Apr;14(4):708-15. PMID: 15060014; PMC: PMC383317 Lastz (formerly Blastz): Chiaromonte F, Yap VB, Miller W. Scoring pairwise genomic sequence alignments. Pac Symp Biocomput. 2002:115-26. PMID: 11928468 Harris RS. Improved pairwise alignment of genomic DNA. Ph.D. Thesis. Pennsylvania State University, USA. 2007. Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D, Miller W. Human-mouse alignments with BLASTZ. Genome Res. 2003 Jan;13(1):103-7. PMID: 12529312; PMC: PMC430961 Phylogenetic Tree: Murphy WJ, Eizirik E, O'Brien SJ, Madsen O, Scally M, Douady CJ, Teeling E, Ryder OA, Stanhope MJ, de Jong WW, Springer MS. Resolution of the early placental mammal radiation using Bayesian phylogenetics. Science. 2001 Dec 14;294(5550):2348-51. PMID: 11743200 
cons27wayViewalign Multiz Alignments Multiz Alignment & Conservation (27 Species) Comparative Genomics 
multiz27way Multiz Align Multiz Alignments of 27 insects Comparative Genomics 
cons27wayViewphastcons Element Conservation (phastCons) Multiz Alignment & Conservation (27 Species) Comparative Genomics 
phastCons27way phastCons 27 insects conservation by PhastCons Comparative Genomics 
cons27wayViewelements Conserved Elements Multiz Alignment & Conservation (27 Species) Comparative Genomics 
phastConsElements27way Cons Elements 27 insects Conserved Elements Comparative Genomics 
cons27wayViewphyloP Basewise Conservation (phyloP) Multiz Alignment & Conservation (27 Species) Comparative Genomics 
phyloP27way phyloP 27 insects Basewise Conservation by PhyloP Comparative Genomics 
cpgIslandExt CpG Islands CpG Islands (Islands < 300 Bases are Light Green) Expression and Regulation Description CpG islands are associated with genes, particularly housekeeping genes, in vertebrates. CpG islands are typically common near transcription start sites and may be associated with promoter regions. Normally a C (cytosine) base followed immediately by a G (guanine) base (a CpG) is rare in vertebrate DNA because the Cs in such an arrangement tend to be methylated. This methylation helps distinguish the newly synthesized DNA strand from the parent strand, which aids in the final stages of DNA proofreading after duplication. However, over evolutionary time, methylated Cs tend to turn into Ts because of spontaneous deamination. The result is that CpGs are relatively rare unless there is selective pressure to keep them or a region is not methylated for some other reason, perhaps having to do with the regulation of gene expression. CpG islands are regions where CpGs are present at significantly higher levels than is typical for the genome as a whole. The unmasked version of the track displays potential CpG islands that exist in repeat regions and would otherwise not be visible in the repeat masked version. By default, only the masked version of the track is displayed. To view the unmasked version, change the visibility settings in the track controls at the top of this page. Methods CpG islands were predicted by searching the sequence one base at a time, scoring each dinucleotide (+17 for CG and -1 for others) and identifying maximally scoring segments. Each segment was then evaluated for the following criteria: GC content of 50% or greater length greater than 200 bp ratio greater than 0.6 of observed number of CG dinucleotides to the expected number on the basis of the number of Gs and Cs in the segment The entire genome sequence, masking areas included, was used for the construction of the track Unmasked CpG. The track CpG Islands is constructed on the sequence after all masked sequence is removed. The CpG count is the number of CG dinucleotides in the island. The Percentage CpG is the ratio of CpG nucleotide bases (twice the CpG count) to the length. The ratio of observed to expected CpG is calculated according to the formula (cited in Gardiner-Garden et al. (1987)): Obs/Exp CpG = Number of CpG * N / (Number of C * Number of G) where N = length of sequence. The calculation of the track data is performed by the following command sequence: twoBitToFa assembly.2bit stdout | maskOutFa stdin hard stdout \ | cpg_lh /dev/stdin 2&gt; cpg_lh.err \ | awk '{&dollar;2 = &dollar;2 - 1; width = &dollar;3 - &dollar;2; printf("%s\t%d\t%s\t%s %s\t%s\t%s\t%0.0f\t%0.1f\t%s\t%s\n", &dollar;1, &dollar;2, &dollar;3, &dollar;5, &dollar;6, width, &dollar;6, width*&dollar;7*0.01, 100.0*2*&dollar;6/width, &dollar;7, &dollar;9);}' \ | sort -k1,1 -k2,2n &gt; cpgIsland.bed The unmasked track data is constructed from twoBitToFa -noMask output for the twoBitToFa command. Data access CpG islands and its associated tables can be explored interactively using the REST API, the Table Browser or the Data Integrator. All the tables can also be queried directly from our public MySQL servers, with more information available on our help page as well as on our blog. The source for the cpg_lh program can be obtained from src/utils/cpgIslandExt/. The cpg_lh program binary can be obtained from: http://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/cpg_lh (choose "save file") Credits This track was generated using a modification of a program developed by G. Miklem and L. Hillier (unpublished). References Gardiner-Garden M, Frommer M. CpG islands in vertebrate genomes. J Mol Biol. 1987 Jul 20;196(2):261-82. PMID: 3656447 
cpgIslandSuper CpG Islands CpG Islands (Islands < 300 Bases are Light Green) Expression and Regulation Description CpG islands are associated with genes, particularly housekeeping genes, in vertebrates. CpG islands are typically common near transcription start sites and may be associated with promoter regions. Normally a C (cytosine) base followed immediately by a G (guanine) base (a CpG) is rare in vertebrate DNA because the Cs in such an arrangement tend to be methylated. This methylation helps distinguish the newly synthesized DNA strand from the parent strand, which aids in the final stages of DNA proofreading after duplication. However, over evolutionary time, methylated Cs tend to turn into Ts because of spontaneous deamination. The result is that CpGs are relatively rare unless there is selective pressure to keep them or a region is not methylated for some other reason, perhaps having to do with the regulation of gene expression. CpG islands are regions where CpGs are present at significantly higher levels than is typical for the genome as a whole. The unmasked version of the track displays potential CpG islands that exist in repeat regions and would otherwise not be visible in the repeat masked version. By default, only the masked version of the track is displayed. To view the unmasked version, change the visibility settings in the track controls at the top of this page. Methods CpG islands were predicted by searching the sequence one base at a time, scoring each dinucleotide (+17 for CG and -1 for others) and identifying maximally scoring segments. Each segment was then evaluated for the following criteria: GC content of 50% or greater length greater than 200 bp ratio greater than 0.6 of observed number of CG dinucleotides to the expected number on the basis of the number of Gs and Cs in the segment The entire genome sequence, masking areas included, was used for the construction of the track Unmasked CpG. The track CpG Islands is constructed on the sequence after all masked sequence is removed. The CpG count is the number of CG dinucleotides in the island. The Percentage CpG is the ratio of CpG nucleotide bases (twice the CpG count) to the length. The ratio of observed to expected CpG is calculated according to the formula (cited in Gardiner-Garden et al. (1987)): Obs/Exp CpG = Number of CpG * N / (Number of C * Number of G) where N = length of sequence. The calculation of the track data is performed by the following command sequence: twoBitToFa assembly.2bit stdout | maskOutFa stdin hard stdout \ | cpg_lh /dev/stdin 2&gt; cpg_lh.err \ | awk '{&dollar;2 = &dollar;2 - 1; width = &dollar;3 - &dollar;2; printf("%s\t%d\t%s\t%s %s\t%s\t%s\t%0.0f\t%0.1f\t%s\t%s\n", &dollar;1, &dollar;2, &dollar;3, &dollar;5, &dollar;6, width, &dollar;6, width*&dollar;7*0.01, 100.0*2*&dollar;6/width, &dollar;7, &dollar;9);}' \ | sort -k1,1 -k2,2n &gt; cpgIsland.bed The unmasked track data is constructed from twoBitToFa -noMask output for the twoBitToFa command. Data access CpG islands and its associated tables can be explored interactively using the REST API, the Table Browser or the Data Integrator. All the tables can also be queried directly from our public MySQL servers, with more information available on our help page as well as on our blog. The source for the cpg_lh program can be obtained from src/utils/cpgIslandExt/. The cpg_lh program binary can be obtained from: http://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/cpg_lh (choose "save file") Credits This track was generated using a modification of a program developed by G. Miklem and L. Hillier (unpublished). References Gardiner-Garden M, Frommer M. CpG islands in vertebrate genomes. J Mol Biol. 1987 Jul 20;196(2):261-82. PMID: 3656447 
cons124way Cons 124 Insects Multiz Alignment & Conservation (124 insects) Comparative Genomics Description This track shows multiple alignments of 124 insects and measurements of evolutionary conservation using two methods (phastCons and phyloP) from the PHAST package, for all 124 species. The multiple alignments were generated using multiz and other tools in the UCSC/Penn State Bioinformatics comparative genomics alignment pipeline. Conserved elements identified by phastCons are also displayed in this track. The phylogenetic tree was derived from kmers in common counting between the sequences to obtain a 'distance' matrix, then using the phylip command 'neighbors' operation for the simple neighbor joining algorithm to establish this binary tree. This tree is not necessarily biologically correct, but it does serve as a useful guide tree for the multiz alignment procedure. See also: Phylip distance operations PhastCons (which has been used in previous Conservation tracks) is a hidden Markov model-based method that estimates the probability that each nucleotide belongs to a conserved element, based on the multiple alignment. It considers not just each individual alignment column, but also its flanking columns. By contrast, phyloP separately measures conservation at individual columns, ignoring the effects of their neighbors. As a consequence, the phyloP plots have a less smooth appearance than the phastCons plots, with more &quot;texture&quot; at individual sites. The two methods have different strengths and weaknesses. PhastCons is sensitive to &quot;runs&quot; of conserved sites, and is therefore effective for picking out conserved elements. PhyloP, on the other hand, is more appropriate for evaluating signatures of selection at particular nucleotides or classes of nucleotides (e.g., third codon positions, or first positions of miRNA target sites). Another important difference is that phyloP can measure acceleration (faster evolution than expected under neutral drift) as well as conservation (slower than expected evolution). In the phyloP plots, sites predicted to be conserved are assigned positive scores (and shown in blue), while sites predicted to be fast-evolving are assigned negative scores (and shown in red). The absolute values of the scores represent -log p-values under a null hypothesis of neutral evolution. The phastCons scores, by contrast, represent probabilities of negative selection and range between 0 and 1. Both phastCons and phyloP treat alignment gaps and unaligned nucleotides as missing data. See also: lastz parameters and other details, and chain minimum score and gap parameters used in these alignments. Missing sequence in the assemblies is highlighted in the track display by regions of yellow when zoomed out and Ns displayed at base level (see Gap Annotation, below). OrganismSpeciesAssembly namebrowser orNCBI sourcealignment type D. melanogasterDrosophila melanogaster Aug. 2014 (BDGP Release 6 + ISO1 MT/dm6) Aug. 2014 (BDGP Release 6 + ISO1 MT/dm6) reference A. albimanusAnopheles albimanus Aug. 2017 (Anop_albi_ALBI9_A_V2) GCA_000349125.2 net A. aquasalisAnopheles aquasalis Dec. 2017 (A_aquasalis_v1.0) GCA_002846955.1 net A. arabiensisAnopheles arabiensis Apr. 2013 (Anop_arab_DONG5_A_V1) GCA_000349185.1 net A. atroparvusAnopheles atroparvus Sep. 2013 (Anop_atro_EBRO_V1) GCA_000473505.1 net A. christyiAnopheles christyi Apr. 2013 (Anop_chri_ACHKN1017_V1) GCA_000349165.1 net A. coluzziiAnopheles coluzzii Apr. 2008 (m5) GCA_000150765.1 net A. cracensAnopheles cracens Apr. 2017 (ASM209184v1) GCA_002091845.1 net A. culicifaciesAnopheles culicifacies Sep. 2013 (Anop_culi_species_A-37_1_V1) GCA_000473375.1 net A. darlingiAnopheles darlingi Dec. 2013 (A_darlingi_v1) GCA_000211455.3 net A. dirusAnopheles dirus Mar. 2013 (Anop_diru_WRAIR2_V1) GCA_000349145.1 net A. epiroticusAnopheles epiroticus Mar. 2013 (Anop_epir_epiroticus2_V1) GCA_000349105.1 net A. farautiAnopheles farauti Jan. 2014 (Anop_fara_FAR1_V2) GCA_000473445.2 net A. farauti_No4Anopheles farauti No. 4 Mar. 2015 (ASM95621v1) GCA_000956215.1 net A. funestusAnopheles funestus Mar. 2013 (Anop_fune_FUMOZ_V1) GCA_000349085.1 net A. gambiaeAnopheles gambiae Oct. 2006 (AgamP3/anoGam3) Oct. 2006 (AgamP3/anoGam3) net A. gambiae_1Anopheles gambiae str. PEST Oct. 2006 (AgamP3) GCF_000005575.2 net A. koliensisAnopheles koliensis Mar. 2015 (ASM95627v1) GCA_000956275.1 net A. maculatusAnopheles maculatus Apr. 2017 (ASM209183v1) GCA_002091835.1 net A. melasAnopheles melas Jan. 2014 (Anop_mela_CM1001059_A_V2) GCA_000473525.2 net A. melliferaApis mellifera 04 Nov 2010 (Amel_4.5/apiMel4) 04 Nov 2010 (Amel_4.5/apiMel4) net A. merusAnopheles merus Jan. 2014 (Anop_meru_MAF_V1) GCA_000473845.2 net A. minimusAnopheles minimus Mar. 2013 (Anop_mini_MINIMUS1_V1) GCA_000349025.1 net A. niliAnopheles nili Jul. 2013 (Anili1) GCA_000439205.1 net A. punctulatusAnopheles punctulatus Mar. 2015 (ASM95625v1) GCA_000956255.1 net A. quadriannulatusAnopheles quadriannulatus Mar. 2013 (Anop_quad_QUAD4_A_V1) GCA_000349065.1 net A. sinensisAnopheles sinensis Jul. 2014 (AS2) GCA_000441895.2 net A. stephensiAnopheles stephensi Sep. 2018 (ASM344897v1) GCA_003448975.1 net Aedes_aegyptiAedes aegypti Jun. 2017 (AaegL5.0) GCF_002204515.2 net Aedes_albopictusAedes albopictus Jan. 2017 (canu_80X_arrow2.2) GCF_001876365.2 net Bactrocera_dorsalisBactrocera dorsalis Dec. 2014 (ASM78921v2) GCF_000789215.1 net Bactrocera_latifronsBactrocera latifrons Oct. 2016 (ASM185335v1) GCF_001853355.1 net Bactrocera_oleaeBactrocera oleae Jul. 2015 (gapfilled_joined_lt9474.gt500.covgt10) GCF_001188975.1 net Bactrocera_tryoniBactrocera tryoni May 2014 (Assembly_2.2_of_Bactrocera_tryoni_genome) GCA_000695345.1 net Belgica_antarcticaBelgica antarctica Sep. 2014 (ASM77530v1) GCA_000775305.1 net Calliphora_vicinaCalliphora vicina Jun. 2015 (ASM101727v1) GCA_001017275.1 net Ceratitis_capitataCeratitis capitata Nov. 2017 (Ccap_2.1) GCF_000347755.3 net Chaoborus_trivitattusChaoborus trivitattus May 2015 (ASM101481v1) GCA_001014815.1 net Chironomus_ripariusChironomus riparius May 2015 (ASM101450v1) GCA_001014505.1 net Chironomus_tentansChironomus tentans Nov. 2014 (CT01) GCA_000786525.1 net Cirrula_hiansCirrula hians May 2015 (ASM101507v1) GCA_001015075.1 net Clogmia_albipunctataClogmia albipunctata May 2015 (ASM101494v1) GCA_001014945.1 net Clunio_marinusClunio marinus Nov. 2016 (CLUMA_1.0) GCA_900005825.1 net Coboldia_fuscipesCoboldia fuscipes May 2015 (ASM101433v1) GCA_001014335.1 net Condylostylus_patibulatusCondylostylus patibulatus May 2015 (ASM101487v1) GCA_001014875.1 net Culex_quinquefasciatusCulex quinquefasciatus Apr. 2007 (CulPip1.0) GCF_000209185.1 net Culicoides_sonorensisCulicoides sonorensis Feb. 2018 (Cson_Genome_version_2.0) GCA_900258525.2 net D. albomicansDrosophila albomicans 21 May 2012 (DroAlb_1.0/droAlb1) 21 May 2012 (DroAlb_1.0/droAlb1) net D. americanaDrosophila americana Oct. 2015 (D._americana_H5_strain_genome_assembly) GCA_001245395.1 net D. ananassaeDrosophila ananassae Feb. 2006 (Agencourt CAF1/droAna3) Feb. 2006 (Agencourt CAF1/droAna3) syntenic D. arizonaeDrosophila arizonae May 2016 (ASM165402v1) GCF_001654025.1 syntenic D. athabascaDrosophila athabasca Jun. 2018 (ASM318502v1) GCA_003185025.1 syntenic D. biarmipesDrosophila biarmipes 04 Mar 2013 (Dbia_2.0/droBia2) 04 Mar 2013 (Dbia_2.0/droBia2) syntenic D. bipectinataDrosophila bipectinata 04 Mar 2013 (Dbip_2.0/droBip2) 04 Mar 2013 (Dbip_2.0/droBip2) net D. busckiiDrosophila busckii Sep. 2015 (ASM127793v1) GCF_001277935.1 syntenic D. elegansDrosophila elegans 04 Mar 2013 (Dele_2.0/droEle2) 04 Mar 2013 (Dele_2.0/droEle2) net D. erectaDrosophila erecta Feb. 2006 (Agencourt CAF1/droEre2) Feb. 2006 (Agencourt CAF1/droEre2) syntenic D. eugracilisDrosophila eugracilis 04 Mar 2013 (Deug_2.0/droEug2) 04 Mar 2013 (Deug_2.0/droEug2) net D. ficusphilaDrosophila ficusphila 04 Mar 2013 (Dfic_2.0/droFic2) 04 Mar 2013 (Dfic_2.0/droFic2) net D. grimshawiDrosophila grimshawi Feb. 2006 (Agencourt CAF1/droGri2) Feb. 2006 (Agencourt CAF1/droGri2) syntenic D. hydeiDrosophila hydei Nov. 2017 (ASM278046v1) GCF_002780465.1 net D. kikkawaiDrosophila kikkawai 04 Mar 2013 (Dkik_2.0/droKik2) 04 Mar 2013 (Dkik_2.0/droKik2) net D. mirandaDrosophila miranda 19 Apr 2013 (DroMir_2.2/droMir2) 19 Apr 2013 (DroMir_2.2/droMir2) syntenic D. mojavensisDrosophila mojavensis Feb. 2006 (Agencourt CAF1/droMoj3) Feb. 2006 (Agencourt CAF1/droMoj3) syntenic D. montanaDrosophila montana May 2018 (ASM308661v1) GCA_003086615.1 net D. nasutaDrosophila nasuta Jul. 2017 (ASM222288v1) GCA_002222885.1 net D. navojoaDrosophila navojoa May 2016 (ASM165401v1) GCF_001654015.1 syntenic D. novamexicanaDrosophila novamexicana Jul. 2018 (DnovRS1) GCA_003285875.1 syntenic D. obscuraDrosophila obscura Jul. 2017 (Dobs_1.0) GCF_002217835.1 net D. persimilisDrosophila persimilis Oct. 2005 (Broad/droPer1) Oct. 2005 (Broad/droPer1) net D. pseudoobscuraDrosophila pseudoobscura pseudoobscura 11 Apr 2013 (Dpse_3.0/droPse3) 11 Apr 2013 (Dpse_3.0/droPse3) syntenic D. pseudoobscura_1Drosophila pseudoobscura pseudoobscura Apr. 2013 (Dpse_3.0) GCF_000001765.3 net D. rhopaloaDrosophila rhopaloa 22 Feb 2013 (Drho_2.0/droRho2) 22 Feb 2013 (Drho_2.0/droRho2) net D. sechelliaDrosophila sechellia Oct. 2005 (Broad/droSec1) Oct. 2005 (Broad/droSec1) syntenic D. serrataDrosophila serrata Apr. 2017 (Dser1.0) GCF_002093755.1 net D. simulansDrosophila simulans Sep. 2014 (ASM75419v2/droSim2) Sep. 2014 (ASM75419v2/droSim2) syntenic D. subobscuraDrosophila subobscura Nov. 2017 (Dsub_1.0) GCA_002749795.1 net D. suzukiiDrosophila suzukii 30 Sep 2013 (Dsuzukii.v01/droSuz1) 30 Sep 2013 (Dsuzukii.v01/droSuz1) net D. takahashiiDrosophila takahashii 04 Mar 2013 (Dtak_2.0/droTak2) 04 Mar 2013 (Dtak_2.0/droTak2) net D. virilisDrosophila virilis Feb. 2006 (Agencourt CAF1/droVir3) Feb. 2006 (Agencourt CAF1/droVir3) syntenic D. willistoniDrosophila willistoni 03 Aug 2006 (dwil_caf1/droWil2) 03 Aug 2006 (dwil_caf1/droWil2) syntenic D. yakubaDrosophila yakuba 27 Jun 2006 (dyak_caf1/droYak3) 27 Jun 2006 (dyak_caf1/droYak3) syntenic Ephydra_gracilisEphydra gracilis May 2015 (ASM101467v1) GCA_001014675.1 net Eristalis_dimidiataEristalis dimidiata May 2015 (ASM101514v1) GCA_001015145.1 net Eutreta_dianaEutreta diana May 2015 (ASM101511v1) GCA_001015115.1 net Glossina_austeniGlossina austeni May 2014 (Glossina_austeni-1.0.3) GCA_000688735.1 net Glossina_brevipalpisGlossina brevipalpis May 2014 (Glossina_brevipalpis_1.0.3) GCA_000671755.1 net Glossina_fuscipesGlossina fuscipes fuscipes May 2014 (Glossina_fuscipes-3.0.2) GCA_000671735.1 net Glossina_morsitans_1Glossina morsitans May 2015 (ASM101451v1) GCA_001014515.1 net Glossina_morsitans_2Glossina morsitans morsitans Mar. 2014 (ASM107743v1) GCA_001077435.1 net Glossina_pallidipesGlossina pallidipes May 2014 (Glossina_pallidipes-1.0.3) GCA_000688715.1 net Glossina_palpalis_gambiensisGlossina palpalis gambiensis Jan. 2015 (Glossina_palpalis_gambiensis-2.0.1) GCA_000818775.1 net Haematobia_irritansHaematobia irritans May 2018 (Hi_v1.0) GCA_003123925.1 net Hermetia_illucensHermetia illucens May 2015 (ASM101489v1) GCA_001014895.1 net Holcocephala_fuscaHolcocephala fusca May 2015 (ASM101521v1) GCA_001015215.1 net Liriomyza_trifoliiLiriomyza trifolii May 2015 (ASM101493v1) GCA_001014935.1 net Lucilia_cuprinaLucilia cuprina Dec. 2017 (Lcup_2.0) GCF_000699065.1 net Lucilia_sericataLucilia sericata May 2015 (ASM101483v1) GCA_001014835.1 net Lutzomyia_longipalpisLutzomyia longipalpis Jun. 2012 (Llon_1.0) GCA_000265325.1 net M. domesticaMusca domestica 22 Apr 2013 (Musca_domestica-2.0.2/musDom2) 22 Apr 2013 (Musca_domestica-2.0.2/musDom2) net Mayetiola_destructorMayetiola destructor Oct. 2010 (Mdes_1.0) GCA_000149185.1 net Megaselia_abditaMegaselia abdita May 2015 (ASM101517v1) GCA_001015175.1 net Megaselia_scalarisMegaselia scalaris Mar. 2013 (ASM34191v2) GCA_000341915.2 net Mochlonyx_cinctipesMochlonyx cinctipes May 2015 (ASM101484v1) GCA_001014845.1 net Neobellieria_bullataNeobellieria bullata Jun. 2015 (ASM101745v1) GCA_001017455.1 net Paykullia_maculataPaykullia maculata Apr. 2018 (ASM305512v1) GCA_003055125.1 net Phlebotomus_papatasiPhlebotomus papatasi May 2012 (Ppap_1.0) GCA_000262795.1 net Phormia_reginaPhormia regina Sep. 2016 (ASM173554v1) GCA_001735545.1 net Phortica_variegataPhortica variegata May 2015 (ASM101441v1) GCA_001014415.1 net Proctacanthus_coquillettiProctacanthus coquilletti Jan. 2017 (200kmer_750.trimmed) GCA_001932985.1 net Rhagoletis_zephyriaRhagoletis zephyria Jul. 2016 (Rhagoletis_zephyria_1.0) GCF_001687245.1 net Sarcophagidae_BV_2014Sarcophagidae sp. BV-2014 Jul. 2015 (ASM104719v1) GCA_001047195.1 net Scaptodrosophila_lebanonensisScaptodrosophila lebanonensis Jul. 2018 (SlebRS1) GCA_003285725.1 net Sphyracephala_brevicornisSphyracephala brevicornis May 2015 (ASM101523v1) GCA_001015235.1 net Stomoxys_calcitransStomoxys calcitrans May 2015 (Stomoxys_calcitrans-1.0.1) GCF_001015335.1 net T. castaneumTribolium castaneum Sep. 2005 (Baylor 2.0/triCas2) Sep. 2005 (Baylor 2.0/triCas2) net Teleopsis_dalmanniTeleopsis dalmanni Jul. 2017 (Tel_dalmanni_2A_v1.0) GCA_002237135.1 net Tephritis_californicaTephritis californica Jun. 2015 (ASM101751v1) GCA_001017515.1 net Themira_minorThemira minor May 2015 (ASM101457v1) GCA_001014575.1 net Tipula_oleraceaTipula oleracea Jun. 2015 (ASM101753v1) GCA_001017535.1 net Trichoceridae_BV_2014Trichoceridae sp. BV-2014 May 2015 (ASM101442v1) GCA_001014425.1 net Trupanea_jonesiTrupanea jonesi May 2015 (ASM101466v1) GCA_001014665.1 net Zaprionus_indianusZaprionus indianus Oct. 2016 (ZP_IN_1.0) GCA_001752445.1 net Zeugodacus_cucurbitaeZeugodacus cucurbitae Dec. 2014 (ASM80634v1) GCF_000806345.1 net Downloads for data in this track are available: Multiz alignments (MAF format), and phylogenetic trees PhyloP conservation (WIG format) PhastCons conservation (WIG format) Display Conventions and Configuration The track configuration options allow the user to display the three different clade sets of scores, all, Brachycera, Nematocera or Holometabola, individually or all simultaneously. In full and pack display modes, conservation scores are displayed as a wiggle track (histogram) in which the height reflects the value of the score. The conservation wiggles can be configured in a variety of ways to highlight different aspects of the displayed information. Click the Graph configuration help link for an explanation of the configuration options. Pairwise alignments of each species to the D. melanogaster genome are displayed below the conservation histogram as a grayscale density plot (in pack mode) or as a wiggle (in full mode) that indicates alignment quality. In dense display mode, conservation is shown in grayscale using darker values to indicate higher levels of overall conservation as scored by phastCons. Checkboxes on the track configuration page allow selection of the species to include in the pairwise display. Configuration buttons are available to select all of the species (Set all), deselect all of the species (Clear all), or use the default settings (Set defaults). Note that excluding species from the pairwise display does not alter the the conservation score display. To view detailed information about the alignments at a specific position, zoom the display in to 30,000 or fewer bases, then click on the alignment. Gap Annotation The Display chains between alignments configuration option enables display of gaps between alignment blocks in the pairwise alignments in a manner similar to the Chain track display. The following conventions are used: Single line: No bases in the aligned species. Possibly due to a lineage-specific insertion between the aligned blocks in the D. melanogaster genome or a lineage-specific deletion between the aligned blocks in the aligning species. Double line: Aligning species has one or more unalignable bases in the gap region. Possibly due to excessive evolutionary distance between species or independent indels in the region between the aligned blocks in both species. Pale yellow coloring: Aligning species has Ns in the gap region. Reflects uncertainty in the relationship between the DNA of both species, due to lack of sequence in relevant portions of the aligning species. Genomic Breaks Discontinuities in the genomic context (chromosome, scaffold or region) of the aligned DNA in the aligning species are shown as follows: Vertical blue bar: Represents a discontinuity that persists indefinitely on either side, e.g. a large region of DNA on either side of the bar comes from a different chromosome in the aligned species due to a large scale rearrangement. Green square brackets: Enclose shorter alignments consisting of DNA from one genomic context in the aligned species nested inside a larger chain of alignments from a different genomic context. The alignment within the brackets may represent a short misalignment, a lineage-specific insertion of a transposon in the D. melanogaster genome that aligns to a paralogous copy somewhere else in the aligned species, or other similar occurrence. Base Level When zoomed-in to the base-level display, the track shows the base composition of each alignment. The numbers and symbols on the Gaps line indicate the lengths of gaps in the D. melanogaster sequence at those alignment positions relative to the longest non-D. melanogaster sequence. If there is sufficient space in the display, the size of the gap is shown. If the space is insufficient and the gap size is a multiple of 3, a &quot;*&quot; is displayed; other gap sizes are indicated by &quot;+&quot;. Codon translation is available in base-level display mode if the displayed region is identified as a coding segment. To display this annotation, select the species for translation from the pull-down menu in the Codon Translation configuration section at the top of the page. Then, select one of the following modes: No codon translation: The gene annotation is not used; the bases are displayed without translation. Use default species reading frames for translation: The annotations from the genome displayed in the Default species to establish reading frame pull-down menu are used to translate all the aligned species present in the alignment. Use reading frames for species if available, otherwise no translation: Codon translation is performed only for those species where the region is annotated as protein coding. Use reading frames for species if available, otherwise use default species: Codon translation is done on those species that are annotated as being protein coding over the aligned region using species-specific annotation; the remaining species are translated using the default species annotation. Codon translation uses the following gene tracks as the basis for translation, depending on the species chosen (Table 2). Gene TrackSpecies NCBI RefSeq GenesD. persimilis Ensembl Genes v68D. erecta, D. ananassae, D. melanogaster Xeno RefGeneD. sechellia no annotationsall others Table 2. Gene tracks used for codon translation. Methods Pairwise alignments with the D. melanogaster genome were generated for each species using lastz from repeat-masked genomic sequence. Pairwise alignments were then linked into chains using a dynamic programming algorithm that finds maximally scoring chains of gapless subsections of the alignments organized in a kd-tree. Please note the specific parameters for the alignments. High-scoring chains were then placed along the genome, with gaps filled by lower-scoring chains, to produce an alignment net. For more information about the chaining and netting process for each species, see the description pages for the Chain and Net tracks. An additional filtering step was introduced in the generation of the 124-way conservation track to reduce the number of paralogs and pseudogenes from the high-quality assemblies and the suspect alignments from the low-quality assemblies: some of the pairwise alignments were filtered based on synteny; and some were filtered to retain only alignments of best quality in both the target and query ("reciprocal best"). See also: D. melanogaster/dm6 124-way alignment filtering parameters. The column alignment type indicates the type of filtering. The resulting best-in-genome pairwise alignments were progressively aligned using multiz/autoMZ, following the tree topology diagrammed above, to produce multiple alignments. The multiple alignments were post-processed to add annotations indicating alignment gaps, genomic breaks, and base quality of the component sequences. The annotated multiple alignments, in MAF format, are available for bulk download. An alignment summary table containing an entry for each alignment block in each species was generated to improve track display performance at large scales. Framing tables were constructed to enable visualization of codons in the multiple alignment display. Phylogenetic Tree Model Both phastCons and phyloP are phylogenetic methods that rely on a tree model containing the tree topology, branch lengths representing evolutionary distance at neutrally evolving sites, the background distribution of nucleotides, and a substitution rate matrix. The all species tree model for this track was generated using the phyloFit program from the PHAST package (REV model, EM algorithm, medium precision) using multiple alignments of 4-fold degenerate sites extracted from the 124-way alignment (msa_view). The 4d sites were derived from the NCBI RefSeq gene set, filtered to select single-coverage long transcripts. This same tree model was used in the phyloP calculations, however their background frequencies were modified to maintain reversibility. The resulting tree model for all species. PhastCons Conservation The phastCons program computes conservation scores based on a phylo-HMM, a type of probabilistic model that describes both the process of DNA substitution at each site in a genome and the way this process changes from one site to the next (Felsenstein and Churchill 1996, Yang 1995, Siepel and Haussler 2005). PhastCons uses a two-state phylo-HMM, with a state for conserved regions and a state for non-conserved regions. The value plotted at each site is the posterior probability that the corresponding alignment column was "generated" by the conserved state of the phylo-HMM. These scores reflect the phylogeny (including branch lengths) of the species in question, a continuous-time Markov model of the nucleotide substitution process, and a tendency for conservation levels to be autocorrelated along the genome (i.e., to be similar at adjacent sites). The general reversible (REV) substitution model was used. Unlike many conservation-scoring programs, phastCons does not rely on a sliding window of fixed size; therefore, short highly-conserved regions and long moderately conserved regions can both obtain high scores. More information about phastCons can be found in Siepel et al. 2005. The phastCons parameters used were: expected-length=45, target-coverage=0.3, rho=0.3. PhyloP Conservation The phyloP program supports several different methods for computing p-values of conservation or acceleration, for individual nucleotides or larger elements ( http://compgen.cshl.edu/phast/). Here it was used to produce separate scores at each base (--wig-scores option), considering all branches of the phylogeny rather than a particular subtree or lineage (i.e., the --subtree option was not used). The scores were computed by performing a likelihood ratio test at each alignment column (--method LRT), and scores for both conservation and acceleration were produced (--mode CONACC). Conserved Elements The conserved elements were predicted by running phastCons with the --viterbi option. The predicted elements are segments of the alignment that are likely to have been "generated" by the conserved state of the phylo-HMM. Each element is assigned a log-odds score equal to its log probability under the conserved model minus its log probability under the non-conserved model. The "score" field associated with this track contains transformed log-odds scores, taking values between 0 and 1000. (The scores are transformed using a monotonic function of the form a * log(x) + b.) The raw log odds scores are retained in the "name" field and can be seen on the details page or in the browser when the track's display mode is set to "pack" or "full". Credits This track was created using the following programs: Alignment tools: lastz (formerly blastz) and multiz by Minmei Hou, Scott Schwartz and Webb Miller of the Penn State Bioinformatics Group Chaining and Netting: axtChain, chainNet by Jim Kent at UCSC Conservation scoring: phastCons, phyloP, phyloFit, tree_doctor, msa_view and other programs in PHAST by Adam Siepel at Cold Spring Harbor Laboratory (original development done at the Haussler lab at UCSC). MAF Annotation tools: mafAddIRows by Brian Raney, UCSC; mafAddQRows by Richard Burhans, Penn State; genePredToMafFrames by Mark Diekhans, UCSC Tree image generator: phyloPng by Galt Barber, UCSC Conservation track display: Kate Rosenbloom, Hiram Clawson (wiggle display), and Brian Raney (gap annotation and codon framing) at UCSC The phylogenetic tree is based on Murphy et al. (2001) and general consensus in the vertebrate phylogeny community as of March 2007. References Phylip distance operations: Fan H, Ives A, Surget_groba Y, Cannon C. An assembly and alignment-free method of phylogeny reconstruction from next-generation sequencing data. BMC Genomics. 2015; 16(1): 522. PMID: 26169061 Bernard G, Ragan M, Chana C.X. Recapitulating phylogenies using k-mers: from trees to networks. F1000Res. 2016; 5: 2789. PMID: 28105314 Phylo-HMMs, phastCons, and phyloP: Felsenstein J, Churchill GA. A Hidden Markov Model approach to variation among sites in rate of evolution. Mol Biol Evol. 1996 Jan;13(1):93-104. PMID: 8583911 Pollard KS, Hubisz MJ, Rosenbloom KR, Siepel A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 2010 Jan;20(1):110-21. PMID: 19858363; PMC: PMC2798823 Siepel A, Bejerano G, Pedersen JS, Hinrichs AS, Hou M, Rosenbloom K, Clawson H, Spieth J, Hillier LW, Richards S, et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 2005 Aug;15(8):1034-50. PMID: 16024819; PMC: PMC1182216 Siepel A, Haussler D. Phylogenetic Hidden Markov Models. In: Nielsen R, editor. Statistical Methods in Molecular Evolution. New York: Springer; 2005. pp. 325-351. Yang Z. A space-time process model for the evolution of DNA sequences. Genetics. 1995 Feb;139(2):993-1005. PMID: 7713447; PMC: PMC1206396 Chain/Net: Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D. Evolution's cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11484-9. PMID: 14500911; PMC: PMC208784 Multiz: Blanchette M, Kent WJ, Riemer C, Elnitski L, Smit AF, Roskin KM, Baertsch R, Rosenbloom K, Clawson H, Green ED, et al. Aligning multiple genomic sequences with the threaded blockset aligner. Genome Res. 2004 Apr;14(4):708-15. PMID: 15060014; PMC: PMC383317 Lastz (formerly Blastz): Chiaromonte F, Yap VB, Miller W. Scoring pairwise genomic sequence alignments. Pac Symp Biocomput. 2002:115-26. PMID: 11928468 Harris RS. Improved pairwise alignment of genomic DNA. Ph.D. Thesis. Pennsylvania State University, USA. 2007. Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D, Miller W. Human-mouse alignments with BLASTZ. Genome Res. 2003 Jan;13(1):103-7. PMID: 12529312; PMC: PMC430961 Phylogenetic Tree: Murphy WJ, Eizirik E, O'Brien SJ, Madsen O, Scally M, Douady CJ, Teeling E, Ryder OA, Stanhope MJ, de Jong WW, Springer MS. Resolution of the early placental mammal radiation using Bayesian phylogenetics. Science. 2001 Dec 14;294(5550):2348-51. PMID: 11743200 
cons124wayViewalign Multiz Alignments Multiz Alignment & Conservation (124 insects) Comparative Genomics 
multiz124way Multiz Align Multiz Alignments of 124 insects Comparative Genomics 
cons124wayViewphastcons Element Conservation (phastCons) Multiz Alignment & Conservation (124 insects) Comparative Genomics 
phastCons124way Cons 124 insects 124 insects conservation by PhastCons Comparative Genomics 
cons124wayViewelements Conserved Elements Multiz Alignment & Conservation (124 insects) Comparative Genomics 
phastConsElements124way 124 insects El 124 insects Conserved Elements Comparative Genomics 
cons124wayViewphyloP Basewise Conservation (phyloP) Multiz Alignment & Conservation (124 insects) Comparative Genomics 
phyloP124way Cons 124 insects 124 insects Basewise Conservation by PhyloP Comparative Genomics 
refSeqComposite NCBI RefSeq RefSeq genes from NCBI Genes and Gene Predictions Description The NCBI RefSeq Genes composite track shows D. melanogaster protein-coding and non-protein-coding genes taken from the NCBI RNA reference sequences collection (RefSeq). All subtracks use coordinates provided by RefSeq, except for the UCSC RefSeq track, which UCSC produces by realigning the RefSeq RNAs to the genome. This realignment may result in occasional differences between the annotation coordinates provided by UCSC and NCBI. For RNA-seq analysis, we advise using NCBI aligned tables like RefSeq All or RefSeq Curated. See the Methods section for more details about how the different tracks were created. Please visit NCBI's Feedback for Gene and Reference Sequences (RefSeq) page to make suggestions, submit additions and corrections, or ask for help concerning RefSeq records. For more information on the different gene tracks, see our Genes FAQ. Display Conventions and Configuration This track is a composite track that contains differing data sets. To show only a selected set of subtracks, uncheck the boxes next to the tracks that you wish to hide. Note: Not all subtracts are available on all assemblies. The possible subtracks include: RefSeq aligned annotations and UCSC alignment of RefSeq annotations RefSeq All &ndash; all curated and predicted annotations provided by RefSeq. RefSeq Curated &ndash; subset of RefSeq All that includes only those annotations whose accessions begin with NM, NR, NP or YP. (NP and YP are used only for protein-coding genes on the mitochondrion; YP is used for human only.) They were manually curated, based on publications describing transcripts and manual reviews of evidence which includes EST and full-length cDNA alignments, protein sequences, splice sites and any other evidence available in databases or the scientific literature. The resulting sequences can differ from the genome, they exist independently from a particular human genome build, and so must be aligned to the genome to create a track. The "RefSeq Curated" track is NCBI's mapping of these transcripts to the genome. Another alignment track exists for these, the "UCSC RefSeq" track (see beloow). RefSeq Predicted &ndash; subset of RefSeq All that includes those annotations whose accessions begin with XM or XR. They were predicted based on protein, cDNA, EST and RNA-seq alignments to the genome assembly by the NCBI Gnomon prediction software. RefSeq Other &ndash; all other annotations produced by the RefSeq group that do not fit the requirements for inclusion in the RefSeq Curated or the RefSeq Predicted tracks. Examples are untranscribed pseudogenes or gene clusters, such as HOX or protocadherin alpha. They were manually curated from publications or databases but are not typical transcribed genes. RefSeq Alignments &ndash; alignments of RefSeq RNAs to the D. melanogaster genome provided by the RefSeq group, following the display conventions for PSL tracks. RefSeq Diffs &ndash; alignment differences between the D. melanogaster reference genome(s) and RefSeq curated transcripts. (Track not currently available for every assembly.) UCSC RefSeq &ndash; annotations generated from UCSC's realignment of RNAs with NM and NR accessions to the D. melanogaster genome. This track was previously known as the &quot;RefSeq Genes&quot; track. RefSeq Select (subset, only on hg38) &ndash; Subset of RefSeq Curated, transcripts marked as part of the RefSeq Select dataset. A single Select transcript is chosen as representative for each protein-coding gene. See NCBI RefSeq Select. RefSeq HGMD (subset) &ndash; Subset of RefSeq Curated, transcripts annotated by the Human Gene Mutation Database. This track is only available on the human genomes hg19 and hg38. It is the most restricted RefSeq subset, targeting clinical diagnostics. The RefSeq All, RefSeq Curated, RefSeq Predicted, and UCSC RefSeq tracks follow the display conventions for gene prediction tracks. The color shading indicates the level of review the RefSeq record has undergone: predicted (light), provisional (medium), or reviewed (dark), as defined by RefSeq. Color Level of review Reviewed: the RefSeq record has been reviewed by NCBI staff or by a collaborator. The NCBI review process includes assessing available sequence data and the literature. Some RefSeq records may incorporate expanded sequence and annotation information. Provisional: the RefSeq record has not yet been subject to individual review. The initial sequence-to-gene association has been established by outside collaborators or NCBI staff. Predicted: the RefSeq record has not yet been subject to individual review, and some aspect of the RefSeq record is predicted. The item labels and codon display properties for features within this track can be configured through the check-box controls at the top of the track description page. To adjust the settings for an individual subtrack, click the wrench icon next to the track name in the subtrack list . Label: By default, items are labeled by gene name. Click the appropriate Label option to display the accession name or OMIM identifier instead of the gene name, show all or a subset of these labels including the gene name, OMIM identifier and accession names, or turn off the label completely. Codon coloring: This track has an optional codon coloring feature that allows users to quickly validate and compare gene predictions. To display codon colors, select the genomic codons option from the Color track by codons pull-down menu. For more information about this feature, go to the Coloring Gene Predictions and Annotations by Codon page. The RefSeq Diffs track contains five different types of inconsistency between the reference genome sequence and the RefSeq transcript sequences. The five types of differences are as follows: mismatch &ndash; aligned but mismatching bases, plus HGVS g. to show the genomic change required to match the transcript and HGVS c./n. to show the transcript change required to match the genome. short gap &ndash; genomic gaps that are too small to be introns (arbitrary cutoff of &lt; 45 bp), most likely insertions/deletion variants or errors, with HGVS g. and c./n. showing differences. shift gap &ndash; shortGap items whose placement could be shifted left and/or right on the genome due to repetitive sequence, with HGVS c./n. position range of ambiguous region in transcript. Here, thin and thick lines are used -- the thin line shows the span of the repetitive sequence, and the thick line shows the rightmost shifted gap. double gap &ndash; genomic gaps that are long enough to be introns but that skip over transcript sequence (invisible in default setting), with HGVS c./n. deletion. skipped &ndash; sequence at the beginning or end of a transcript that is not aligned to the genome (invisible in default setting), with HGVS c./n. deletion HGVS Terminology (Human Genome Variation Society): g. = genomic sequence ; c. = coding DNA sequence ; n. = non-coding RNA reference sequence. When reporting HGVS with RefSeq sequences, to make sure that results from research articles can be mapped to the genome unambiguously, please specify the RefSeq annotation release displayed on the transcript's Genome Browser details page and also the RefSeq transcript ID with version (e.g. NM_012309.4 not NM_012309). Methods Tracks contained in the RefSeq annotation and RefSeq RNA alignment tracks were created at UCSC using data from the NCBI RefSeq project. Data files were downloaded from RefSeq in GFF file format and converted to the genePred and PSL table formats for display in the Genome Browser. Information about the NCBI annotation pipeline can be found here. The RefSeq Diffs track is generated by UCSC using NCBI's RefSeq RNA alignments. The UCSC RefSeq Genes track is constructed using the same methods as previous RefSeq Genes tracks. RefSeq RNAs were aligned against the D. melanogaster genome using BLAT. Those with an alignment of less than 15% were discarded. When a single RNA aligned in multiple places, the alignment having the highest base identity was identified. Only alignments having a base identity level within 0.1% of the best and at least 96% base identity with the genomic sequence were kept. Data Access The raw data for these tracks can be accessed in multiple ways. It can be explored interactively using the REST API, Table Browser or Data Integrator. The tables can also be accessed programmatically through our public MySQL server or downloaded from our downloads server for local processing. The previous track versions are available in the archives of our downloads server. You can also access any RefSeq table entries in JSON format through our JSON API. The data in the RefSeq Other and RefSeq Diffs tracks are organized in bigBed file format; more information about accessing the information in this bigBed file can be found below. The other subtracks are associated with database tables as follows: genePred format: RefSeq All - ncbiRefSeq RefSeq Curated - ncbiRefSeqCurated RefSeq Predicted - ncbiRefSeqPredicted UCSC RefSeq - refGene PSL format: RefSeq Alignments - ncbiRefSeqPsl The first column of each of these tables is &quot;bin&quot;. This column is designed to speed up access for display in the Genome Browser, but can be safely ignored in downstream analysis. You can read more about the bin indexing system here. The annotations in the RefSeqOther and RefSeqDiffs tracks are stored in bigBed files, which can be obtained from our downloads server here, ncbiRefSeqOther.bb and ncbiRefSeqDiffs.bb. Individual regions or the whole set of genome-wide annotations can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system from the utilities directory linked below. For example, to extract only annotations in a given region, you could use the following command: bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/dm6/ncbiRefSeq/ncbiRefSeqOther.bb -chrom=chr16 -start=34990190 -end=36727467 stdout You can download a GTF format version of the RefSeq All table from the GTF downloads directory. The genePred format tracks can also be converted to GTF format using the genePredToGtf utility, available from the utilities directory on the UCSC downloads server. The utility can be run from the command line like so: genePredToGtf dm6 ncbiRefSeqPredicted ncbiRefSeqPredicted.gtf Note that using genePredToGtf in this manner accesses our public MySQL server, and you therefore must set up your hg.conf as described on the MySQL page linked near the beginning of the Data Access section. A file containing the RNA sequences in FASTA format for all items in the RefSeq All, RefSeq Curated, and RefSeq Predicted tracks can be found on our downloads server here. Please refer to our mailing list archives for questions. Previous versions of the ncbiRefSeq set of tracks can be found on our archive download server. Credits This track was produced at UCSC from data generated by scientists worldwide and curated by the NCBI RefSeq project. References Kent WJ. BLAT - the BLAST-like alignment tool. Genome Res. 2002 Apr;12(4):656-64. PMID: 11932250; PMC: PMC187518 Pruitt KD, Brown GR, Hiatt SM, Thibaud-Nissen F, Astashyn A, Ermolaeva O, Farrell CM, Hart J, Landrum MJ, McGarvey KM et al. RefSeq: an update on mammalian reference sequences. Nucleic Acids Res. 2014 Jan;42(Database issue):D756-63. PMID: 24259432; PMC: PMC3965018 Pruitt KD, Tatusova T, Maglott DR. NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 2005 Jan 1;33(Database issue):D501-4. PMID: 15608248; PMC: PMC539979 
refGene UCSC RefSeq UCSC annotations of RefSeq RNAs (NM_* and NR_*) Genes and Gene Predictions Description The RefSeq Genes track shows known D. melanogaster protein-coding and non-protein-coding genes taken from the NCBI RNA reference sequences collection (RefSeq), which were directly contributed to NCBI by FlyBase. The data underlying this track are updated weekly. Please visit the Feedback for Gene and Reference Sequences (RefSeq) page to make suggestions, submit additions and corrections, or ask for help concerning RefSeq records. Display Conventions and Configuration This track follows the display conventions for gene prediction tracks. The color shading indicates the level of review the RefSeq record has undergone: predicted (light), provisional (medium), reviewed (dark). The item labels and display colors of features within this track can be configured through the controls at the top of the track description page. This page is accessed via the small button to the left of the track's graphical display or through the link on the track's control menu. Label: By default, items are labeled by gene name. Click the appropriate Label option to display the accession name instead of the gene name, show both the gene and accession names, or turn off the label completely. Codon coloring: This track contains an optional codon coloring feature that allows users to quickly validate and compare gene predictions. To display codon colors, select the genomic codons option from the Color track by codons pull-down menu. Click here for more information about this feature. Hide non-coding genes: By default, both the protein-coding and non-protein-coding genes are displayed. If you wish to see only the coding genes, click this box. Methods RefSeq RNAs were aligned against the D. melanogaster genome using blat; those with an alignment of less than 15% were discarded. When a single RNA aligned in multiple places, the alignment having the highest base identity was identified. Only alignments having a base identity level within 0.1% of the best and at least 96% base identity with the genomic sequence were kept. Credits This track was produced at UCSC from RNA sequence data generated by scientists worldwide and curated by the NCBI RefSeq project. References Kent WJ. BLAT--the BLAST-like alignment tool. Genome Res. 2002 Apr;12(4):656-64. PMID: 11932250; PMC: PMC187518 Pruitt KD, Tatusova T, Maglott DR. NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 2005 Jan 1;33(Database issue):D501-4. PMID: 15608248; PMC: PMC539979 
ncbiRefSeqGenomicDiff RefSeq Diffs Differences between NCBI RefSeq Transcripts and the Reference Genome Genes and Gene Predictions 
ncbiRefSeqPsl RefSeq Alignments RefSeq Alignments of RNAs Genes and Gene Predictions 
ncbiRefSeqOther RefSeq Other NCBI RefSeq Other Annotations (not NM_*, NR_*, XM_*, XR_*, NP_* or YP_*) Genes and Gene Predictions 
ncbiRefSeqCurated RefSeq Curated NCBI RefSeq genes, curated subset (NM_*, NR_*, NP_* or YP_*) Genes and Gene Predictions 
ncbiRefSeq RefSeq All NCBI RefSeq genes, curated and predicted (NM_*, XM_*, NR_*, XR_*, NP_*, YP_*) Genes and Gene Predictions 
cpgIslandExtUnmasked Unmasked CpG CpG Islands on All Sequence (Islands < 300 Bases are Light Green) Expression and Regulation Description CpG islands are associated with genes, particularly housekeeping genes, in vertebrates. CpG islands are typically common near transcription start sites and may be associated with promoter regions. Normally a C (cytosine) base followed immediately by a G (guanine) base (a CpG) is rare in vertebrate DNA because the Cs in such an arrangement tend to be methylated. This methylation helps distinguish the newly synthesized DNA strand from the parent strand, which aids in the final stages of DNA proofreading after duplication. However, over evolutionary time, methylated Cs tend to turn into Ts because of spontaneous deamination. The result is that CpGs are relatively rare unless there is selective pressure to keep them or a region is not methylated for some other reason, perhaps having to do with the regulation of gene expression. CpG islands are regions where CpGs are present at significantly higher levels than is typical for the genome as a whole. The unmasked version of the track displays potential CpG islands that exist in repeat regions and would otherwise not be visible in the repeat masked version. By default, only the masked version of the track is displayed. To view the unmasked version, change the visibility settings in the track controls at the top of this page. Methods CpG islands were predicted by searching the sequence one base at a time, scoring each dinucleotide (+17 for CG and -1 for others) and identifying maximally scoring segments. Each segment was then evaluated for the following criteria: GC content of 50% or greater length greater than 200 bp ratio greater than 0.6 of observed number of CG dinucleotides to the expected number on the basis of the number of Gs and Cs in the segment The entire genome sequence, masking areas included, was used for the construction of the track Unmasked CpG. The track CpG Islands is constructed on the sequence after all masked sequence is removed. The CpG count is the number of CG dinucleotides in the island. The Percentage CpG is the ratio of CpG nucleotide bases (twice the CpG count) to the length. The ratio of observed to expected CpG is calculated according to the formula (cited in Gardiner-Garden et al. (1987)): Obs/Exp CpG = Number of CpG * N / (Number of C * Number of G) where N = length of sequence. The calculation of the track data is performed by the following command sequence: twoBitToFa assembly.2bit stdout | maskOutFa stdin hard stdout \ | cpg_lh /dev/stdin 2&gt; cpg_lh.err \ | awk '{&dollar;2 = &dollar;2 - 1; width = &dollar;3 - &dollar;2; printf("%s\t%d\t%s\t%s %s\t%s\t%s\t%0.0f\t%0.1f\t%s\t%s\n", &dollar;1, &dollar;2, &dollar;3, &dollar;5, &dollar;6, width, &dollar;6, width*&dollar;7*0.01, 100.0*2*&dollar;6/width, &dollar;7, &dollar;9);}' \ | sort -k1,1 -k2,2n &gt; cpgIsland.bed The unmasked track data is constructed from twoBitToFa -noMask output for the twoBitToFa command. Data access CpG islands and its associated tables can be explored interactively using the REST API, the Table Browser or the Data Integrator. All the tables can also be queried directly from our public MySQL servers, with more information available on our help page as well as on our blog. The source for the cpg_lh program can be obtained from src/utils/cpgIslandExt/. The cpg_lh program binary can be obtained from: http://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/cpg_lh (choose "save file") Credits This track was generated using a modification of a program developed by G. Miklem and L. Hillier (unpublished). References Gardiner-Garden M, Frommer M. CpG islands in vertebrate genomes. J Mol Biol. 1987 Jul 20;196(2):261-82. PMID: 3656447 
intronEst Spliced ESTs D. melanogaster ESTs That Have Been Spliced mRNA and EST Description This track shows alignments between D. melanogaster expressed sequence tags (ESTs) in GenBank and the genome that show signs of splicing when aligned against the genome. ESTs are single-read sequences, typically about 500 bases in length, that usually represent fragments of transcribed genes. To be considered spliced, an EST must show evidence of at least one canonical intron, i.e. one that is at least 32 bases in length and has GT/AG ends. By requiring splicing, the level of contamination in the EST databases is drastically reduced at the expense of eliminating many genuine 3' ESTs. For a display of all ESTs (including unspliced), see the D. melanogaster EST track. Display Conventions and Configuration This track follows the display conventions for PSL alignment tracks. In dense display mode, darker shading indicates a larger number of aligned ESTs. The strand information (+/-) indicates the direction of the match between the EST and the matching genomic sequence. It bears no relationship to the direction of transcription of the RNA with which it might be associated. The description page for this track has a filter that can be used to change the display mode, alter the color, and include/exclude a subset of items within the track. This may be helpful when many items are shown in the track display, especially when only some are relevant to the current task. To use the filter: Type a term in one or more of the text boxes to filter the EST display. For example, to apply the filter to all ESTs expressed in a specific organ, type the name of the organ in the tissue box. To view the list of valid terms for each text box, consult the table in the Table Browser that corresponds to the factor on which you wish to filter. For example, the &quot;tissue&quot; table contains all the types of tissues that can be entered into the tissue text box. Wildcards may also be used in the filter. If filtering on more than one value, choose the desired combination logic. If &quot;and&quot; is selected, only ESTs that match all filter criteria will be highlighted. If &quot;or&quot; is selected, ESTs that match any one of the filter criteria will be highlighted. Choose the color or display characteristic that should be used to highlight or include/exclude the filtered items. If &quot;exclude&quot; is chosen, the browser will not display ESTs that match the filter criteria. If &quot;include&quot; is selected, the browser will display only those ESTs that match the filter criteria. This track may also be configured to display base labeling, a feature that allows the user to display all bases in the aligning sequence or only those that differ from the genomic sequence. For more information about this option, click here. Methods To make an EST, RNA is isolated from cells and reverse transcribed into cDNA. Typically, the cDNA is cloned into a plasmid vector and a read is taken from the 5' and/or 3' primer. For most &mdash; but not all &mdash; ESTs, the reverse transcription is primed by an oligo-dT, which hybridizes with the poly-A tail of mature mRNA. The reverse transcriptase may or may not make it to the 5' end of the mRNA, which may or may not be degraded. In general, the 3' ESTs mark the end of transcription reasonably well, but the 5' ESTs may end at any point within the transcript. Some of the newer cap-selected libraries cover transcription start reasonably well. Before the cap-selection techniques emerged, some projects used random rather than poly-A priming in an attempt to retrieve sequence distant from the 3' end. These projects were successful at this, but as a side effect also deposited sequences from unprocessed mRNA and perhaps even genomic sequences into the EST databases. Even outside of the random-primed projects, there is a degree of non-mRNA contamination. Because of this, a single unspliced EST should be viewed with considerable skepticism. To generate this track, D. melanogaster ESTs from GenBank were aligned against the genome using blat. Note that the maximum intron length allowed by blat is 750,000 bases, which may eliminate some ESTs with very long introns that might otherwise align. When a single EST aligned in multiple places, the alignment having the highest base identity was identified. Only alignments having a base identity level within 0.5% of the best and at least 96% base identity with the genomic sequence are displayed in this track. Credits This track was produced at UCSC from EST sequence data submitted to the international public sequence databases by scientists worldwide. References Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. GenBank: update. Nucleic Acids Res. 2004 Jan 1;32(Database issue):D23-6. PMID: 14681350; PMC: PMC308779 Kent WJ. BLAT - the BLAST-like alignment tool. Genome Res. 2002 Apr;12(4):656-64. PMID: 11932250; PMC: PMC187518 
gold Assembly Assembly from Fragments Mapping and Sequencing Description This track shows the sequences used in the Aug. 2014 D. melanogaster genome assembly. Genome assembly procedures are covered in the NCBI assembly documentation. NCBI also provides specific information about this assembly. The definition of this assembly is from the AGP file delivered with the sequence. The NCBI document AGP Specification describes the format of the AGP file. In dense mode, this track depicts the contigs that make up the currently viewed scaffold. Contig boundaries are distinguished by the use of alternating gold and brown coloration. Where gaps exist between contigs, spaces are shown between the gold and brown blocks. The relative order and orientation of the contigs within a scaffold is always known; therefore, a line is drawn in the graphical display to bridge the blocks. Component types found in this track (with counts of that type in parentheses): W - whole genome shotgun (1,862) O - other sequence (8) 
augustusGene AUGUSTUS AUGUSTUS ab initio gene predictions v3.1 Genes and Gene Predictions Description This track shows ab initio predictions from the program AUGUSTUS (version 3.1). The predictions are based on the genome sequence alone. For more information on the different gene tracks, see our Genes FAQ. Methods Statistical signal models were built for splice sites, branch-point patterns, translation start sites, and the poly-A signal. Furthermore, models were built for the sequence content of protein-coding and non-coding regions as well as for the length distributions of different exon and intron types. Detailed descriptions of most of these different models can be found in Mario Stanke's dissertation. This track shows the most likely gene structure according to a Semi-Markov Conditional Random Field model. Alternative splicing transcripts were obtained with a sampling algorithm (--alternatives-from-sampling=true --sample=100 --minexonintronprob=0.2 --minmeanexonintronprob=0.5 --maxtracks=3 --temperature=2). The different models used by Augustus were trained on a number of different species-specific gene sets, which included 1000-2000 training gene structures. The --species option allows one to choose the species used for training the models. Different training species were used for the --species option when generating these predictions for different groups of assemblies. Assembly Group Training Species Fish zebrafish Birds chicken Human and all other vertebrates human Nematodes caenorhabditis Drosophila fly A. mellifera honeybee1 A. gambiae culex S. cerevisiae saccharomyces This table describes which training species was used for a particular group of assemblies. When available, the closest related training species was used. Credits Thanks to the Stanke lab for providing the AUGUSTUS program. The training for the chicken version was done by Stefanie K&ouml;nig and the training for the human and zebrafish versions was done by Mario Stanke. References Stanke M, Diekhans M, Baertsch R, Haussler D. Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics. 2008 Mar 1;24(5):637-44. PMID: 18218656 Stanke M, Waack S. Gene prediction with a hidden Markov model and a new intron submodel. Bioinformatics. 2003 Oct;19 Suppl 2:ii215-25. PMID: 14534192 
crisprRanges CRISPR Regions Genome regions processed to find CRISPR/Cas9 target sites (exons +/- 200 bp) Genes and Gene Predictions Description This track shows regions of the genome within 200 bp of transcribed regions and DNA sequences targetable by CRISPR RNA guides using the Cas9 enzyme from S. pyogenes (PAM: NGG). CRISPR target sites were annotated with predicted specificity (off-target effects) and predicted efficiency (on-target cleavage) by various algorithms through the tool CRISPOR. Display Conventions and Configuration The track &quot;CRISPR Regions&quot; shows the regions of the genome where target sites were analyzed, i.e. within 200 bp of transcribed regions as annotated by Ensembl transcript models. The track &quot;CRISPR Targets&quot; shows the target sites in these regions. The target sequence of the guide is shown with a thick (exon) bar. The PAM motif match (NGG) is shown with a thinner bar. Guides are colored to reflect both predicted specificity and efficiency. Specificity reflects the &quot;uniqueness&quot; of a 20mer sequence in the genome; the less unique a sequence is, the more likely it is to cleave other locations of the genome (off-target effects). Efficiency is the frequency of cleavage at the target site (on-target efficiency). Shades of gray stand for sites that are hard to target specifically, as the 20mer is not very unique in the genome: impossible to target: target site has at least one identical copy in the genome and was not scored hard to target: many similar sequences in the genome that alignment stopped, repeat? hard to target: target site was aligned but results in a low specificity score &lt;= 50 (see below) Colors highlight targets that are specific in the genome (MIT specificity &gt; 50) but have different predicted efficiencies: unable to calculate Doench/Fusi 2016 efficiency score low predicted cleavage: Doench/Fusi 2016 Efficiency percentile &lt;= 30 medium predicted cleavage: Doench/Fusi 2016 Efficiency percentile &gt; 30 and &lt; 55 high predicted cleavage: Doench/Fusi 2016 Efficiency &gt; 55 Mouse-over a target site to show predicted specificity and efficiency scores: The MIT Specificity score summarizes all off-targets into a single number from 0-100. The higher the number, the fewer off-target effects are expected. We recommend guides with an MIT specificity &gt; 50. The efficiency score tries to predict if a guide leads to rather strong or weak cleavage. According to (Haeussler et al. 2016), the Doench 2016 Efficiency score should be used to select the guide with the highest cleavage efficiency when expressing guides from RNA PolIII Promoters such as U6. Scores are given as percentiles, e.g. &quot;70%&quot; means that 70% of mammalian guides have a score equal or lower than this guide. The raw score number is also shown in parentheses after the percentile. The Moreno-Mateos 2015 Efficiency score should be used instead of the Doench 2016 score when transcribing the guide in vitro with a T7 promoter, e.g. for injections in mouse, zebrafish or Xenopus embryos. The Moreno-Mateos score is given in percentiles and the raw value in parentheses, see the note above. Click onto features to show all scores and predicted off-targets with up to four mismatches. The Out-of-Frame score by Bae et al. 2014 is correlated with the probability that mutations induced by the guide RNA will disrupt the open reading frame. The authors recommend out-of-frame scores &gt; 66 to create knock-outs with a single guide efficiently. Off-target sites are sorted by the CFD (Cutting Frequency Determination) score (Doench et al. 2016). The higher the CFD score, the more likely there is off-target cleavage at that site. Off-targets with a CFD score &lt; 0.023 are not shown on this page, but are availble when following the link to the external CRISPOR tool. When compared against experimentally validated off-targets by Haeussler et al. 2016, the large majority of predicted off-targets with CFD scores &lt; 0.023 were false-positives. Methods Relationship between predictions and experimental data Like most algorithms, the MIT specificity score is not always a perfect predictor of off-target effects. Despite low scores, many tested guides caused few and/or weak off-target cleavage when tested with whole-genome assays (Figure 2 from Haeussler et al. 2016), as shown below, and the published data contains few data points with high specificity scores. Overall though, the assays showed that the higher the specificity score, the lower the off-target effects. Similarly, efficiency scoring is not very accurate: guides with low scores can be efficient and vice versa. As a general rule, however, the higher the score, the less likely that a guide is very inefficient. The following histograms illustrate, for each type of score, how the share of inefficient guides drops with increasing efficiency scores: When reading this plot, keep in mind that both scores were evaluated on their own training data. Especially for the Moreno-Mateos score, the results are too optimistic, due to overfitting. When evaluated on independent datasets, the correlation of the prediction with other assays was around 25% lower, see Haeussler et al. 2016. At the time of writing, there is no independent dataset available yet to determine the Moreno-Mateos accuracy for each score percentile range. Track methods Exons as predicted by Ensembl Gene models were used, extended by 200 basepairs on each side, searched for the -NGG motif. Flanking 20mer guide sequences were aligned to the genome with BWA and scored with MIT Specificity scores using the command-line version of crispor.org. Non-unique guide sequences were skipped. Flanking sequences were extracted from the genome and input for Crispor efficiency scoring, available from the Crispor downloads page, which includes the Doench 2016, Moreno-Mateos 2015 and Bae 2014 algorithms, among others. Data Access The raw data can be explored interactively with the Table Browser. For automated analysis, the genome annotation is stored in a bigBed file that can be downloaded from our download server. The files for this track are called crispr.bb and crisprDetails.tab and are located in the /gbdb/dm6/crispr directory of our downloads server. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed, which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g. bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg19/crisprRanges/crispr.bb -chrom=chr21 -start=0 -end=10000000 stdout The file crisprDetails.tab includes the details of the off-targets. The last column of the bigBed file is the offset of the respective line in crisprDetails.tab. E.g. if the last column is 14227033723, then the following command will extract the line with the corresponding off-target details: curl -s -r 14227033723-14227043723 http://hgdownload.soe.ucsc.edu/gbdb/hg19/crispr/crisprDetails.tab | head -n1. The off-target details can currently not be joined with the table browser. The file crisprDetails.tab is a tab-separated text file with two fields. The first field contains the numbers of off-targets for each mismatch, e.g. "0,0,1,3,49" means 0 off-targets at zero mismatches, 1 at two mismatches, 3 at three and 49 off-targets at four mismatches. The second field is a pipe-separated list of semicolon-separated tuples with the genome coordinates and the CFD score. E.g. "chr10;123376795+;42|chr5;148353274-;39" describes two off-targets, with the first at chr1:123376795 on the positive strand and a CFD score 0.42 Credits Track created by Maximilian Haeussler and Hiram Clawson, with helpful input from Jean-Paul Concordet (MNHN Paris) and Alberto Stolfi (NYU). References Haeussler M, Sch&#246;nig K, Eckert H, Eschstruth A, Miann&#233; J, Renaud JB, Schneider-Maunoury S, Shkumatava A, Teboul L, Kent J et al. Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biol. 2016 Jul 5;17(1):148. PMID: 27380939; PMC: PMC4934014 Bae S, Kweon J, Kim HS, Kim JS. Microhomology-based choice of Cas9 nuclease target sites. Nat Methods. 2014 Jul;11(7):705-6. PMID: 24972169 Doench JG, Fusi N, Sullender M, Hegde M, Vaimberg EW, Donovan KF, Smith I, Tothova Z, Wilen C, Orchard R et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat Biotechnol. 2016 Feb;34(2):184-91. PMID: 26780180; PMC: PMC4744125 Hsu PD, Scott DA, Weinstein JA, Ran FA, Konermann S, Agarwala V, Li Y, Fine EJ, Wu X, Shalem O et al. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat Biotechnol. 2013 Sep;31(9):827-32. PMID: 23873081; PMC: PMC3969858 Moreno-Mateos MA, Vejnar CE, Beaudoin JD, Fernandez JP, Mis EK, Khokha MK, Giraldez AJ. CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo. Nat Methods. 2015 Oct;12(10):982-8. PMID: 26322839; PMC: PMC4589495 
crispr CRISPR CRISPR/Cas9 Sp. Pyog. target sites Genes and Gene Predictions Description This track shows regions of the genome within 200 bp of transcribed regions and DNA sequences targetable by CRISPR RNA guides using the Cas9 enzyme from S. pyogenes (PAM: NGG). CRISPR target sites were annotated with predicted specificity (off-target effects) and predicted efficiency (on-target cleavage) by various algorithms through the tool CRISPOR. Display Conventions and Configuration The track &quot;CRISPR Regions&quot; shows the regions of the genome where target sites were analyzed, i.e. within 200 bp of transcribed regions as annotated by Ensembl transcript models. The track &quot;CRISPR Targets&quot; shows the target sites in these regions. The target sequence of the guide is shown with a thick (exon) bar. The PAM motif match (NGG) is shown with a thinner bar. Guides are colored to reflect both predicted specificity and efficiency. Specificity reflects the &quot;uniqueness&quot; of a 20mer sequence in the genome; the less unique a sequence is, the more likely it is to cleave other locations of the genome (off-target effects). Efficiency is the frequency of cleavage at the target site (on-target efficiency). Shades of gray stand for sites that are hard to target specifically, as the 20mer is not very unique in the genome: impossible to target: target site has at least one identical copy in the genome and was not scored hard to target: many similar sequences in the genome that alignment stopped, repeat? hard to target: target site was aligned but results in a low specificity score &lt;= 50 (see below) Colors highlight targets that are specific in the genome (MIT specificity &gt; 50) but have different predicted efficiencies: unable to calculate Doench/Fusi 2016 efficiency score low predicted cleavage: Doench/Fusi 2016 Efficiency percentile &lt;= 30 medium predicted cleavage: Doench/Fusi 2016 Efficiency percentile &gt; 30 and &lt; 55 high predicted cleavage: Doench/Fusi 2016 Efficiency &gt; 55 Mouse-over a target site to show predicted specificity and efficiency scores: The MIT Specificity score summarizes all off-targets into a single number from 0-100. The higher the number, the fewer off-target effects are expected. We recommend guides with an MIT specificity &gt; 50. The efficiency score tries to predict if a guide leads to rather strong or weak cleavage. According to (Haeussler et al. 2016), the Doench 2016 Efficiency score should be used to select the guide with the highest cleavage efficiency when expressing guides from RNA PolIII Promoters such as U6. Scores are given as percentiles, e.g. &quot;70%&quot; means that 70% of mammalian guides have a score equal or lower than this guide. The raw score number is also shown in parentheses after the percentile. The Moreno-Mateos 2015 Efficiency score should be used instead of the Doench 2016 score when transcribing the guide in vitro with a T7 promoter, e.g. for injections in mouse, zebrafish or Xenopus embryos. The Moreno-Mateos score is given in percentiles and the raw value in parentheses, see the note above. Click onto features to show all scores and predicted off-targets with up to four mismatches. The Out-of-Frame score by Bae et al. 2014 is correlated with the probability that mutations induced by the guide RNA will disrupt the open reading frame. The authors recommend out-of-frame scores &gt; 66 to create knock-outs with a single guide efficiently. Off-target sites are sorted by the CFD (Cutting Frequency Determination) score (Doench et al. 2016). The higher the CFD score, the more likely there is off-target cleavage at that site. Off-targets with a CFD score &lt; 0.023 are not shown on this page, but are availble when following the link to the external CRISPOR tool. When compared against experimentally validated off-targets by Haeussler et al. 2016, the large majority of predicted off-targets with CFD scores &lt; 0.023 were false-positives. Methods Relationship between predictions and experimental data Like most algorithms, the MIT specificity score is not always a perfect predictor of off-target effects. Despite low scores, many tested guides caused few and/or weak off-target cleavage when tested with whole-genome assays (Figure 2 from Haeussler et al. 2016), as shown below, and the published data contains few data points with high specificity scores. Overall though, the assays showed that the higher the specificity score, the lower the off-target effects. Similarly, efficiency scoring is not very accurate: guides with low scores can be efficient and vice versa. As a general rule, however, the higher the score, the less likely that a guide is very inefficient. The following histograms illustrate, for each type of score, how the share of inefficient guides drops with increasing efficiency scores: When reading this plot, keep in mind that both scores were evaluated on their own training data. Especially for the Moreno-Mateos score, the results are too optimistic, due to overfitting. When evaluated on independent datasets, the correlation of the prediction with other assays was around 25% lower, see Haeussler et al. 2016. At the time of writing, there is no independent dataset available yet to determine the Moreno-Mateos accuracy for each score percentile range. Track methods Exons as predicted by Ensembl Gene models were used, extended by 200 basepairs on each side, searched for the -NGG motif. Flanking 20mer guide sequences were aligned to the genome with BWA and scored with MIT Specificity scores using the command-line version of crispor.org. Non-unique guide sequences were skipped. Flanking sequences were extracted from the genome and input for Crispor efficiency scoring, available from the Crispor downloads page, which includes the Doench 2016, Moreno-Mateos 2015 and Bae 2014 algorithms, among others. Data Access The raw data can be explored interactively with the Table Browser. For automated analysis, the genome annotation is stored in a bigBed file that can be downloaded from our download server. The files for this track are called crispr.bb and crisprDetails.tab and are located in the /gbdb/dm6/crispr directory of our downloads server. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed, which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g. bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg19/crispr/crispr.bb -chrom=chr21 -start=0 -end=10000000 stdout The file crisprDetails.tab includes the details of the off-targets. The last column of the bigBed file is the offset of the respective line in crisprDetails.tab. E.g. if the last column is 14227033723, then the following command will extract the line with the corresponding off-target details: curl -s -r 14227033723-14227043723 http://hgdownload.soe.ucsc.edu/gbdb/hg19/crispr/crisprDetails.tab | head -n1. The off-target details can currently not be joined with the table browser. The file crisprDetails.tab is a tab-separated text file with two fields. The first field contains the numbers of off-targets for each mismatch, e.g. "0,0,1,3,49" means 0 off-targets at zero mismatches, 1 at two mismatches, 3 at three and 49 off-targets at four mismatches. The second field is a pipe-separated list of semicolon-separated tuples with the genome coordinates and the CFD score. E.g. "chr10;123376795+;42|chr5;148353274-;39" describes two off-targets, with the first at chr1:123376795 on the positive strand and a CFD score 0.42 Credits Track created by Maximilian Haeussler and Hiram Clawson, with helpful input from Jean-Paul Concordet (MNHN Paris) and Alberto Stolfi (NYU). References Haeussler M, Sch&#246;nig K, Eckert H, Eschstruth A, Miann&#233; J, Renaud JB, Schneider-Maunoury S, Shkumatava A, Teboul L, Kent J et al. Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biol. 2016 Jul 5;17(1):148. PMID: 27380939; PMC: PMC4934014 Bae S, Kweon J, Kim HS, Kim JS. Microhomology-based choice of Cas9 nuclease target sites. Nat Methods. 2014 Jul;11(7):705-6. PMID: 24972169 Doench JG, Fusi N, Sullender M, Hegde M, Vaimberg EW, Donovan KF, Smith I, Tothova Z, Wilen C, Orchard R et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat Biotechnol. 2016 Feb;34(2):184-91. PMID: 26780180; PMC: PMC4744125 Hsu PD, Scott DA, Weinstein JA, Ran FA, Konermann S, Agarwala V, Li Y, Fine EJ, Wu X, Shalem O et al. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat Biotechnol. 2013 Sep;31(9):827-32. PMID: 23873081; PMC: PMC3969858 Moreno-Mateos MA, Vejnar CE, Beaudoin JD, Fernandez JP, Mis EK, Khokha MK, Giraldez AJ. CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo. Nat Methods. 2015 Oct;12(10):982-8. PMID: 26322839; PMC: PMC4589495 
crisprTargets CRISPR Targets CRISPR/Cas9 -NGG Targets Genes and Gene Predictions Description This track shows regions of the genome within 200 bp of transcribed regions and DNA sequences targetable by CRISPR RNA guides using the Cas9 enzyme from S. pyogenes (PAM: NGG). CRISPR target sites were annotated with predicted specificity (off-target effects) and predicted efficiency (on-target cleavage) by various algorithms through the tool CRISPOR. Display Conventions and Configuration The track &quot;CRISPR Regions&quot; shows the regions of the genome where target sites were analyzed, i.e. within 200 bp of transcribed regions as annotated by Ensembl transcript models. The track &quot;CRISPR Targets&quot; shows the target sites in these regions. The target sequence of the guide is shown with a thick (exon) bar. The PAM motif match (NGG) is shown with a thinner bar. Guides are colored to reflect both predicted specificity and efficiency. Specificity reflects the &quot;uniqueness&quot; of a 20mer sequence in the genome; the less unique a sequence is, the more likely it is to cleave other locations of the genome (off-target effects). Efficiency is the frequency of cleavage at the target site (on-target efficiency). Shades of gray stand for sites that are hard to target specifically, as the 20mer is not very unique in the genome: impossible to target: target site has at least one identical copy in the genome and was not scored hard to target: many similar sequences in the genome that alignment stopped, repeat? hard to target: target site was aligned but results in a low specificity score &lt;= 50 (see below) Colors highlight targets that are specific in the genome (MIT specificity &gt; 50) but have different predicted efficiencies: unable to calculate Doench/Fusi 2016 efficiency score low predicted cleavage: Doench/Fusi 2016 Efficiency percentile &lt;= 30 medium predicted cleavage: Doench/Fusi 2016 Efficiency percentile &gt; 30 and &lt; 55 high predicted cleavage: Doench/Fusi 2016 Efficiency &gt; 55 Mouse-over a target site to show predicted specificity and efficiency scores: The MIT Specificity score summarizes all off-targets into a single number from 0-100. The higher the number, the fewer off-target effects are expected. We recommend guides with an MIT specificity &gt; 50. The efficiency score tries to predict if a guide leads to rather strong or weak cleavage. According to (Haeussler et al. 2016), the Doench 2016 Efficiency score should be used to select the guide with the highest cleavage efficiency when expressing guides from RNA PolIII Promoters such as U6. Scores are given as percentiles, e.g. &quot;70%&quot; means that 70% of mammalian guides have a score equal or lower than this guide. The raw score number is also shown in parentheses after the percentile. The Moreno-Mateos 2015 Efficiency score should be used instead of the Doench 2016 score when transcribing the guide in vitro with a T7 promoter, e.g. for injections in mouse, zebrafish or Xenopus embryos. The Moreno-Mateos score is given in percentiles and the raw value in parentheses, see the note above. Click onto features to show all scores and predicted off-targets with up to four mismatches. The Out-of-Frame score by Bae et al. 2014 is correlated with the probability that mutations induced by the guide RNA will disrupt the open reading frame. The authors recommend out-of-frame scores &gt; 66 to create knock-outs with a single guide efficiently. Off-target sites are sorted by the CFD (Cutting Frequency Determination) score (Doench et al. 2016). The higher the CFD score, the more likely there is off-target cleavage at that site. Off-targets with a CFD score &lt; 0.023 are not shown on this page, but are availble when following the link to the external CRISPOR tool. When compared against experimentally validated off-targets by Haeussler et al. 2016, the large majority of predicted off-targets with CFD scores &lt; 0.023 were false-positives. Methods Relationship between predictions and experimental data Like most algorithms, the MIT specificity score is not always a perfect predictor of off-target effects. Despite low scores, many tested guides caused few and/or weak off-target cleavage when tested with whole-genome assays (Figure 2 from Haeussler et al. 2016), as shown below, and the published data contains few data points with high specificity scores. Overall though, the assays showed that the higher the specificity score, the lower the off-target effects. Similarly, efficiency scoring is not very accurate: guides with low scores can be efficient and vice versa. As a general rule, however, the higher the score, the less likely that a guide is very inefficient. The following histograms illustrate, for each type of score, how the share of inefficient guides drops with increasing efficiency scores: When reading this plot, keep in mind that both scores were evaluated on their own training data. Especially for the Moreno-Mateos score, the results are too optimistic, due to overfitting. When evaluated on independent datasets, the correlation of the prediction with other assays was around 25% lower, see Haeussler et al. 2016. At the time of writing, there is no independent dataset available yet to determine the Moreno-Mateos accuracy for each score percentile range. Track methods Exons as predicted by Ensembl Gene models were used, extended by 200 basepairs on each side, searched for the -NGG motif. Flanking 20mer guide sequences were aligned to the genome with BWA and scored with MIT Specificity scores using the command-line version of crispor.org. Non-unique guide sequences were skipped. Flanking sequences were extracted from the genome and input for Crispor efficiency scoring, available from the Crispor downloads page, which includes the Doench 2016, Moreno-Mateos 2015 and Bae 2014 algorithms, among others. Data Access The raw data can be explored interactively with the Table Browser. For automated analysis, the genome annotation is stored in a bigBed file that can be downloaded from our download server. The files for this track are called crispr.bb and crisprDetails.tab and are located in the /gbdb/dm6/crispr directory of our downloads server. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed, which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g. bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg19/crisprTargets/crispr.bb -chrom=chr21 -start=0 -end=10000000 stdout The file crisprDetails.tab includes the details of the off-targets. The last column of the bigBed file is the offset of the respective line in crisprDetails.tab. E.g. if the last column is 14227033723, then the following command will extract the line with the corresponding off-target details: curl -s -r 14227033723-14227043723 http://hgdownload.soe.ucsc.edu/gbdb/hg19/crispr/crisprDetails.tab | head -n1. The off-target details can currently not be joined with the table browser. The file crisprDetails.tab is a tab-separated text file with two fields. The first field contains the numbers of off-targets for each mismatch, e.g. "0,0,1,3,49" means 0 off-targets at zero mismatches, 1 at two mismatches, 3 at three and 49 off-targets at four mismatches. The second field is a pipe-separated list of semicolon-separated tuples with the genome coordinates and the CFD score. E.g. "chr10;123376795+;42|chr5;148353274-;39" describes two off-targets, with the first at chr1:123376795 on the positive strand and a CFD score 0.42 Credits Track created by Maximilian Haeussler and Hiram Clawson, with helpful input from Jean-Paul Concordet (MNHN Paris) and Alberto Stolfi (NYU). References Haeussler M, Sch&#246;nig K, Eckert H, Eschstruth A, Miann&#233; J, Renaud JB, Schneider-Maunoury S, Shkumatava A, Teboul L, Kent J et al. Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biol. 2016 Jul 5;17(1):148. PMID: 27380939; PMC: PMC4934014 Bae S, Kweon J, Kim HS, Kim JS. Microhomology-based choice of Cas9 nuclease target sites. Nat Methods. 2014 Jul;11(7):705-6. PMID: 24972169 Doench JG, Fusi N, Sullender M, Hegde M, Vaimberg EW, Donovan KF, Smith I, Tothova Z, Wilen C, Orchard R et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat Biotechnol. 2016 Feb;34(2):184-91. PMID: 26780180; PMC: PMC4744125 Hsu PD, Scott DA, Weinstein JA, Ran FA, Konermann S, Agarwala V, Li Y, Fine EJ, Wu X, Shalem O et al. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat Biotechnol. 2013 Sep;31(9):827-32. PMID: 23873081; PMC: PMC3969858 Moreno-Mateos MA, Vejnar CE, Beaudoin JD, Fernandez JP, Mis EK, Khokha MK, Giraldez AJ. CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo. Nat Methods. 2015 Oct;12(10):982-8. PMID: 26322839; PMC: PMC4589495 
est D. melanogaster ESTs D. melanogaster ESTs Including Unspliced mRNA and EST Description This track shows alignments between D. melanogaster expressed sequence tags (ESTs) in GenBank and the genome. ESTs are single-read sequences, typically about 500 bases in length, that usually represent fragments of transcribed genes. Display Conventions and Configuration This track follows the display conventions for PSL alignment tracks. In dense display mode, the items that are more darkly shaded indicate matches of better quality. The strand information (+/-) indicates the direction of the match between the EST and the matching genomic sequence. It bears no relationship to the direction of transcription of the RNA with which it might be associated. The description page for this track has a filter that can be used to change the display mode, alter the color, and include/exclude a subset of items within the track. This may be helpful when many items are shown in the track display, especially when only some are relevant to the current task. To use the filter: Type a term in one or more of the text boxes to filter the EST display. For example, to apply the filter to all ESTs expressed in a specific organ, type the name of the organ in the tissue box. To view the list of valid terms for each text box, consult the table in the Table Browser that corresponds to the factor on which you wish to filter. For example, the &quot;tissue&quot; table contains all the types of tissues that can be entered into the tissue text box. Wildcards may also be used in the filter. If filtering on more than one value, choose the desired combination logic. If &quot;and&quot; is selected, only ESTs that match all filter criteria will be highlighted. If &quot;or&quot; is selected, ESTs that match any one of the filter criteria will be highlighted. Choose the color or display characteristic that should be used to highlight or include/exclude the filtered items. If &quot;exclude&quot; is chosen, the browser will not display ESTs that match the filter criteria. If &quot;include&quot; is selected, the browser will display only those ESTs that match the filter criteria. This track may also be configured to display base labeling, a feature that allows the user to display all bases in the aligning sequence or only those that differ from the genomic sequence. For more information about this option, click here. Methods To make an EST, RNA is isolated from cells and reverse transcribed into cDNA. Typically, the cDNA is cloned into a plasmid vector and a read is taken from the 5' and/or 3' primer. For most &mdash; but not all &mdash; ESTs, the reverse transcription is primed by an oligo-dT, which hybridizes with the poly-A tail of mature mRNA. The reverse transcriptase may or may not make it to the 5' end of the mRNA, which may or may not be degraded. In general, the 3' ESTs mark the end of transcription reasonably well, but the 5' ESTs may end at any point within the transcript. Some of the newer cap-selected libraries cover transcription start reasonably well. Before the cap-selection techniques emerged, some projects used random rather than poly-A priming in an attempt to retrieve sequence distant from the 3' end. These projects were successful at this, but as a side effect also deposited sequences from unprocessed mRNA and perhaps even genomic sequences into the EST databases. Even outside of the random-primed projects, there is a degree of non-mRNA contamination. Because of this, a single unspliced EST should be viewed with considerable skepticism. To generate this track, D. melanogaster ESTs from GenBank were aligned against the genome using blat. Note that the maximum intron length allowed by blat is 750,000 bases, which may eliminate some ESTs with very long introns that might otherwise align. When a single EST aligned in multiple places, the alignment having the highest base identity was identified. Only alignments having a base identity level within 0.5% of the best and at least 96% base identity with the genomic sequence are displayed in this track. Credits This track was produced at UCSC from EST sequence data submitted to the international public sequence databases by scientists worldwide. References Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. GenBank: update. Nucleic Acids Res. 2004 Jan 1;32(Database issue):D23-6. PMID: 14681350; PMC: PMC308779 Kent WJ. BLAT - the BLAST-like alignment tool. Genome Res. 2002 Apr;12(4):656-64. PMID: 11932250; PMC: PMC187518 
mrna D. melanogaster mRNAs D. melanogaster mRNAs from GenBank mRNA and EST Description The mRNA track shows alignments between D. melanogaster mRNAs in GenBank and the genome. Display Conventions and Configuration This track follows the display conventions for PSL alignment tracks. In dense display mode, the items that are more darkly shaded indicate matches of better quality. The description page for this track has a filter that can be used to change the display mode, alter the color, and include/exclude a subset of items within the track. This may be helpful when many items are shown in the track display, especially when only some are relevant to the current task. To use the filter: Type a term in one or more of the text boxes to filter the mRNA display. For example, to apply the filter to all mRNAs expressed in a specific organ, type the name of the organ in the tissue box. To view the list of valid terms for each text box, consult the table in the Table Browser that corresponds to the factor on which you wish to filter. For example, the &quot;tissue&quot; table contains all the types of tissues that can be entered into the tissue text box. Wildcards may also be used in the filter. If filtering on more than one value, choose the desired combination logic. If &quot;and&quot; is selected, only mRNAs that match all filter criteria will be highlighted. If &quot;or&quot; is selected, mRNAs that match any one of the filter criteria will be highlighted. Choose the color or display characteristic that should be used to highlight or include/exclude the filtered items. If &quot;exclude&quot; is chosen, the browser will not display mRNAs that match the filter criteria. If &quot;include&quot; is selected, the browser will display only those mRNAs that match the filter criteria. This track may also be configured to display codon coloring, a feature that allows the user to quickly compare mRNAs against the genomic sequence. For more information about this option, click here. Methods GenBank D. melanogaster mRNAs were aligned against the genome using the blat program. When a single mRNA aligned in multiple places, the alignment having the highest base identity was found. Only alignments having a base identity level within 0.5% of the best and at least 96% base identity with the genomic sequence were kept. Credits The mRNA track was produced at UCSC from mRNA sequence data submitted to the international public sequence databases by scientists worldwide. References Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. GenBank: update. Nucleic Acids Res. 2004 Jan 1;32(Database issue):D23-6. PMID: 14681350; PMC: PMC308779 Kent WJ. BLAT - the BLAST-like alignment tool. Genome Res. 2002 Apr;12(4):656-64. PMID: 11932250; PMC: PMC187518 
ensGene Ensembl Genes Ensembl Genes Genes and Gene Predictions Description These gene predictions were generated by Ensembl. For more information on the different gene tracks, see our Genes FAQ. Methods For a description of the methods used in Ensembl gene predictions, please refer to Hubbard et al. (2002), also listed in the References section below. Data access Ensembl Gene data can be explored interactively using the Table Browser or the Data Integrator. For local downloads, the genePred format files for dm6 are available in our downloads directory as ensGene.txt.gz or in our genes download directory in GTF format. For programmatic access, the data can be queried from the REST API or directly from our public MySQL servers. Instructions on this method are available on our MySQL help page and on our blog. Previous versions of this track can be found on our archive download server. Credits We would like to thank Ensembl for providing these gene annotations. For more information, please see Ensembl&#39s genome annotation page. References Hubbard T, Barker D, Birney E, Cameron G, Chen Y, Clark L, Cox T, Cuff J, Curwen V, Down T et al. The Ensembl genome database project. Nucleic Acids Res. 2002 Jan 1;30(1):38-41. PMID: 11752248; PMC: PMC99161 
evaSnp EVA SNP Release 3 Short Genetic Variants from European Variant Archive Release 3 Variation and Repeats Description This track contains mappings of single nucleotide variants and small insertions and deletions (indels) from the European Variation Archive (EVA) Release 3 for the D. melanogaster dm6 genome. The dbSNP database at NCBI no longer hosts non-human variants. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP variant corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. The display is set to automatically collapse to dense visibility when there are more than 100k variants in the window. When the window size is more than 250k bp, the display is switched to density graph mode. Searching, details, and filtering Navigation to an individual variant can be accomplished by typing or copying the variant identifier (rsID) or the genomic coordinates into the Position/Search box on the Browser. A click on an item in the graphical display displays a page with data about that variant. Data fields include the Reference and Alternate Alleles, the class of the variant as reported by EVA, the source of the data, the amino acid change, if any, and the functional class as determined by UCSC's Variant Annotation Integrator. Variants can be filtered using the track controls to show subsets of the data by either EVA Sequence Ontology (SO) term, UCSC-generated functional effect, or by color, which bins the UCSC functional effects into general classes. Mouse-over Mousing over an item shows the ucscClass, which is the consequence according to the Variant Annotation Integrator, and the aaChange when one is available, which is the change in amino acid in HGVS.p terms. Items may have multiple ucscClasses, which will all be shown in the mouse-over in a comma-separated list. Likewise, multiple HGVS.p terms may be shown for each rsID separated by spaces describing all possible AA changes. Multiple items may appear due to different variant predictions on multiple gene transcripts. For all organisms the gene models used were ncbiRefSeqCurated, except for mm39 which used ncbiRefSeqSelect. Track colors Variants are colored according to the most potentially deleterious functional effect prediction according to the Variant Annotation Integrator. Specific bins can be seen in the Methods section below. Color Variant Type Protein-altering variants and splice site variants Synonymous codon variants Non-coding transcript or Untranslated Region (UTR) variants Intergenic and intronic variants Sequence ontology (SO) Variants are classified by EVA into one of the following sequence ontology terms: substitution &mdash; A single nucleotide in the reference is replaced by another, alternate allele deletion &mdash; One or more nucleotides is deleted. The representation in the database is to display one additional nucleotide in both the Reference field (Ref) and the Alternate Allele field (Alt). E.g. a variant that is a deletion of an A maybe be represented as Ref = GA and Alt = G. insertion &mdash; One or more nucleotides is inserted. The representation in the database is to display one additional nucleotide in both the Reference field (Ref) and the Alternate Allele field (Alt). E.g. a variant that is an insertion of a T maybe be represented as Ref = G and Alt = GT delins &mdash; Similar to tandemRepeat, in that the runs of Ref and Alt Alleles are of different length, except that there is more than one type of nucleotide, e.g., Ref = CCAAAAACAAAAACA, Alt = ACAAAAAC. multipleNucleotideVariant &mdash; More than one nucleotide is substituted by an equal number of different nucleotides, e.g., Ref = AA, Alt = GC. sequence alteration &mdash; A parent term meant to signify a deviation from another sequence. Can be assigned to variants that have not been characterized yet. Methods Data were downloaded from the European Variation Archive EVA release 3 (2022-02-24) current_ids.vcf.gz files corresponding to the proper assembly. Chromosome names were converted to UCSC-style, a few problematic variants were removed, and the variants passed through the Variant Annotation Integrator to predict consequence. For every organism the ncbiRefSeqCurated gene models were used to predict the consequences, except for mm39 which used the ncbiRefSeqSelect models. Variants were then colored according to their predicted consequence in the following fashion: Protein-altering variants and splice site variants - exon_loss_variant, frameshift_variant, inframe_deletion, inframe_insertion, initiator_codon_variant, missense_variant, splice_acceptor_variant, splice_donor_variant, splice_region_variant, stop_gained, stop_lost, coding_sequence_variant, transcript_ablation Synonymous codon variants - synonymous_variant, stop_retained_variant Non-coding transcript or Untranslated Region (UTR) variants - 5_prime_UTR_variant, 3_prime_UTR_variant, complex_transcript_variant, non_coding_transcript_exon_variant Intergenic and intronic variants - upstream_gene_variant, downstream_gene_variant, intron_variant, intergenic_variant, NMD_transcript_variant, no_sequence_alteration Sequence Ontology (&quot;SO:&quot;) terms were converted to the variant classes, then the files were converted to BED, and then bigBed format. No functional annotations were provided by the EVA (e.g., missense, nonsense, etc). These were computed using UCSC's Variant Annotation Integrator (Hinrichs, et al., 2016). Amino-acid substitutions for missense variants are based on RefSeq alignments of mRNA transcripts, which do not always match the amino acids predicted from translating the genomic sequence. Therefore, in some instances, the variant and the genomic nucleotide and associated amino acid may be reversed. E.g., a Pro > Arg change from the perspective of the mRNA would be Arg > Pro from the persepective the genomic sequence. For complete documentation of the processing of these tracks, read the EVA Release 3 MakeDoc. Data Access Note: It is not recommeneded to use LiftOver to convert SNPs between assemblies, and more information about how to convert SNPs between assemblies can be found on the following FAQ entry. The data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. For automated download and analysis, this annotation is stored in a bigBed file that can be downloaded from our download server. The file for this track is called evaSnp.bb. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g. bigBedToBed https://hgdownload.soe.ucsc.edu/gbdb/dm6/bbi/evaSnp.bb -chrom=chr21 -start=0 -end=100000000 stdout Credits This track was produced from the European Variation Archive release 3 data. Consequences were predicted using UCSC's Variant Annotation Integrator and NCBI's RefSeq gene models. References Cezard T, Cunningham F, Hunt SE, Koylass B, Kumar N, Saunders G, Shen A, Silva AF, Tsukanov K, Venkataraman S et al. The European Variation Archive: a FAIR resource of genomic variation for all species. Nucleic Acids Res. 2021 Oct 28:gkab960. doi:10.1093/nar/gkab960. Epub ahead of print. PMID: 34718739. PMID: PMC8728205. Hinrichs AS, Raney BJ, Speir ML, Rhead B, Casper J, Karolchik D, Kuhn RM, Rosenbloom KR, Zweig AS, Haussler D, Kent WJ. UCSC Data Integrator and Variant Annotation Integrator. Bioinformatics. 2016 May 1;32(9):1430-2. PMID: 26740527; PMC: PMC4848401 
evaSnp4 EVA SNP Release 4 Short Genetic Variants from European Variant Archive Release 4 Variation and Repeats Description This track contains mappings of single nucleotide variants and small insertions and deletions (indels) from the European Variation Archive (EVA) Release 4 for the D. melanogaster dm6 genome. The dbSNP database at NCBI no longer hosts non-human variants. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP variant corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. The display is set to automatically collapse to dense visibility when there are more than 100k variants in the window. When the window size is more than 250k bp, the display is switched to density graph mode. Searching, details, and filtering Navigation to an individual variant can be accomplished by typing or copying the variant identifier (rsID) or the genomic coordinates into the Position/Search box on the Browser. A click on an item in the graphical display displays a page with data about that variant. Data fields include the Reference and Alternate Alleles, the class of the variant as reported by EVA, the source of the data, the amino acid change, if any, and the functional class as determined by UCSC's Variant Annotation Integrator. Variants can be filtered using the track controls to show subsets of the data by either EVA Sequence Ontology (SO) term, UCSC-generated functional effect, or by color, which bins the UCSC functional effects into general classes. Mouse-over Mousing over an item shows the ucscClass, which is the consequence according to the Variant Annotation Integrator, and the aaChange when one is available, which is the change in amino acid in HGVS.p terms. Items may have multiple ucscClasses, which will all be shown in the mouse-over in a comma-separated list. Likewise, multiple HGVS.p terms may be shown for each rsID separated by spaces describing all possible AA changes. Multiple items may appear due to different variant predictions on multiple gene transcripts. For all organisms the gene models used were the NCBI RefSeq curated when available, if not then ensembl genes, or finally UCSC mappings of RefSeq if neither of the previous models was possible. Track colors Variants are colored according to the most potentially deleterious functional effect prediction according to the Variant Annotation Integrator. Specific bins can be seen in the Methods section below. Color Variant Type Protein-altering variants and splice site variants Synonymous codon variants Non-coding transcript or Untranslated Region (UTR) variants Intergenic and intronic variants Sequence ontology (SO) Variants are classified by EVA into one of the following sequence ontology terms: substitution &mdash; A single nucleotide in the reference is replaced by another, alternate allele deletion &mdash; One or more nucleotides is deleted. The representation in the database is to display one additional nucleotide in both the Reference field (Ref) and the Alternate Allele field (Alt). E.g. a variant that is a deletion of an A maybe be represented as Ref = GA and Alt = G. insertion &mdash; One or more nucleotides is inserted. The representation in the database is to display one additional nucleotide in both the Reference field (Ref) and the Alternate Allele field (Alt). E.g. a variant that is an insertion of a T maybe be represented as Ref = G and Alt = GT delins &mdash; Similar to tandemRepeat, in that the runs of Ref and Alt Alleles are of different length, except that there is more than one type of nucleotide, e.g., Ref = CCAAAAACAAAAACA, Alt = ACAAAAAC. multipleNucleotideVariant &mdash; More than one nucleotide is substituted by an equal number of different nucleotides, e.g., Ref = AA, Alt = GC. sequence alteration &mdash; A parent term meant to signify a deviation from another sequence. Can be assigned to variants that have not been characterized yet. Methods Data were downloaded from the European Variation Archive EVA release 4 (2022-11-21) current_ids.vcf.gz files corresponding to the proper assembly. Chromosome names were converted to UCSC-style and the variants passed through the Variant Annotation Integrator to predict consequence. For every organism the NCBI RefSeq curated models were used when available, followed by ensembl genes, and finally UCSC mapping of RefSeq when neither of the previous models were possible. Variants were then colored according to their predicted consequence in the following fashion: Protein-altering variants and splice site variants - exon_loss_variant, frameshift_variant, inframe_deletion, inframe_insertion, initiator_codon_variant, missense_variant, splice_acceptor_variant, splice_donor_variant, splice_region_variant, stop_gained, stop_lost, coding_sequence_variant, transcript_ablation Synonymous codon variants - synonymous_variant, stop_retained_variant Non-coding transcript or Untranslated Region (UTR) variants - 5_prime_UTR_variant, 3_prime_UTR_variant, complex_transcript_variant, non_coding_transcript_exon_variant Intergenic and intronic variants - upstream_gene_variant, downstream_gene_variant, intron_variant, intergenic_variant, NMD_transcript_variant, no_sequence_alteration Sequence Ontology (&quot;SO:&quot;) terms were converted to the variant classes, then the files were converted to BED, and then bigBed format. No functional annotations were provided by the EVA (e.g., missense, nonsense, etc). These were computed using UCSC's Variant Annotation Integrator (Hinrichs, et al., 2016). Amino-acid substitutions for missense variants are based on RefSeq alignments of mRNA transcripts, which do not always match the amino acids predicted from translating the genomic sequence. Therefore, in some instances, the variant and the genomic nucleotide and associated amino acid may be reversed. E.g., a Pro &gt; Arg change from the perspective of the mRNA would be Arg &gt; Pro from the persepective the genomic sequence. Also, in bosTau9, galGal5, rheMac8, danRer10 and danRer11 the mitochondrial sequence was removed or renamed to match UCSC. For complete documentation of the processing of these tracks, read the EVA Release 4 MakeDoc. Data Access Note: It is not recommeneded to use LiftOver to convert SNPs between assemblies, and more information about how to convert SNPs between assemblies can be found on the following FAQ entry. The data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. For automated download and analysis, this annotation is stored in a bigBed file that can be downloaded from our download server. The file for this track is called evaSnp4.bb. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g. bigBedToBed https://hgdownload.soe.ucsc.edu/gbdb/dm6/bbi/evaSnp4.bb -chrom=chr21 -start=0 -end=100000000 stdout Credits This track was produced from the European Variation Archive release 4 data. Consequences were predicted using UCSC's Variant Annotation Integrator and NCBI's RefSeq as well as ensembl gene models. References Cezard T, Cunningham F, Hunt SE, Koylass B, Kumar N, Saunders G, Shen A, Silva AF, Tsukanov K, Venkataraman S et al. The European Variation Archive: a FAIR resource of genomic variation for all species. Nucleic Acids Res. 2021 Oct 28:gkab960. doi:10.1093/nar/gkab960. Epub ahead of print. PMID: 34718739. PMID: PMC8728205. Hinrichs AS, Raney BJ, Speir ML, Rhead B, Casper J, Karolchik D, Kuhn RM, Rosenbloom KR, Zweig AS, Haussler D, Kent WJ. UCSC Data Integrator and Variant Annotation Integrator. Bioinformatics. 2016 May 1;32(9):1430-2. PMID: 26740527; PMC: PMC4848401 
evaSnp5 EVA SNP Release 5 Short Genetic Variants from European Variant Archive Release 5 Variation and Repeats Description This track contains mappings of single nucleotide variants and small insertions and deletions (indels) from the European Variation Archive (EVA) Release 5 for the D. melanogaster dm6 genome. The dbSNP database at NCBI no longer hosts non-human variants. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP variant corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. The display is set to automatically collapse to dense visibility when there are more than 100k variants in the window. When the window size is more than 250k bp, the display is switched to density graph mode. Searching, details, and filtering Navigation to an individual variant can be accomplished by typing or copying the variant identifier (rsID) or the genomic coordinates into the Position/Search box on the Browser. A click on an item in the graphical display displays a page with data about that variant. Data fields include the Reference and Alternate Alleles, the class of the variant as reported by EVA, the source of the data, the amino acid change, if any, and the functional class as determined by UCSC's Variant Annotation Integrator. Variants can be filtered using the track controls to show subsets of the data by either EVA Sequence Ontology (SO) term, UCSC-generated functional effect, or by color, which bins the UCSC functional effects into general classes. Mouse-over Mousing over an item shows the ucscClass, which is the consequence according to the Variant Annotation Integrator, and the aaChange when one is available, which is the change in amino acid in HGVS.p terms. Items may have multiple ucscClasses, which will all be shown in the mouse-over in a comma-separated list. Likewise, multiple HGVS.p terms may be shown for each rsID separated by spaces describing all possible AA changes. Multiple items may appear due to different variant predictions on multiple gene transcripts. For all organisms the gene models used were the NCBI RefSeq curated when available, if not then ensembl genes, or finally UCSC mappings of RefSeq if neither of the previous models was possible. Track colors Variants are colored according to the most potentially deleterious functional effect prediction according to the Variant Annotation Integrator. Specific bins can be seen in the Methods section below. Color Variant Type Protein-altering variants and splice site variants Synonymous codon variants Non-coding transcript or Untranslated Region (UTR) variants Intergenic and intronic variants Sequence ontology (SO) Variants are classified by EVA into one of the following sequence ontology terms: substitution &mdash; A single nucleotide in the reference is replaced by another, alternate allele deletion &mdash; One or more nucleotides is deleted. The representation in the database is to display one additional nucleotide in both the Reference field (Ref) and the Alternate Allele field (Alt). E.g. a variant that is a deletion of an A maybe be represented as Ref = GA and Alt = G. insertion &mdash; One or more nucleotides is inserted. The representation in the database is to display one additional nucleotide in both the Reference field (Ref) and the Alternate Allele field (Alt). E.g. a variant that is an insertion of a T maybe be represented as Ref = G and Alt = GT delins &mdash; Similar to tandemRepeat, in that the runs of Ref and Alt Alleles are of different length, except that there is more than one type of nucleotide, e.g., Ref = CCAAAAACAAAAACA, Alt = ACAAAAAC. multipleNucleotideVariant &mdash; More than one nucleotide is substituted by an equal number of different nucleotides, e.g., Ref = AA, Alt = GC. sequence alteration &mdash; A parent term meant to signify a deviation from another sequence. Can be assigned to variants that have not been characterized yet. Methods Data were downloaded from the European Variation Archive EVA release 5 (2023-9-7) current_ids.vcf.gz files corresponding to the proper assembly. Chromosome names were converted to UCSC-style and the variants passed through the Variant Annotation Integrator to predict consequence. For every organism the NCBI RefSeq curated models were used when available, followed by ensembl genes, and finally UCSC mapping of RefSeq when neither of the previous models were possible. Variants were then colored according to their predicted consequence in the following fashion: Protein-altering variants and splice site variants - exon_loss_variant, frameshift_variant, inframe_deletion, inframe_insertion, initiator_codon_variant, missense_variant, splice_acceptor_variant, splice_donor_variant, splice_region_variant, stop_gained, stop_lost, coding_sequence_variant, transcript_ablation Synonymous codon variants - synonymous_variant, stop_retained_variant Non-coding transcript or Untranslated Region (UTR) variants - 5_prime_UTR_variant, 3_prime_UTR_variant, complex_transcript_variant, non_coding_transcript_exon_variant Intergenic and intronic variants - upstream_gene_variant, downstream_gene_variant, intron_variant, intergenic_variant, NMD_transcript_variant, no_sequence_alteration Sequence Ontology (&quot;SO:&quot;) terms were converted to the variant classes, then the files were converted to BED, and then bigBed format. No functional annotations were provided by the EVA (e.g., missense, nonsense, etc). These were computed using UCSC's Variant Annotation Integrator (Hinrichs, et al., 2016). Amino-acid substitutions for missense variants are based on RefSeq alignments of mRNA transcripts, which do not always match the amino acids predicted from translating the genomic sequence. Therefore, in some instances, the variant and the genomic nucleotide and associated amino acid may be reversed. E.g., a Pro &gt; Arg change from the perspective of the mRNA would be Arg &gt; Pro from the persepective the genomic sequence. Also, in bosTau9, galGal5, rheMac8, danRer10 and danRer11 the mitochondrial sequence was removed or renamed to match UCSC. For complete documentation of the processing of these tracks, read the EVA Release 5 MakeDoc. Data Access Note: It is not recommeneded to use LiftOver to convert SNPs between assemblies, and more information about how to convert SNPs between assemblies can be found on the following FAQ entry. The data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. For automated download and analysis, this annotation is stored in a bigBed file that can be downloaded from our download server. The file for this track is called evaSnp5.bb. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g. bigBedToBed https://hgdownload.soe.ucsc.edu/gbdb/dm6/bbi/evaSnp5.bb -chrom=chr21 -start=0 -end=100000000 stdout Credits This track was produced from the European Variation Archive release 5 data. Consequences were predicted using UCSC's Variant Annotation Integrator and NCBI's RefSeq as well as ensembl gene models. References Cezard T, Cunningham F, Hunt SE, Koylass B, Kumar N, Saunders G, Shen A, Silva AF, Tsukanov K, Venkataraman S et al. The European Variation Archive: a FAIR resource of genomic variation for all species. Nucleic Acids Res. 2021 Oct 28:gkab960. doi:10.1093/nar/gkab960. Epub ahead of print. PMID: 34718739. PMID: PMC8728205. Hinrichs AS, Raney BJ, Speir ML, Rhead B, Casper J, Karolchik D, Kuhn RM, Rosenbloom KR, Zweig AS, Haussler D, Kent WJ. UCSC Data Integrator and Variant Annotation Integrator. Bioinformatics. 2016 May 1;32(9):1430-2. PMID: 26740527; PMC: PMC4848401 
gap Gap Gap Locations Mapping and Sequencing Description This track shows the gaps in the Aug. 2014 D. melanogaster genome assembly. Genome assembly procedures are covered in the NCBI assembly documentation. NCBI also provides specific information about this assembly. The definition of the gaps in this assembly is from the AGP file delivered with the sequence. The NCBI document AGP Specification describes the format of the AGP file. Gaps are represented as black boxes in this track. If the relative order and orientation of the contigs on either side of the gap is supported by read pair data, it is a bridged gap and a white line is drawn through the black box representing the gap. This assembly contains the following principal types of gaps: other - gaps added at UCSC to annotate strings of Ns that were not marked in the AGP file (count: 572; size range: 10 - 53,860 bases) 
gc5BaseBw GC Percent GC Percent in 5-Base Windows Mapping and Sequencing Description The GC percent track shows the percentage of G (guanine) and C (cytosine) bases in 5-base windows. High GC content is typically associated with gene-rich areas. This track may be configured in a variety of ways to highlight different apsects of the displayed information. Click the &quot;Graph configuration help&quot; link for an explanation of the configuration options. Credits The data and presentation of this graph were prepared by Hiram Clawson. 
genscan Genscan Genes Genscan Gene Predictions Genes and Gene Predictions Description This track shows predictions from the Genscan program written by Chris Burge. The predictions are based on transcriptional, translational and donor/acceptor splicing signals as well as the length and compositional distributions of exons, introns and intergenic regions. For more information on the different gene tracks, see our Genes FAQ. Display Conventions and Configuration This track follows the display conventions for gene prediction tracks. The track description page offers the following filter and configuration options: Color track by codons: Select the genomic codons option to color and label each codon in a zoomed-in display to facilitate validation and comparison of gene predictions. Go to the Coloring Gene Predictions and Annotations by Codon page for more information about this feature. Methods For a description of the Genscan program and the model that underlies it, refer to Burge and Karlin (1997) in the References section below. The splice site models used are described in more detail in Burge (1998) below. Credits Thanks to Chris Burge for providing the Genscan program. References Burge C. Modeling Dependencies in Pre-mRNA Splicing Signals. In: Salzberg S, Searls D, Kasif S, editors. Computational Methods in Molecular Biology. Amsterdam: Elsevier Science; 1998. p. 127-163. Burge C, Karlin S. Prediction of complete gene structures in human genomic DNA. J. Mol. Biol. 1997 Apr 25;268(1):78-94. PMID: 9149143 
ucscToINSDC INSDC Accession at INSDC - International Nucleotide Sequence Database Collaboration Mapping and Sequencing Description This track associates UCSC Genome Browser chromosome names to accession names from the International Nucleotide Sequence Database Collaboration (INSDC). The data were downloaded from the NCBI assembly database. Credits The data for this track was prepared by Hiram Clawson. 
insectsChainNet Insects  Chain/Net Insects  Chain and Net Alignments Comparative Genomics Description This track shows regions of the genome that are alignable to other genomes ("chain" subtracks) or in synteny ("net" subtracks). The alignable parts are shown with thick blocks that look like exons. Non-alignable parts between these are shown like introns. Chain Track The chain track shows alignments of a query genome sequence to the D. melanogaster genome using a gap scoring system that allows longer gaps than traditional affine gap scoring systems. It can also tolerate gaps in both the query sequence and D. melanogaster simultaneously. These &quot;double-sided&quot; gaps can be caused by local inversions and overlapping deletions in both species. The chain track displays boxes joined together by either single or double lines. The boxes represent aligning regions. Single lines indicate gaps that are largely due to a deletion in the the query sequence assembly or an insertion in the D. melanogaster assembly. Double lines represent more complex gaps that involve substantial sequence in both species. This may result from inversions, overlapping deletions, an abundance of local mutation, or an unsequenced gap in one species. In cases where multiple chains align over a particular region of the D. melanogaster genome, the chains with single-lined gaps are often due to processed pseudogenes, while chains with double-lined gaps are more often due to paralogs and unprocessed pseudogenes. In the &quot;pack&quot; and &quot;full&quot; display modes, the individual feature names indicate the chromosome, strand, and location (in thousands) of the match for each matching alignment. Net Track The net track shows the best query sequence/D. melanogaster chain for every part of the D. melanogaster genome. It is useful for finding syntenic regions, possibly orthologs, and for studying genome rearrangement. Display Conventions and Configuration Chain Track By default, the chains to chromosome-based assemblies are colored based on which chromosome they map to in the aligning organism. To turn off the coloring, check the &quot;off&quot; button next to: Color track based on chromosome. To display only the chains of one chromosome in the aligning organism, enter the name of that chromosome (e.g. chr4) in box next to: Filter by chromosome. Net Track In full display mode, the top-level (level 1) chains are the largest, highest-scoring chains that span this region. In many cases gaps exist in the top-level chain. When possible, these are filled in by other chains that are displayed at level 2. The gaps in level 2 chains may be filled by level 3 chains and so forth. In the graphical display, the boxes represent ungapped alignments; the lines represent gaps. Click on a box to view detailed information about the chain as a whole; click on a line to display information about the gap. The detailed information is useful in determining the cause of the gap or, for lower level chains, the genomic rearrangement. Individual items in the display are categorized as one of four types (other than gap): Top - the best, longest match. Displayed on level 1. Syn - line-ups on the same chromosome as the gap in the level above it. Inv - a line-up on the same chromosome as the gap above it, but in the opposite orientation. NonSyn - a match to a chromosome different from the gap in the level above. Methods Chain track Transposons that have been inserted since the query sequence/D. melanogaster split were removed from the assemblies. The abbreviated genomes were aligned with lastz, and the transposons were added back in. The resulting alignments were converted into axt format using the lavToAxt program. The axt alignments were fed into axtChain, which organizes all alignments between a single query sequence chromosome and a single D. melanogaster chromosome into a group and creates a kd-tree out of the gapless subsections (blocks) of the alignments. A dynamic program was then run over the kd-trees to find the maximally scoring chains of these blocks. Chains scoring below a minimum score of &quot;5000&quot; were discarded; the remaining chains are displayed in this track. The linear gap matrix used with axtChain: -linearGap=loose tablesize 11 smallSize 111 position 1 2 3 11 111 2111 12111 32111 72111 152111 252111 qGap 325 360 400 450 600 1100 3600 7600 15600 31600 56600 tGap 325 360 400 450 600 1100 3600 7600 15600 31600 56600 bothGap 625 660 700 750 900 1400 4000 8000 16000 32000 57000 Net track Chains were derived from lastz alignments, using the methods described on the chain tracks description pages, and sorted with the highest-scoring chains in the genome ranked first. The program chainNet was then used to place the chains one at a time, trimming them as necessary to fit into sections not already covered by a higher-scoring chain. During this process, a natural hierarchy emerged in which a chain that filled a gap in a higher-scoring chain was placed underneath that chain. The program netSyntenic was used to fill in information about the relationship between higher- and lower-level chains, such as whether a lower-level chain was syntenic or inverted relative to the higher-level chain. The program netClass was then used to fill in how much of the gaps and chains contained Ns (sequencing gaps) in one or both species and how much was filled with transposons inserted before and after the two organisms diverged. Credits Lastz (previously known as blastz) was developed at Pennsylvania State University by Minmei Hou, Scott Schwartz, Zheng Zhang, and Webb Miller with advice from Ross Hardison. Lineage-specific repeats were identified by Arian Smit and his RepeatMasker program. The axtChain program was developed at the University of California at Santa Cruz by Jim Kent with advice from Webb Miller and David Haussler. The browser display and database storage of the chains and nets were created by Robert Baertsch and Jim Kent. The chainNet, netSyntenic, and netClass programs were developed at the University of California Santa Cruz by Jim Kent. References Chiaromonte F, Yap VB, Miller W. Scoring pairwise genomic sequence alignments. Pac Symp Biocomput. 2002:115-26. PMID: 11928468 Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D. Evolution's cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11484-9. PMID: 14500911; PMC: PMC208784 Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D, Miller W. Human-mouse alignments with BLASTZ. Genome Res. 2003 Jan;13(1):103-7. PMID: 12529312; PMC: PMC430961 
insectsChainNetViewnet Nets Insects  Chain and Net Alignments Comparative Genomics 
netApiMel4 apiMel4 Net A. mellifera (04 Nov 2010 (Amel_4.5/apiMel4)) Alignment Net Comparative Genomics 
netTriCas2 triCas2 Net T. castaneum (Sep. 2005 (Baylor 2.0/triCas2)) Alignment Net Comparative Genomics 
netAnoGam1 A. gambiae Net A. gambiae (Feb. 2003 (IAGEC MOZ2/anoGam1)) Alignment Net Comparative Genomics Description This track shows the best A. gambiae/D. melanogaster chain for every part of the D. melanogaster genome. It is useful for finding orthologous regions and for studying genome rearrangement. The A. gambiae sequence used in this annotation is from the Feb. 2003 (IAGEC MOZ2/anoGam1) (anoGam1) assembly. Display Conventions and Configuration In full display mode, the top-level (level 1) chains are the largest, highest-scoring chains that span this region. In many cases gaps exist in the top-level chain. When possible, these are filled in by other chains that are displayed at level 2. The gaps in level 2 chains may be filled by level 3 chains and so forth. In the graphical display, the boxes represent ungapped alignments; the lines represent gaps. Click on a box to view detailed information about the chain as a whole; click on a line to display information about the gap. The detailed information is useful in determining the cause of the gap or, for lower level chains, the genomic rearrangement. Individual items in the display are categorized as one of four types (other than gap): Top - the best, longest match. Displayed on level 1. Syn - line-ups on the same chromosome as the gap in the level above it. Inv - a line-up on the same chromosome as the gap above it, but in the opposite orientation. NonSyn - a match to a chromosome different from the gap in the level above. Methods Chains were derived from blastz alignments, using the methods described on the chain tracks description pages, and sorted with the highest-scoring chains in the genome ranked first. The program chainNet was then used to place the chains one at a time, trimming them as necessary to fit into sections not already covered by a higher-scoring chain. During this process, a natural hierarchy emerged in which a chain that filled a gap in a higher-scoring chain was placed underneath that chain. The program netSyntenic was used to fill in information about the relationship between higher- and lower-level chains, such as whether a lower-level chain was syntenic or inverted relative to the higher-level chain. The program netClass was then used to fill in how much of the gaps and chains contained Ns (sequencing gaps) in one or both species and how much was filled with transposons inserted before and after the two organisms diverged. Credits The chainNet, netSyntenic, and netClass programs were developed at the University of California Santa Cruz by Jim Kent. Blastz was developed at Pennsylvania State University by Minmei Hou, Scott Schwartz, Zheng Zhang, and Webb Miller with advice from Ross Hardison. Lineage-specific repeats were identified by Arian Smit and his program RepeatMasker. The browser display and database storage of the nets were made by Robert Baertsch and Jim Kent. References Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D. Evolution's cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11484-9. PMID: 14500911; PMC: PMC208784 Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D, Miller W. Human-mouse alignments with BLASTZ. Genome Res. 2003 Jan;13(1):103-7. PMID: 12529312; PMC: PMC430961 
netAnoGam3 A. gambiae Net A. gambiae (Oct. 2006 (AgamP3/anoGam3)) Alignment Net Comparative Genomics 
netMusDom2 musDom2 Net M. domestica (22 Apr 2013 (Musca_domestica-2.0.2/musDom2)) Alignment Net Comparative Genomics 
netDroAlb1 droAlb1 Net D. albomicans (21 May 2012 (DroAlb_1.0/droAlb1)) Alignment Net Comparative Genomics 
netDroMoj3 droMoj3 Net D. mojavensis (Feb. 2006 (Agencourt CAF1/droMoj3)) Alignment Net Comparative Genomics 
netDroGri2 droGri2 Net D. grimshawi (Feb. 2006 (Agencourt CAF1/droGri2)) Alignment Net Comparative Genomics 
netDroWil2 droWil2 Net D. willistoni (03 Aug 2006 (dwil_caf1/droWil2)) Alignment Net Comparative Genomics 
netDroVir3 droVir3 Net D. virilis (Feb. 2006 (Agencourt CAF1/droVir3)) Alignment Net Comparative Genomics 
netDroPer1 D. persimilis Net D. persimilis (Oct. 2005 (Broad/droPer1)) Alignment Net Comparative Genomics Description This track shows the best D. persimilis/D. melanogaster chain for every part of the D. melanogaster genome. It is useful for finding orthologous regions and for studying genome rearrangement. The D. persimilis sequence used in this annotation is from the Oct. 2005 (Broad/droPer1) (droPer1) assembly. Display Conventions and Configuration In full display mode, the top-level (level 1) chains are the largest, highest-scoring chains that span this region. In many cases gaps exist in the top-level chain. When possible, these are filled in by other chains that are displayed at level 2. The gaps in level 2 chains may be filled by level 3 chains and so forth. In the graphical display, the boxes represent ungapped alignments; the lines represent gaps. Click on a box to view detailed information about the chain as a whole; click on a line to display information about the gap. The detailed information is useful in determining the cause of the gap or, for lower level chains, the genomic rearrangement. Individual items in the display are categorized as one of four types (other than gap): Top - the best, longest match. Displayed on level 1. Syn - line-ups on the same chromosome as the gap in the level above it. Inv - a line-up on the same chromosome as the gap above it, but in the opposite orientation. NonSyn - a match to a chromosome different from the gap in the level above. Methods Chains were derived from blastz alignments, using the methods described on the chain tracks description pages, and sorted with the highest-scoring chains in the genome ranked first. The program chainNet was then used to place the chains one at a time, trimming them as necessary to fit into sections not already covered by a higher-scoring chain. During this process, a natural hierarchy emerged in which a chain that filled a gap in a higher-scoring chain was placed underneath that chain. The program netSyntenic was used to fill in information about the relationship between higher- and lower-level chains, such as whether a lower-level chain was syntenic or inverted relative to the higher-level chain. The program netClass was then used to fill in how much of the gaps and chains contained Ns (sequencing gaps) in one or both species and how much was filled with transposons inserted before and after the two organisms diverged. Credits The chainNet, netSyntenic, and netClass programs were developed at the University of California Santa Cruz by Jim Kent. Blastz was developed at Pennsylvania State University by Minmei Hou, Scott Schwartz, Zheng Zhang, and Webb Miller with advice from Ross Hardison. Lineage-specific repeats were identified by Arian Smit and his program RepeatMasker. The browser display and database storage of the nets were made by Robert Baertsch and Jim Kent. References Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D. Evolution's cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11484-9. PMID: 14500911; PMC: PMC208784 Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D, Miller W. Human-mouse alignments with BLASTZ. Genome Res. 2003 Jan;13(1):103-7. PMID: 12529312; PMC: PMC430961 
netDroMir2 droMir2 Net D. miranda (19 Apr 2013 (DroMir_2.2/droMir2)) Alignment Net Comparative Genomics 
netDroPse3 droPse3 Net D. pseudoobscura (11 Apr 2013 (Dpse_3.0/droPse3)) Alignment Net Comparative Genomics 
netDroBip2 droBip2 Net D. bipectinata (04 Mar 2013 (Dbip_2.0/droBip2)) Alignment Net Comparative Genomics 
netDroAna3 droAna3 Net D. ananassae (Feb. 2006 (Agencourt CAF1/droAna3)) Alignment Net Comparative Genomics 
netDroKik2 droKik2 Net D. kikkawai (04 Mar 2013 (Dkik_2.0/droKik2)) Alignment Net Comparative Genomics 
netDroSuz1 droSuz1 Net D. suzukii (30 Sep 2013 (Dsuzukii.v01/droSuz1)) Alignment Net Comparative Genomics 
netDroFic2 droFic2 Net D. ficusphila (04 Mar 2013 (Dfic_2.0/droFic2)) Alignment Net Comparative Genomics 
netDroRho2 droRho2 Net D. rhopaloa (22 Feb 2013 (Drho_2.0/droRho2)) Alignment Net Comparative Genomics 
netDroBia2 droBia2 Net D. biarmipes (04 Mar 2013 (Dbia_2.0/droBia2)) Alignment Net Comparative Genomics 
netDroEug2 droEug2 Net D. eugracilis (04 Mar 2013 (Deug_2.0/droEug2)) Alignment Net Comparative Genomics 
netDroEle2 droEle2 Net D. elegans (04 Mar 2013 (Dele_2.0/droEle2)) Alignment Net Comparative Genomics 
netDroTak2 droTak2 Net D. takahashii (04 Mar 2013 (Dtak_2.0/droTak2)) Alignment Net Comparative Genomics 
netDroEre2 droEre2 Net D. erecta (Feb. 2006 (Agencourt CAF1/droEre2)) Alignment Net Comparative Genomics 
netDroYak3 droYak3 Net D. yakuba (27 Jun 2006 (dyak_caf1/droYak3)) Alignment Net Comparative Genomics 
netDroSec1 D. sechellia Net D. sechellia (Oct. 2005 (Broad/droSec1)) Alignment Net Comparative Genomics Description This track shows the best D. sechellia/D. melanogaster chain for every part of the D. melanogaster genome. It is useful for finding orthologous regions and for studying genome rearrangement. The D. sechellia sequence used in this annotation is from the Oct. 2005 (Broad/droSec1) (droSec1) assembly. Display Conventions and Configuration In full display mode, the top-level (level 1) chains are the largest, highest-scoring chains that span this region. In many cases gaps exist in the top-level chain. When possible, these are filled in by other chains that are displayed at level 2. The gaps in level 2 chains may be filled by level 3 chains and so forth. In the graphical display, the boxes represent ungapped alignments; the lines represent gaps. Click on a box to view detailed information about the chain as a whole; click on a line to display information about the gap. The detailed information is useful in determining the cause of the gap or, for lower level chains, the genomic rearrangement. Individual items in the display are categorized as one of four types (other than gap): Top - the best, longest match. Displayed on level 1. Syn - line-ups on the same chromosome as the gap in the level above it. Inv - a line-up on the same chromosome as the gap above it, but in the opposite orientation. NonSyn - a match to a chromosome different from the gap in the level above. Methods Chains were derived from blastz alignments, using the methods described on the chain tracks description pages, and sorted with the highest-scoring chains in the genome ranked first. The program chainNet was then used to place the chains one at a time, trimming them as necessary to fit into sections not already covered by a higher-scoring chain. During this process, a natural hierarchy emerged in which a chain that filled a gap in a higher-scoring chain was placed underneath that chain. The program netSyntenic was used to fill in information about the relationship between higher- and lower-level chains, such as whether a lower-level chain was syntenic or inverted relative to the higher-level chain. The program netClass was then used to fill in how much of the gaps and chains contained Ns (sequencing gaps) in one or both species and how much was filled with transposons inserted before and after the two organisms diverged. Credits The chainNet, netSyntenic, and netClass programs were developed at the University of California Santa Cruz by Jim Kent. Blastz was developed at Pennsylvania State University by Minmei Hou, Scott Schwartz, Zheng Zhang, and Webb Miller with advice from Ross Hardison. Lineage-specific repeats were identified by Arian Smit and his program RepeatMasker. The browser display and database storage of the nets were made by Robert Baertsch and Jim Kent. References Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D. Evolution's cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11484-9. PMID: 14500911; PMC: PMC208784 Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D, Miller W. Human-mouse alignments with BLASTZ. Genome Res. 2003 Jan;13(1):103-7. PMID: 12529312; PMC: PMC430961 
netDroSim1 droSim1 Net D. simulans (Apr. 2005 (WUGSC mosaic 1.0/droSim1)) Alignment Net Comparative Genomics Description This track shows the best D. simulans/D. melanogaster chain for every part of the D. melanogaster genome. It is useful for finding orthologous regions and for studying genome rearrangement. The D. simulans sequence used in this annotation is from the Apr. 2005 (WUGSC mosaic 1.0/droSim1) (droSim1) assembly. Display Conventions and Configuration In full display mode, the top-level (level 1) chains are the largest, highest-scoring chains that span this region. In many cases gaps exist in the top-level chain. When possible, these are filled in by other chains that are displayed at level 2. The gaps in level 2 chains may be filled by level 3 chains and so forth. In the graphical display, the boxes represent ungapped alignments; the lines represent gaps. Click on a box to view detailed information about the chain as a whole; click on a line to display information about the gap. The detailed information is useful in determining the cause of the gap or, for lower level chains, the genomic rearrangement. Individual items in the display are categorized as one of four types (other than gap): Top - the best, longest match. Displayed on level 1. Syn - line-ups on the same chromosome as the gap in the level above it. Inv - a line-up on the same chromosome as the gap above it, but in the opposite orientation. NonSyn - a match to a chromosome different from the gap in the level above. Methods Chains were derived from blastz alignments, using the methods described on the chain tracks description pages, and sorted with the highest-scoring chains in the genome ranked first. The program chainNet was then used to place the chains one at a time, trimming them as necessary to fit into sections not already covered by a higher-scoring chain. During this process, a natural hierarchy emerged in which a chain that filled a gap in a higher-scoring chain was placed underneath that chain. The program netSyntenic was used to fill in information about the relationship between higher- and lower-level chains, such as whether a lower-level chain was syntenic or inverted relative to the higher-level chain. The program netClass was then used to fill in how much of the gaps and chains contained Ns (sequencing gaps) in one or both species and how much was filled with transposons inserted before and after the two organisms diverged. Credits The chainNet, netSyntenic, and netClass programs were developed at the University of California Santa Cruz by Jim Kent. Blastz was developed at Pennsylvania State University by Minmei Hou, Scott Schwartz, Zheng Zhang, and Webb Miller with advice from Ross Hardison. References Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D. Evolution's cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11484-9. PMID: 14500911; PMC: PMC208784 Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D, Miller W. Human-mouse alignments with BLASTZ. Genome Res. 2003 Jan;13(1):103-7. PMID: 12529312; PMC: PMC430961 
netDroSim2 droSim2 Net D. simulans (Sep. 2014 (ASM75419v2/droSim2)) Alignment Net Comparative Genomics 
insectsChainNetViewchain Chains Insects  Chain and Net Alignments Comparative Genomics 
chainApiMel4 apiMel4 Chain A. mellifera (04 Nov 2010 (Amel_4.5/apiMel4)) Chained Alignments Comparative Genomics 
chainTriCas2 triCas2 Chain T. castaneum (Sep. 2005 (Baylor 2.0/triCas2)) Chained Alignments Comparative Genomics 
chainTrichoceridae_BV_2014 Trichoceridae_BV_2014 Chain Trichoceridae_BV_2014 (Trichoceridae_BV_2014) Chained Alignments Comparative Genomics 
chainTipula_oleracea Tipula_oleracea Chain Tipula_oleracea (Tipula_oleracea) Chained Alignments Comparative Genomics 
chainChaoborus_trivitattus Chaoborus_trivitattus Chain Chaoborus_trivitattus (Chaoborus_trivitattus) Chained Alignments Comparative Genomics 
chainA_nili A_nili Chain A_nili (A_nili) Chained Alignments Comparative Genomics 
chainA_gambiae_1 A_gambiae_1 Chain A_gambiae_1 (A_gambiae_1) Chained Alignments Comparative Genomics 
chainChironomus_riparius Chironomus_riparius Chain Chironomus_riparius (Chironomus_riparius) Chained Alignments Comparative Genomics 
chainChironomus_tentans Chironomus_tentans Chain Chironomus_tentans (Chironomus_tentans) Chained Alignments Comparative Genomics 
chainMayetiola_destructor Mayetiola_destructor Chain Mayetiola_destructor (Mayetiola_destructor) Chained Alignments Comparative Genomics 
chainClunio_marinus Clunio_marinus Chain Clunio_marinus (Clunio_marinus) Chained Alignments Comparative Genomics 
chainA_punctulatus A_punctulatus Chain A_punctulatus (A_punctulatus) Chained Alignments Comparative Genomics 
chainBelgica_antarctica Belgica_antarctica Chain Belgica_antarctica (Belgica_antarctica) Chained Alignments Comparative Genomics 
chainA_koliensis A_koliensis Chain A_koliensis (A_koliensis) Chained Alignments Comparative Genomics 
chainLutzomyia_longipalpis Lutzomyia_longipalpis Chain Lutzomyia_longipalpis (Lutzomyia_longipalpis) Chained Alignments Comparative Genomics 
chainA_farauti_No4 A_farauti_No4 Chain A_farauti_No4 (A_farauti_No4) Chained Alignments Comparative Genomics 
chainA_darlingi A_darlingi Chain A_darlingi (A_darlingi) Chained Alignments Comparative Genomics 
chainMochlonyx_cinctipes Mochlonyx_cinctipes Chain Mochlonyx_cinctipes (Mochlonyx_cinctipes) Chained Alignments Comparative Genomics 
chainA_aquasalis A_aquasalis Chain A_aquasalis (A_aquasalis) Chained Alignments Comparative Genomics 
chainA_cracens A_cracens Chain A_cracens (A_cracens) Chained Alignments Comparative Genomics 
chainA_christyi A_christyi Chain A_christyi (A_christyi) Chained Alignments Comparative Genomics 
chainA_albimanus A_albimanus Chain A_albimanus (A_albimanus) Chained Alignments Comparative Genomics 
chainCulicoides_sonorensis Culicoides_sonorensis Chain Culicoides_sonorensis (Culicoides_sonorensis) Chained Alignments Comparative Genomics 
chainCoboldia_fuscipes Coboldia_fuscipes Chain Coboldia_fuscipes (Coboldia_fuscipes) Chained Alignments Comparative Genomics 
chainA_stephensi A_stephensi Chain A_stephensi (A_stephensi) Chained Alignments Comparative Genomics 
chainA_culicifacies A_culicifacies Chain A_culicifacies (A_culicifacies) Chained Alignments Comparative Genomics 
chainPhlebotomus_papatasi Phlebotomus_papatasi Chain Phlebotomus_papatasi (Phlebotomus_papatasi) Chained Alignments Comparative Genomics 
chainClogmia_albipunctata Clogmia_albipunctata Chain Clogmia_albipunctata (Clogmia_albipunctata) Chained Alignments Comparative Genomics 
chainA_coluzzii A_coluzzii Chain A_coluzzii (A_coluzzii) Chained Alignments Comparative Genomics 
chainA_melas A_melas Chain A_melas (A_melas) Chained Alignments Comparative Genomics 
chainA_funestus A_funestus Chain A_funestus (A_funestus) Chained Alignments Comparative Genomics 
chainA_minimus A_minimus Chain A_minimus (A_minimus) Chained Alignments Comparative Genomics 
chainA_farauti A_farauti Chain A_farauti (A_farauti) Chained Alignments Comparative Genomics 
chainA_quadriannulatus A_quadriannulatus Chain A_quadriannulatus (A_quadriannulatus) Chained Alignments Comparative Genomics 
chainA_epiroticus A_epiroticus Chain A_epiroticus (A_epiroticus) Chained Alignments Comparative Genomics 
chainA_atroparvus A_atroparvus Chain A_atroparvus (A_atroparvus) Chained Alignments Comparative Genomics 
chainA_sinensis A_sinensis Chain A_sinensis (A_sinensis) Chained Alignments Comparative Genomics 
chainA_arabiensis A_arabiensis Chain A_arabiensis (A_arabiensis) Chained Alignments Comparative Genomics 
chainA_dirus A_dirus Chain A_dirus (A_dirus) Chained Alignments Comparative Genomics 
chainA_merus A_merus Chain A_merus (A_merus) Chained Alignments Comparative Genomics 
chainA_maculatus A_maculatus Chain A_maculatus (A_maculatus) Chained Alignments Comparative Genomics 
chainCulex_quinquefasciatus Culex_quinquefasciatus Chain Culex_quinquefasciatus (Culex_quinquefasciatus) Chained Alignments Comparative Genomics 
chainAnoGam1 A. gambiae Chain A. gambiae (Feb. 2003 (IAGEC MOZ2/anoGam1)) Chained Alignments Comparative Genomics Description This track shows alignments of A. gambiae (anoGam1, Feb. 2003 (IAGEC MOZ2/anoGam1)) to the D. melanogaster genome using a gap scoring system that allows longer gaps than traditional affine gap scoring systems. It can also tolerate gaps in both A. gambiae and D. melanogaster simultaneously. These &quot;double-sided&quot; gaps can be caused by local inversions and overlapping deletions in both species. The A. gambiae sequence is from the MOZ2 assembly. The chain track displays boxes joined together by either single or double lines. The boxes represent aligning regions. Single lines indicate gaps that are largely due to a deletion in the A. gambiae assembly or an insertion in the D. melanogaster assembly. Double lines represent more complex gaps that involve substantial sequence in both species. This may result from inversions, overlapping deletions, an abundance of local mutation, or an unsequenced gap in one species. In cases where multiple chains align over a particular region of the D. melanogaster genome, the chains with single-lined gaps are often due to processed pseudogenes, while chains with double-lined gaps are more often due to paralogs and unprocessed pseudogenes. In the "pack" and "full" display modes, the individual feature names indicate the chromosome, strand, and location (in thousands) of the match for each matching alignment. Display Conventions and Configuration By default, the chains to chromosome-based assemblies are colored based on which chromosome they map to in the aligning organism. To turn off the coloring, check the &quot;off&quot; button next to: Color track based on chromosome. To display only the chains of one chromosome in the aligning organism, enter the name of that chromosome (e.g. chr4) in box next to: Filter by chromosome. Methods The A. gambiae/D. melanogaster genomes were aligned with blastz and converted into axt format using the lavToAxt program. The axt alignments were fed into axtChain, which organizes all alignments between a single A. gambiae chromosome and a single D. melanogaster chromosome into a group and creates a kd-tree out of the gapless subsections (blocks) of the alignments. A dynamic program was then run over the kd-trees to find the maximally scoring chains of these blocks. Chains scoring below a threshold were discarded; the remaining chains are displayed in this track. Credits Blastz was developed at Pennsylvania State University by Minmei Hou, Scott Schwartz, Zheng Zhang, and Webb Miller with advice from Ross Hardison. Lineage-specific repeats were identified by Arian Smit and his RepeatMasker program. The axtChain program was developed at the University of California at Santa Cruz by Jim Kent with advice from Webb Miller and David Haussler. The browser display and database storage of the chains were generated by Robert Baertsch and Jim Kent. References Chiaromonte F, Yap VB, Miller W. Scoring pairwise genomic sequence alignments. Pac Symp Biocomput. 2002:115-26. PMID: 11928468 Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D. Evolution's cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11484-9. PMID: 14500911; PMC: PMC208784 Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D, Miller W. Human-mouse alignments with BLASTZ. Genome Res. 2003 Jan;13(1):103-7. PMID: 12529312; PMC: PMC430961 
chainAnoGam3 A. gambiae Chain A. gambiae (Oct. 2006 (AgamP3/anoGam3)) Chained Alignments Comparative Genomics 
chainAedes_aegypti Aedes_aegypti Chain Aedes_aegypti (Aedes_aegypti) Chained Alignments Comparative Genomics 
chainAedes_albopictus Aedes_albopictus Chain Aedes_albopictus (Aedes_albopictus) Chained Alignments Comparative Genomics 
chainTrupanea_jonesi Trupanea_jonesi Chain Trupanea_jonesi (Trupanea_jonesi) Chained Alignments Comparative Genomics 
chainMegaselia_scalaris Megaselia_scalaris Chain Megaselia_scalaris (Megaselia_scalaris) Chained Alignments Comparative Genomics 
chainCondylostylus_patibulatus Condylostylus_patibulatus Chain Condylostylus_patibulatus (Condylostylus_patibulatus) Chained Alignments Comparative Genomics 
chainEristalis_dimidiata Eristalis_dimidiata Chain Eristalis_dimidiata (Eristalis_dimidiata) Chained Alignments Comparative Genomics 
chainLiriomyza_trifolii Liriomyza_trifolii Chain Liriomyza_trifolii (Liriomyza_trifolii) Chained Alignments Comparative Genomics 
chainSarcophagidae_BV_2014 Sarcophagidae_BV_2014 Chain Sarcophagidae_BV_2014 (Sarcophagidae_BV_2014) Chained Alignments Comparative Genomics 
chainHolcocephala_fusca Holcocephala_fusca Chain Holcocephala_fusca (Holcocephala_fusca) Chained Alignments Comparative Genomics 
chainEutreta_diana Eutreta_diana Chain Eutreta_diana (Eutreta_diana) Chained Alignments Comparative Genomics 
chainNeobellieria_bullata Neobellieria_bullata Chain Neobellieria_bullata (Neobellieria_bullata) Chained Alignments Comparative Genomics 
chainHermetia_illucens Hermetia_illucens Chain Hermetia_illucens (Hermetia_illucens) Chained Alignments Comparative Genomics 
chainCirrula_hians Cirrula_hians Chain Cirrula_hians (Cirrula_hians) Chained Alignments Comparative Genomics 
chainTephritis_californica Tephritis_californica Chain Tephritis_californica (Tephritis_californica) Chained Alignments Comparative Genomics 
chainMegaselia_abdita Megaselia_abdita Chain Megaselia_abdita (Megaselia_abdita) Chained Alignments Comparative Genomics 
chainThemira_minor Themira_minor Chain Themira_minor (Themira_minor) Chained Alignments Comparative Genomics 
chainHaematobia_irritans Haematobia_irritans Chain Haematobia_irritans (Haematobia_irritans) Chained Alignments Comparative Genomics 
chainProctacanthus_coquilletti Proctacanthus_coquilletti Chain Proctacanthus_coquilletti (Proctacanthus_coquilletti) Chained Alignments Comparative Genomics 
chainSphyracephala_brevicornis Sphyracephala_brevicornis Chain Sphyracephala_brevicornis (Sphyracephala_brevicornis) Chained Alignments Comparative Genomics 
chainCalliphora_vicina Calliphora_vicina Chain Calliphora_vicina (Calliphora_vicina) Chained Alignments Comparative Genomics 
chainGlossina_morsitans_1 Glossina_morsitans_1 Chain Glossina_morsitans_1 (Glossina_morsitans_1) Chained Alignments Comparative Genomics 
chainLucilia_sericata Lucilia_sericata Chain Lucilia_sericata (Lucilia_sericata) Chained Alignments Comparative Genomics 
chainEphydra_gracilis Ephydra_gracilis Chain Ephydra_gracilis (Ephydra_gracilis) Chained Alignments Comparative Genomics 
chainGlossina_palpalis_gambiensis Glossina_palpalis_gambiensis Chain Glossina_palpalis_gambiensis (Glossina_palpalis_gambiensis) Chained Alignments Comparative Genomics 
chainGlossina_austeni Glossina_austeni Chain Glossina_austeni (Glossina_austeni) Chained Alignments Comparative Genomics 
chainGlossina_morsitans_2 Glossina_morsitans_2 Chain Glossina_morsitans_2 (Glossina_morsitans_2) Chained Alignments Comparative Genomics 
chainGlossina_brevipalpis Glossina_brevipalpis Chain Glossina_brevipalpis (Glossina_brevipalpis) Chained Alignments Comparative Genomics 
chainGlossina_fuscipes Glossina_fuscipes Chain Glossina_fuscipes (Glossina_fuscipes) Chained Alignments Comparative Genomics 
chainGlossina_pallidipes Glossina_pallidipes Chain Glossina_pallidipes (Glossina_pallidipes) Chained Alignments Comparative Genomics 
chainStomoxys_calcitrans Stomoxys_calcitrans Chain Stomoxys_calcitrans (Stomoxys_calcitrans) Chained Alignments Comparative Genomics 
chainBactrocera_dorsalis Bactrocera_dorsalis Chain Bactrocera_dorsalis (Bactrocera_dorsalis) Chained Alignments Comparative Genomics 
chainMusDom2 musDom2 Chain M. domestica (22 Apr 2013 (Musca_domestica-2.0.2/musDom2)) Chained Alignments Comparative Genomics 
chainBactrocera_tryoni Bactrocera_tryoni Chain Bactrocera_tryoni (Bactrocera_tryoni) Chained Alignments Comparative Genomics 
chainPaykullia_maculata Paykullia_maculata Chain Paykullia_maculata (Paykullia_maculata) Chained Alignments Comparative Genomics 
chainCeratitis_capitata Ceratitis_capitata Chain Ceratitis_capitata (Ceratitis_capitata) Chained Alignments Comparative Genomics 
chainPhormia_regina Phormia_regina Chain Phormia_regina (Phormia_regina) Chained Alignments Comparative Genomics 
chainZeugodacus_cucurbitae Zeugodacus_cucurbitae Chain Zeugodacus_cucurbitae (Zeugodacus_cucurbitae) Chained Alignments Comparative Genomics 
chainBactrocera_oleae Bactrocera_oleae Chain Bactrocera_oleae (Bactrocera_oleae) Chained Alignments Comparative Genomics 
chainBactrocera_latifrons Bactrocera_latifrons Chain Bactrocera_latifrons (Bactrocera_latifrons) Chained Alignments Comparative Genomics 
chainLucilia_cuprina Lucilia_cuprina Chain Lucilia_cuprina (Lucilia_cuprina) Chained Alignments Comparative Genomics 
chainRhagoletis_zephyria Rhagoletis_zephyria Chain Rhagoletis_zephyria (Rhagoletis_zephyria) Chained Alignments Comparative Genomics 
chainTeleopsis_dalmanni Teleopsis_dalmanni Chain Teleopsis_dalmanni (Teleopsis_dalmanni) Chained Alignments Comparative Genomics 
chainPhortica_variegata Phortica_variegata Chain Phortica_variegata (Phortica_variegata) Chained Alignments Comparative Genomics 
chainD_navojoa D_navojoa Chain D_navojoa (D_navojoa) Chained Alignments Comparative Genomics 
chainZaprionus_indianus Zaprionus_indianus Chain Zaprionus_indianus (Zaprionus_indianus) Chained Alignments Comparative Genomics 
chainD_nasuta D_nasuta Chain D_nasuta (D_nasuta) Chained Alignments Comparative Genomics 
chainD_arizonae D_arizonae Chain D_arizonae (D_arizonae) Chained Alignments Comparative Genomics 
chainD_busckii D_busckii Chain D_busckii (D_busckii) Chained Alignments Comparative Genomics 
chainScaptodrosophila_lebanonensis Scaptodrosophila_lebanonensis Chain Scaptodrosophila_lebanonensis (Scaptodrosophila_lebanonensis) Chained Alignments Comparative Genomics 
chainDroAlb1 droAlb1 Chain D. albomicans (21 May 2012 (DroAlb_1.0/droAlb1)) Chained Alignments Comparative Genomics 
chainD_montana D_montana Chain D_montana (D_montana) Chained Alignments Comparative Genomics 
chainD_americana D_americana Chain D_americana (D_americana) Chained Alignments Comparative Genomics 
chainD_hydei D_hydei Chain D_hydei (D_hydei) Chained Alignments Comparative Genomics 
chainD_novamexicana D_novamexicana Chain D_novamexicana (D_novamexicana) Chained Alignments Comparative Genomics 
chainD_pseudoobscura_1 D_pseudoobscura_1 Chain D_pseudoobscura_1 (D_pseudoobscura_1) Chained Alignments Comparative Genomics 
chainDroMoj3 droMoj3 Chain D. mojavensis (Feb. 2006 (Agencourt CAF1/droMoj3)) Chained Alignments Comparative Genomics 
chainDroGri2 droGri2 Chain D. grimshawi (Feb. 2006 (Agencourt CAF1/droGri2)) Chained Alignments Comparative Genomics 
chainDroWil2 droWil2 Chain D. willistoni (03 Aug 2006 (dwil_caf1/droWil2)) Chained Alignments Comparative Genomics 
chainDroVir3 droVir3 Chain D. virilis (Feb. 2006 (Agencourt CAF1/droVir3)) Chained Alignments Comparative Genomics 
chainD_athabasca D_athabasca Chain D_athabasca (D_athabasca) Chained Alignments Comparative Genomics 
chainD_subobscura D_subobscura Chain D_subobscura (D_subobscura) Chained Alignments Comparative Genomics 
chainDroPer1 D. persimilis Chain D. persimilis (Oct. 2005 (Broad/droPer1)) Chained Alignments Comparative Genomics Description This track shows alignments of D. persimilis (droPer1, Oct. 2005 (Broad/droPer1)) to the D. melanogaster genome using a gap scoring system that allows longer gaps than traditional affine gap scoring systems. It can also tolerate gaps in both D. persimilis and D. melanogaster simultaneously. These &quot;double-sided&quot; gaps can be caused by local inversions and overlapping deletions in both species. The chain track displays boxes joined together by either single or double lines. The boxes represent aligning regions. Single lines indicate gaps that are largely due to a deletion in the D. persimilis assembly or an insertion in the D. melanogaster assembly. Double lines represent more complex gaps that involve substantial sequence in both species. This may result from inversions, overlapping deletions, an abundance of local mutation, or an unsequenced gap in one species. In cases where multiple chains align over a particular region of the D. melanogaster genome, the chains with single-lined gaps are often due to processed pseudogenes, while chains with double-lined gaps are more often due to paralogs and unprocessed pseudogenes. In the "pack" and "full" display modes, the individual feature names indicate the chromosome, strand, and location (in thousands) of the match for each matching alignment. Display Conventions and Configuration By default, the chains to chromosome-based assemblies are colored based on which chromosome they map to in the aligning organism. To turn off the coloring, check the &quot;off&quot; button next to: Color track based on chromosome. To display only the chains of one chromosome in the aligning organism, enter the name of that chromosome (e.g. chr4) in box next to: Filter by chromosome. Methods The D. persimilis/D. melanogaster genomes were aligned with blastz and converted into axt format using the lavToAxt program. The axt alignments were fed into axtChain, which organizes all alignments between a single D. persimilis chromosome and a single D. melanogaster chromosome into a group and creates a kd-tree out of the gapless subsections (blocks) of the alignments. A dynamic program was then run over the kd-trees to find the maximally scoring chains of these blocks. Chains scoring below a threshold were discarded; the remaining chains are displayed in this track. Credits Blastz was developed at Pennsylvania State University by Minmei Hou, Scott Schwartz, Zheng Zhang, and Webb Miller with advice from Ross Hardison. Lineage-specific repeats were identified by Arian Smit and his RepeatMasker program. The axtChain program was developed at the University of California at Santa Cruz by Jim Kent with advice from Webb Miller and David Haussler. The browser display and database storage of the chains were generated by Robert Baertsch and Jim Kent. References Chiaromonte F, Yap VB, Miller W. Scoring pairwise genomic sequence alignments. Pac Symp Biocomput. 2002:115-26. PMID: 11928468 Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D. Evolution's cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11484-9. PMID: 14500911; PMC: PMC208784 Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D, Miller W. Human-mouse alignments with BLASTZ. Genome Res. 2003 Jan;13(1):103-7. PMID: 12529312; PMC: PMC430961 
chainDroMir2 droMir2 Chain D. miranda (19 Apr 2013 (DroMir_2.2/droMir2)) Chained Alignments Comparative Genomics 
chainDroPse3 droPse3 Chain D. pseudoobscura (11 Apr 2013 (Dpse_3.0/droPse3)) Chained Alignments Comparative Genomics 
chainD_obscura D_obscura Chain D_obscura (D_obscura) Chained Alignments Comparative Genomics 
chainDroBip2 droBip2 Chain D. bipectinata (04 Mar 2013 (Dbip_2.0/droBip2)) Chained Alignments Comparative Genomics 
chainDroAna3 droAna3 Chain D. ananassae (Feb. 2006 (Agencourt CAF1/droAna3)) Chained Alignments Comparative Genomics 
chainD_serrata D_serrata Chain D_serrata (D_serrata) Chained Alignments Comparative Genomics 
chainDroKik2 droKik2 Chain D. kikkawai (04 Mar 2013 (Dkik_2.0/droKik2)) Chained Alignments Comparative Genomics 
chainDroSuz1 droSuz1 Chain D. suzukii (30 Sep 2013 (Dsuzukii.v01/droSuz1)) Chained Alignments Comparative Genomics 
chainDroFic2 droFic2 Chain D. ficusphila (04 Mar 2013 (Dfic_2.0/droFic2)) Chained Alignments Comparative Genomics 
chainDroRho2 droRho2 Chain D. rhopaloa (22 Feb 2013 (Drho_2.0/droRho2)) Chained Alignments Comparative Genomics 
chainDroBia2 droBia2 Chain D. biarmipes (04 Mar 2013 (Dbia_2.0/droBia2)) Chained Alignments Comparative Genomics 
chainDroEug2 droEug2 Chain D. eugracilis (04 Mar 2013 (Deug_2.0/droEug2)) Chained Alignments Comparative Genomics 
chainDroEle2 droEle2 Chain D. elegans (04 Mar 2013 (Dele_2.0/droEle2)) Chained Alignments Comparative Genomics 
chainDroTak2 droTak2 Chain D. takahashii (04 Mar 2013 (Dtak_2.0/droTak2)) Chained Alignments Comparative Genomics 
chainDroEre2 droEre2 Chain D. erecta (Feb. 2006 (Agencourt CAF1/droEre2)) Chained Alignments Comparative Genomics 
chainDroYak3 droYak3 Chain D. yakuba (27 Jun 2006 (dyak_caf1/droYak3)) Chained Alignments Comparative Genomics 
chainDroSec1 D. sechellia Chain D. sechellia (Oct. 2005 (Broad/droSec1)) Chained Alignments Comparative Genomics Description This track shows alignments of D. sechellia (droSec1, Oct. 2005 (Broad/droSec1)) to the D. melanogaster genome using a gap scoring system that allows longer gaps than traditional affine gap scoring systems. It can also tolerate gaps in both D. sechellia and D. melanogaster simultaneously. These &quot;double-sided&quot; gaps can be caused by local inversions and overlapping deletions in both species. The chain track displays boxes joined together by either single or double lines. The boxes represent aligning regions. Single lines indicate gaps that are largely due to a deletion in the D. sechellia assembly or an insertion in the D. melanogaster assembly. Double lines represent more complex gaps that involve substantial sequence in both species. This may result from inversions, overlapping deletions, an abundance of local mutation, or an unsequenced gap in one species. In cases where multiple chains align over a particular region of the D. melanogaster genome, the chains with single-lined gaps are often due to processed pseudogenes, while chains with double-lined gaps are more often due to paralogs and unprocessed pseudogenes. In the "pack" and "full" display modes, the individual feature names indicate the chromosome, strand, and location (in thousands) of the match for each matching alignment. Display Conventions and Configuration By default, the chains to chromosome-based assemblies are colored based on which chromosome they map to in the aligning organism. To turn off the coloring, check the &quot;off&quot; button next to: Color track based on chromosome. To display only the chains of one chromosome in the aligning organism, enter the name of that chromosome (e.g. chr4) in box next to: Filter by chromosome. Methods The D. sechellia/D. melanogaster genomes were aligned with blastz and converted into axt format using the lavToAxt program. The axt alignments were fed into axtChain, which organizes all alignments between a single D. sechellia chromosome and a single D. melanogaster chromosome into a group and creates a kd-tree out of the gapless subsections (blocks) of the alignments. A dynamic program was then run over the kd-trees to find the maximally scoring chains of these blocks. Chains scoring below a threshold were discarded; the remaining chains are displayed in this track. Credits Blastz was developed at Pennsylvania State University by Minmei Hou, Scott Schwartz, Zheng Zhang, and Webb Miller with advice from Ross Hardison. Lineage-specific repeats were identified by Arian Smit and his RepeatMasker program. The axtChain program was developed at the University of California at Santa Cruz by Jim Kent with advice from Webb Miller and David Haussler. The browser display and database storage of the chains were generated by Robert Baertsch and Jim Kent. References Chiaromonte F, Yap VB, Miller W. Scoring pairwise genomic sequence alignments. Pac Symp Biocomput. 2002:115-26. PMID: 11928468 Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D. Evolution's cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11484-9. PMID: 14500911; PMC: PMC208784 Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D, Miller W. Human-mouse alignments with BLASTZ. Genome Res. 2003 Jan;13(1):103-7. PMID: 12529312; PMC: PMC430961 
chainDroSim1 droSim1 Chain D. simulans (Apr. 2005 (WUGSC mosaic 1.0/droSim1)) Chained Alignments Comparative Genomics Description This track shows alignments of D. simulans (droSim1, Apr. 2005 (WUGSC mosaic 1.0/droSim1)) to the D. melanogaster genome using a gap scoring system that allows longer gaps than traditional affine gap scoring systems. It can also tolerate gaps in both D. simulans and D. melanogaster simultaneously. These &quot;double-sided&quot; gaps can be caused by local inversions and overlapping deletions in both species. The chain track displays boxes joined together by either single or double lines. The boxes represent aligning regions. Single lines indicate gaps that are largely due to a deletion in the D. simulans assembly or an insertion in the D. melanogaster assembly. Double lines represent more complex gaps that involve substantial sequence in both species. This may result from inversions, overlapping deletions, an abundance of local mutation, or an unsequenced gap in one species. In cases where multiple chains align over a particular region of the D. melanogaster genome, the chains with single-lined gaps are often due to processed pseudogenes, while chains with double-lined gaps are more often due to paralogs and unprocessed pseudogenes. In the "pack" and "full" display modes, the individual feature names indicate the chromosome, strand, and location (in thousands) of the match for each matching alignment. Display Conventions and Configuration By default, the chains to chromosome-based assemblies are colored based on which chromosome they map to in the aligning organism. To turn off the coloring, check the &quot;off&quot; button next to: Color track based on chromosome. To display only the chains of one chromosome in the aligning organism, enter the name of that chromosome (e.g. chr4) in box next to: Filter by chromosome. Methods The blastz alignments were converted into axt format using the lavToAxt program. The axt alignments were fed into axtChain, which organizes all alignments between a single D. simulans chromosome and a single D. melanogaster chromosome into a group and creates a kd-tree out of the gapless subsections (blocks) of the alignments. A dynamic program was then run over the kd-trees to find the maximally scoring chains of these blocks. Chains scoring below a threshold were discarded; the remaining chains are displayed in this track. Credits Blastz was developed at Pennsylvania State University by Minmei Hou, Scott Schwartz, Zheng Zhang, and Webb Miller with advice from Ross Hardison. Lineage-specific repeats were identified by Arian Smit and his RepeatMasker program. The axtChain program was developed at the University of California at Santa Cruz by Jim Kent with advice from Webb Miller and David Haussler. The browser display and database storage of the chains were generated by Robert Baertsch and Jim Kent. References Chiaromonte F, Yap VB, Miller W. Scoring pairwise genomic sequence alignments. Pac Symp Biocomput. 2002:115-26. PMID: 11928468 Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D. Evolution's cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11484-9. PMID: 14500911; PMC: PMC208784 Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D, Miller W. Human-mouse alignments with BLASTZ. Genome Res. 2003 Jan;13(1):103-7. PMID: 12529312; PMC: PMC430961 
chainDroSim2 droSim2 Chain D. simulans (Sep. 2014 (ASM75419v2/droSim2)) Chained Alignments Comparative Genomics 
nestedRepeats Interrupted Rpts Fragments of Interrupted Repeats Joined by RepeatMasker ID Variation and Repeats Description This track shows joined fragments of interrupted repeats extracted from the output of the RepeatMasker program which screens DNA sequences for interspersed repeats and low complexity DNA sequences using the Repbase Update library of repeats from the Genetic Information Research Institute (GIRI). Repbase Update is described in Jurka (2000) in the References section below. The detailed annotations from RepeatMasker are in the RepeatMasker track. This track shows fragments of original repeat insertions which have been interrupted by insertions of younger repeats or through local rearrangements. The fragments are joined using the ID column of RepeatMasker output. Display Conventions and Configuration In pack or full mode, each interrupted repeat is displayed as boxes (fragments) joined by horizontal lines, labeled with the repeat name. If all fragments are on the same strand, arrows are added to the horizontal line to indicate the strand. In dense or squish mode, labels and arrows are omitted and in dense mode, all items are collapsed to fit on a single row. Items are shaded according to the average identity score of their fragments. Usually, the shade of an item is similar to the shades of its fragments unless some fragments are much more diverged than others. The score displayed above is the average identity score, clipped to a range of 50% - 100% and then mapped to the range 0 - 1000 for shading in the browser. Methods UCSC has used the most current versions of the RepeatMasker software and repeat libraries available to generate these data. Note that these versions may be newer than those that are publicly available on the Internet. Data are generated using the RepeatMasker -s flag. Additional flags may be used for certain organisms. See the FAQ for more information. Credits Thanks to Arian Smit, Robert Hubley and GIRI for providing the tools and repeat libraries used to generate this track. References Smit AFA, Hubley R, Green P. RepeatMasker Open-3.0. http://www.repeatmasker.org. 1996-2010. Repbase Update is described in: Jurka J. Repbase Update: a database and an electronic journal of repetitive elements. Trends Genet. 2000 Sep;16(9):418-420. PMID: 10973072 For a discussion of repeats in mammalian genomes, see: Smit AF. Interspersed repeats and other mementos of transposable elements in mammalian genomes. Curr Opin Genet Dev. 1999 Dec;9(6):657-63. PMID: 10607616 Smit AF. The origin of interspersed repeats in the human genome. Curr Opin Genet Dev. 1996 Dec;6(6):743-8. PMID: 8994846 
microsat Microsatellite Microsatellites - Di-nucleotide and Tri-nucleotide Repeats Variation and Repeats Description This track displays regions that are likely to be useful as microsatellite markers. These are sequences of at least 15 perfect di-nucleotide and tri-nucleotide repeats and tend to be highly polymorphic in the population. Methods The data shown in this track are a subset of the Simple Repeats track, selecting only those repeats of period 2 and 3, with 100% identity and no indels and with at least 15 copies of the repeat. The Simple Repeats track is created using the Tandem Repeats Finder. For more information about this program, see Benson (1999). Credits Tandem Repeats Finder was written by Gary Benson. References Benson G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 1999 Jan 15;27(2):573-80. PMID: 9862982; PMC: PMC148217 
oreganno ORegAnno Regulatory elements from ORegAnno Expression and Regulation Description This track displays literature-curated regulatory regions, transcription factor binding sites, and regulatory polymorphisms from ORegAnno (Open Regulatory Annotation). For more detailed information on a particular regulatory element, follow the link to ORegAnno from the details page. ORegAnno (Open Regulatory Annotation). --> Display Conventions and Configuration The display may be filtered to show only selected region types, such as: regulatory regions (shown in light blue) regulatory polymorphisms (shown in dark blue) transcription factor binding sites (shown in orange) regulatory haplotypes (shown in red) miRNA binding sites (shown in blue-green) To exclude a region type, uncheck the appropriate box in the list at the top of the Track Settings page. Methods An ORegAnno record describes an experimentally proven and published regulatory region (promoter, enhancer, etc.), transcription factor binding site, or regulatory polymorphism. Each annotation must have the following attributes: A stable ORegAnno identifier. A valid taxonomy ID from the NCBI taxonomy database. A valid PubMed reference. A target gene that is either user-defined, in Entrez Gene or in EnsEMBL. A sequence with at least 40 flanking bases (preferably more) to allow the site to be mapped to any release of an associated genome. At least one piece of specific experimental evidence, including the biological technique used to discover the regulatory sequence. (Currently only the evidence subtypes are supplied with the UCSC track.) A positive, neutral or negative outcome based on the experimental results from the primary reference. (Only records with a positive outcome are currently included in the UCSC track.) The following attributes are optionally included: A transcription factor that is either user-defined, in Entrez Gene or in EnsEMBL. A specific cell type for each piece of experimental evidence, using the eVOC cell type ontology. A specific dataset identifier (e.g. the REDfly dataset) that allows external curators to manage particular annotation sets using ORegAnno's curation tools. A &quot;search space&quot; sequence that specifies the region that was assayed, not just the regulatory sequence. A dbSNP identifier and type of variant (germline, somatic or artificial) for regulatory polymorphisms. Mapping to genome coordinates is performed periodically to current genome builds by BLAST sequence alignment. The information provided in this track represents an abbreviated summary of the details for each ORegAnno record. Please visit the official ORegAnno entry (by clicking on the ORegAnno link on the details page of a specific regulatory element) for complete details such as evidence descriptions, comments, validation score history, etc. Credits ORegAnno core team and principal contacts: Stephen Montgomery, Obi Griffith, and Steven Jones from Canada's Michael Smith Genome Sciences Centre, Vancouver, British Columbia, Canada. The ORegAnno community (please see individual citations for various features): ORegAnno Citation. References Lesurf R, Cotto KC, Wang G, Griffith M, Kasaian K, Jones SJ, Montgomery SB, Griffith OL, Open Regulatory Annotation Consortium.. ORegAnno 3.0: a community-driven resource for curated regulatory annotation. Nucleic Acids Res. 2016 Jan 4;44(D1):D126-32. PMID: 26578589; PMC: PMC4702855 Griffith OL, Montgomery SB, Bernier B, Chu B, Kasaian K, Aerts S, Mahony S, Sleumer MC, Bilenky M, Haeussler M et al. ORegAnno: an open-access community-driven resource for regulatory annotation. Nucleic Acids Res. 2008 Jan;36(Database issue):D107-13. PMID: 18006570; PMC: PMC2239002 Montgomery SB, Griffith OL, Sleumer MC, Bergman CM, Bilenky M, Pleasance ED, Prychyna Y, Zhang X, Jones SJ. ORegAnno: an open access database and curation system for literature-derived promoters, transcription factor binding sites and regulatory variation. Bioinformatics. 2006 Mar 1;22(5):637-40. PMID: 16397004 
xenoMrna Other mRNAs Non-D. melanogaster mRNAs from GenBank mRNA and EST Description This track displays translated blat alignments of vertebrate and invertebrate mRNA in GenBank from organisms other than D. melanogaster. Display Conventions and Configuration This track follows the display conventions for PSL alignment tracks. In dense display mode, the items that are more darkly shaded indicate matches of better quality. The strand information (+/-) for this track is in two parts. The first + indicates the orientation of the query sequence whose translated protein produced the match (here always 5' to 3', hence +). The second + or - indicates the orientation of the matching translated genomic sequence. Because the two orientations of a DNA sequence give different predicted protein sequences, there are four combinations. ++ is not the same as --, nor is +- the same as -+. The description page for this track has a filter that can be used to change the display mode, alter the color, and include/exclude a subset of items within the track. This may be helpful when many items are shown in the track display, especially when only some are relevant to the current task. To use the filter: Type a term in one or more of the text boxes to filter the mRNA display. For example, to apply the filter to all mRNAs expressed in a specific organ, type the name of the organ in the tissue box. To view the list of valid terms for each text box, consult the table in the Table Browser that corresponds to the factor on which you wish to filter. For example, the &quot;tissue&quot; table contains all the types of tissues that can be entered into the tissue text box. Wildcards may also be used in the filter. If filtering on more than one value, choose the desired combination logic. If &quot;and&quot; is selected, only mRNAs that match all filter criteria will be highlighted. If &quot;or&quot; is selected, mRNAs that match any one of the filter criteria will be highlighted. Choose the color or display characteristic that should be used to highlight or include/exclude the filtered items. If &quot;exclude&quot; is chosen, the browser will not display mRNAs that match the filter criteria. If &quot;include&quot; is selected, the browser will display only those mRNAs that match the filter criteria. This track may also be configured to display codon coloring, a feature that allows the user to quickly compare mRNAs against the genomic sequence. For more information about this option, click here. Methods The mRNAs were aligned against the D. melanogaster genome using translated blat. When a single mRNA aligned in multiple places, the alignment having the highest base identity was found. Only those alignments having a base identity level within 1% of the best and at least 25% base identity with the genomic sequence were kept. Credits The mRNA track was produced at UCSC from mRNA sequence data submitted to the international public sequence databases by scientists worldwide. References Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. GenBank: update. Nucleic Acids Res. 2004 Jan 1;32(Database issue):D23-6. PMID: 14681350; PMC: PMC308779 Kent WJ. BLAT--the BLAST-like alignment tool. Genome Res. 2002 Apr;12(4):656-64. PMID: 11932250; PMC: PMC187518 
xenoRefGene Other RefSeq Non-D. melanogaster RefSeq Genes Genes and Gene Predictions Description This track shows known protein-coding and non-protein-coding genes for organisms other than D. melanogaster, taken from the NCBI RNA reference sequences collection (RefSeq). The data underlying this track are updated weekly. Display Conventions and Configuration This track follows the display conventions for gene prediction tracks. The color shading indicates the level of review the RefSeq record has undergone: predicted (light), provisional (medium), reviewed (dark). The item labels and display colors of features within this track can be configured through the controls at the top of the track description page. Label: By default, items are labeled by gene name. Click the appropriate Label option to display the accession name instead of the gene name, show both the gene and accession names, or turn off the label completely. Codon coloring: This track contains an optional codon coloring feature that allows users to quickly validate and compare gene predictions. To display codon colors, select the genomic codons option from the Color track by codons pull-down menu. Click here for more information about this feature. Hide non-coding genes: By default, both the protein-coding and non-protein-coding genes are displayed. If you wish to see only the coding genes, click this box. Methods The RNAs were aligned against the D. melanogaster genome using blat; those with an alignment of less than 15% were discarded. When a single RNA aligned in multiple places, the alignment having the highest base identity was identified. Only alignments having a base identity level within 0.5% of the best and at least 25% base identity with the genomic sequence were kept. Credits This track was produced at UCSC from RNA sequence data generated by scientists worldwide and curated by the NCBI RefSeq project. References Kent WJ. BLAT - the BLAST-like alignment tool. Genome Res. 2002 Apr;12(4):656-64. PMID: 11932250; PMC: PMC187518 
refGenePfam Pfam in RefSeq Pfam Domains in RefSeq Genes Genes and Gene Predictions Description Most proteins are composed of one or more conserved functional regions called domains. This track shows the high-quality, manually-curated Pfam-A domains found in proteins associated with the RefSeq Genes transcripts. Display Conventions and Configuration This track follows the display conventions for gene tracks. Methods The proteins associated with the transcripts in the refGene table (see RefSeq Genes description page) are submitted to the set of Pfam-A HMMs which annotate regions within the predicted peptide that are recognizable as Pfam protein domains. These regions are then mapped to the transcripts themselves using the pslMap utility. Of the several options for filtering out false positives, the &quot;Trusted cutoff (TC)&quot; threshold method is used in this track to determine significance. For more information regarding thresholds and scores, see the HMMER documentation and results interpretation pages. Note: There is currently an undocumented but known HMMER problem which results in lessened sensitivity and possible missed searches for some zinc finger domains. Until a fix is released for HMMER /PFAM thresholds, please also consult the "UniProt Domains" subtrack of the UniProt track for more comprehensive zinc finger annotations. Credits pslMap was written by Mark Diekhans at UCSC. References Finn RD, Mistry J, Tate J, Coggill P, Heger A, Pollington JE, Gavin OL, Gunasekaran P, Ceric G, Forslund K et al. The Pfam protein families database. Nucleic Acids Res. 2010 Jan;38(Database issue):D211-22. PMID: 19920124; PMC: PMC2808889 
ucscToRefSeq RefSeq Acc RefSeq Accession Mapping and Sequencing Description This track associates UCSC Genome Browser chromosome names to accession identifiers from the NCBI Reference Sequence Database (RefSeq). The data were downloaded from the NCBI assembly database. Credits The data for this track was prepared by Hiram Clawson. 
ReMap ReMap ChIP-seq ReMap Atlas of Regulatory Regions Expression and Regulation Description This track represents the ReMap Atlas of regulatory regions, which consists of a large-scale integrative analysis of all Public ChIP-seq data for transcriptional regulators from GEO, ArrayExpress, and ENCODE. Below is a schematic diagram of the types of regulatory regions: ReMap 2022 Atlas (all peaks for each analyzed data set) ReMap 2022 Non-redundant peaks (merged similar target) ReMap 2022 Cis Regulatory Modules Display Conventions and Configuration Each transcription factor follows a specific RGB color. ChIP-seq peak summits are represented by vertical bars. Hsap: A data set is defined as a ChIP/Exo-seq experiment in a given GEO/ArrayExpress/ENCODE series (e.g. GSE41561), for a given TF (e.g. ESR1), in a particular biological condition (e.g. MCF-7). Data sets are labeled with the concatenation of these three pieces of information (e.g. GSE41561.ESR1.MCF-7). Atha: The data set is defined as a ChIP-seq experiment in a given series (e.g. GSE94486), for a given target (e.g. ARR1), in a particular biological condition (i.e. ecotype, tissue type, experimental conditions; e.g. Col-0_seedling_3d-6BA-4h). Data sets are labeled with the concatenation of these three pieces of information (e.g. GSE94486.ARR1.Col-0_seedling_3d-6BA-4h). Methods This 4th release of ReMap (2022) presents the analysis of 1,206 quality controlled ChIP-seq (n=1,315 before QCs) data sets from public sources (GEO, ENCODE). Those ChIP-seq data sets have been mapped to the dm6 drosophila assembly. The data set is defined as a ChIP-seq experiment in a given series (e.g. GSE107059), for a given TF (e.g. Trl), in a particular biological condition (i.e. cell line, tissue type, disease state, or experimental conditions; e.g. Schneider-2). Data sets were labeled by concatenating these three pieces of information, such as GSE107059.Trl.Schneider-2. Those merged analyses cover a total of 550 DNA-binding proteins (transcriptional regulators) such as a variety of transcription factors (TFs), transcription co-activators (TCFs), and chromatin-remodeling factors (CRFs) for 16 million peaks. ENCODE Available ENCODE ChIP-seq data sets for transcriptional regulators from the ENCODE portal were processed with the standardized ReMap pipeline. The list of ENCODE data was retrieved as FASTQ files from the ENCODE portal using filters. Metadata information in JSON format and FASTQ files were retrieved using the Python requests module. ChIP-seq processing Both Public and ENCODE data were processed similarly. Bowtie 2 (PMC3322381) (version 2.2.9) with options -end-to-end -sensitive was used to align all reads on the genome. Biological and technical replicates for each unique combination of GSE/TF/Cell type or Biological condition were used for peak calling. TFBS were identified using MACS2 peak-calling tool (PMC3120977) (version 2.1.1.2) in order to follow ENCODE ChIP-seq guidelines, with stringent thresholds (MACS2 default thresholds, p-value: 1e-5). An input data set was used when available. Quality assessment To assess the quality of public data sets, a score was computed based on the cross-correlation and the FRiP (fraction of reads in peaks) metrics developed by the ENCODE Consortium (https://genome.ucsc.edu/ENCODE/qualityMetrics.html). Two thresholds were defined for each of the two cross-correlation ratios (NSC, normalized strand coefficient: 1.05 and 1.10; RSC, relative strand coefficient: 0.8 and 1.0). Detailed descriptions of the ENCODE quality coefficients can be found at https://genome.ucsc.edu/ENCODE/qualityMetrics.html. The phantompeak tools suite was used (https://code.google.com/p/phantompeakqualtools/) to compute RSC and NSC. Please refer to the ReMap 2022, 2020, and 2018 publications for more details (citation below). This is a detailled view of the data increase in ReMap v2 with FOXA1 peaks at a specific location. --> Data Access ReMap Atlas of regulatory regions data can be explored interactively with the Table Browser and cross-referenced with the Data Integrator. For programmatic access, the track can be accessed using the Genome Browser&apos;s REST API. ReMap annotations can be downloaded from the Genome Browser's download server as a bigBed file. This compressed binary format can be remotely queried through command line utilities. Please note that some of the download files can be quite large. Individual BED files for specific TFs, cells/biotypes, or data sets can be found and downloaded on the ReMap website. References Ch&#232;neby J, Gheorghe M, Artufel M, Mathelier A, Ballester B. ReMap 2018: an updated atlas of regulatory regions from an integrative analysis of DNA-binding ChIP- seq experiments. Nucleic Acids Res. 2018 Jan 4;46(D1):D267-D275. PMID: 29126285; PMC: PMC5753247 Ch&#232;neby J, M&#233;n&#233;trier Z, Mestdagh M, Rosnet T, Douida A, Rhalloussi W, Bergon A, Lopez F, Ballester B. ReMap 2020: a database of regulatory regions from an integrative analysis of Human and Arabidopsis DNA-binding sequencing experiments. Nucleic Acids Res. 2020 Jan 8;48(D1):D180-D188. PMID: 31665499; PMC: PMC7145625 Griffon A, Barbier Q, Dalino J, van Helden J, Spicuglia S, Ballester B. Integrative analysis of public ChIP-seq experiments reveals a complex multi-cell regulatory landscape. Nucleic Acids Res. 2015 Feb 27;43(4):e27. PMID: 25477382; PMC: PMC4344487 Hammal F, de Langen P, Bergon A, Lopez F, Ballester B. ReMap 2022: a database of Human, Mouse, Drosophila and Arabidopsis regulatory regions from an integrative analysis of DNA-binding sequencing experiments. Nucleic Acids Res. 2022 Jan 7;50(D1):D316-D325. PMID: 34751401; PMC: PMC8728178 
ReMapTFs ReMap ChIP-seq ReMap Atlas of Regulatory Regions Expression and Regulation Description This track represents the ReMap Atlas of regulatory regions, which consists of a large-scale integrative analysis of all Public ChIP-seq data for transcriptional regulators from GEO, ArrayExpress, and ENCODE. Below is a schematic diagram of the types of regulatory regions: ReMap 2022 Atlas (all peaks for each analyzed data set) ReMap 2022 Non-redundant peaks (merged similar target) ReMap 2022 Cis Regulatory Modules Display Conventions and Configuration Each transcription factor follows a specific RGB color. ChIP-seq peak summits are represented by vertical bars. Hsap: A data set is defined as a ChIP/Exo-seq experiment in a given GEO/ArrayExpress/ENCODE series (e.g. GSE41561), for a given TF (e.g. ESR1), in a particular biological condition (e.g. MCF-7). Data sets are labeled with the concatenation of these three pieces of information (e.g. GSE41561.ESR1.MCF-7). Atha: The data set is defined as a ChIP-seq experiment in a given series (e.g. GSE94486), for a given target (e.g. ARR1), in a particular biological condition (i.e. ecotype, tissue type, experimental conditions; e.g. Col-0_seedling_3d-6BA-4h). Data sets are labeled with the concatenation of these three pieces of information (e.g. GSE94486.ARR1.Col-0_seedling_3d-6BA-4h). Methods This 4th release of ReMap (2022) presents the analysis of 1,206 quality controlled ChIP-seq (n=1,315 before QCs) data sets from public sources (GEO, ENCODE). Those ChIP-seq data sets have been mapped to the dm6 drosophila assembly. The data set is defined as a ChIP-seq experiment in a given series (e.g. GSE107059), for a given TF (e.g. Trl), in a particular biological condition (i.e. cell line, tissue type, disease state, or experimental conditions; e.g. Schneider-2). Data sets were labeled by concatenating these three pieces of information, such as GSE107059.Trl.Schneider-2. Those merged analyses cover a total of 550 DNA-binding proteins (transcriptional regulators) such as a variety of transcription factors (TFs), transcription co-activators (TCFs), and chromatin-remodeling factors (CRFs) for 16 million peaks. ENCODE Available ENCODE ChIP-seq data sets for transcriptional regulators from the ENCODE portal were processed with the standardized ReMap pipeline. The list of ENCODE data was retrieved as FASTQ files from the ENCODE portal using filters. Metadata information in JSON format and FASTQ files were retrieved using the Python requests module. ChIP-seq processing Both Public and ENCODE data were processed similarly. Bowtie 2 (PMC3322381) (version 2.2.9) with options -end-to-end -sensitive was used to align all reads on the genome. Biological and technical replicates for each unique combination of GSE/TF/Cell type or Biological condition were used for peak calling. TFBS were identified using MACS2 peak-calling tool (PMC3120977) (version 2.1.1.2) in order to follow ENCODE ChIP-seq guidelines, with stringent thresholds (MACS2 default thresholds, p-value: 1e-5). An input data set was used when available. Quality assessment To assess the quality of public data sets, a score was computed based on the cross-correlation and the FRiP (fraction of reads in peaks) metrics developed by the ENCODE Consortium (https://genome.ucsc.edu/ENCODE/qualityMetrics.html). Two thresholds were defined for each of the two cross-correlation ratios (NSC, normalized strand coefficient: 1.05 and 1.10; RSC, relative strand coefficient: 0.8 and 1.0). Detailed descriptions of the ENCODE quality coefficients can be found at https://genome.ucsc.edu/ENCODE/qualityMetrics.html. The phantompeak tools suite was used (https://code.google.com/p/phantompeakqualtools/) to compute RSC and NSC. Please refer to the ReMap 2022, 2020, and 2018 publications for more details (citation below). This is a detailled view of the data increase in ReMap v2 with FOXA1 peaks at a specific location. --> Data Access ReMap Atlas of regulatory regions data can be explored interactively with the Table Browser and cross-referenced with the Data Integrator. For programmatic access, the track can be accessed using the Genome Browser&apos;s REST API. ReMap annotations can be downloaded from the Genome Browser's download server as a bigBed file. This compressed binary format can be remotely queried through command line utilities. Please note that some of the download files can be quite large. Individual BED files for specific TFs, cells/biotypes, or data sets can be found and downloaded on the ReMap website. References Ch&#232;neby J, Gheorghe M, Artufel M, Mathelier A, Ballester B. ReMap 2018: an updated atlas of regulatory regions from an integrative analysis of DNA-binding ChIP- seq experiments. Nucleic Acids Res. 2018 Jan 4;46(D1):D267-D275. PMID: 29126285; PMC: PMC5753247 Ch&#232;neby J, M&#233;n&#233;trier Z, Mestdagh M, Rosnet T, Douida A, Rhalloussi W, Bergon A, Lopez F, Ballester B. ReMap 2020: a database of regulatory regions from an integrative analysis of Human and Arabidopsis DNA-binding sequencing experiments. Nucleic Acids Res. 2020 Jan 8;48(D1):D180-D188. PMID: 31665499; PMC: PMC7145625 Griffon A, Barbier Q, Dalino J, van Helden J, Spicuglia S, Ballester B. Integrative analysis of public ChIP-seq experiments reveals a complex multi-cell regulatory landscape. Nucleic Acids Res. 2015 Feb 27;43(4):e27. PMID: 25477382; PMC: PMC4344487 Hammal F, de Langen P, Bergon A, Lopez F, Ballester B. ReMap 2022: a database of Human, Mouse, Drosophila and Arabidopsis regulatory regions from an integrative analysis of DNA-binding sequencing experiments. Nucleic Acids Res. 2022 Jan 7;50(D1):D316-D325. PMID: 34751401; PMC: PMC8728178 
ReMapDensity ReMap density ReMap density Expression and Regulation Description This track represents the ReMap Atlas of regulatory regions, which consists of a large-scale integrative analysis of all Public ChIP-seq data for transcriptional regulators from GEO, ArrayExpress, and ENCODE. Below is a schematic diagram of the types of regulatory regions: ReMap 2022 Atlas (all peaks for each analyzed data set) ReMap 2022 Non-redundant peaks (merged similar target) ReMap 2022 Cis Regulatory Modules Display Conventions and Configuration Each transcription factor follows a specific RGB color. ChIP-seq peak summits are represented by vertical bars. Hsap: A data set is defined as a ChIP/Exo-seq experiment in a given GEO/ArrayExpress/ENCODE series (e.g. GSE41561), for a given TF (e.g. ESR1), in a particular biological condition (e.g. MCF-7). Data sets are labeled with the concatenation of these three pieces of information (e.g. GSE41561.ESR1.MCF-7). Atha: The data set is defined as a ChIP-seq experiment in a given series (e.g. GSE94486), for a given target (e.g. ARR1), in a particular biological condition (i.e. ecotype, tissue type, experimental conditions; e.g. Col-0_seedling_3d-6BA-4h). Data sets are labeled with the concatenation of these three pieces of information (e.g. GSE94486.ARR1.Col-0_seedling_3d-6BA-4h). Methods This 4th release of ReMap (2022) presents the analysis of 1,206 quality controlled ChIP-seq (n=1,315 before QCs) data sets from public sources (GEO, ENCODE). Those ChIP-seq data sets have been mapped to the dm6 drosophila assembly. The data set is defined as a ChIP-seq experiment in a given series (e.g. GSE107059), for a given TF (e.g. Trl), in a particular biological condition (i.e. cell line, tissue type, disease state, or experimental conditions; e.g. Schneider-2). Data sets were labeled by concatenating these three pieces of information, such as GSE107059.Trl.Schneider-2. Those merged analyses cover a total of 550 DNA-binding proteins (transcriptional regulators) such as a variety of transcription factors (TFs), transcription co-activators (TCFs), and chromatin-remodeling factors (CRFs) for 16 million peaks. ENCODE Available ENCODE ChIP-seq data sets for transcriptional regulators from the ENCODE portal were processed with the standardized ReMap pipeline. The list of ENCODE data was retrieved as FASTQ files from the ENCODE portal using filters. Metadata information in JSON format and FASTQ files were retrieved using the Python requests module. ChIP-seq processing Both Public and ENCODE data were processed similarly. Bowtie 2 (PMC3322381) (version 2.2.9) with options -end-to-end -sensitive was used to align all reads on the genome. Biological and technical replicates for each unique combination of GSE/TF/Cell type or Biological condition were used for peak calling. TFBS were identified using MACS2 peak-calling tool (PMC3120977) (version 2.1.1.2) in order to follow ENCODE ChIP-seq guidelines, with stringent thresholds (MACS2 default thresholds, p-value: 1e-5). An input data set was used when available. Quality assessment To assess the quality of public data sets, a score was computed based on the cross-correlation and the FRiP (fraction of reads in peaks) metrics developed by the ENCODE Consortium (https://genome.ucsc.edu/ENCODE/qualityMetrics.html). Two thresholds were defined for each of the two cross-correlation ratios (NSC, normalized strand coefficient: 1.05 and 1.10; RSC, relative strand coefficient: 0.8 and 1.0). Detailed descriptions of the ENCODE quality coefficients can be found at https://genome.ucsc.edu/ENCODE/qualityMetrics.html. The phantompeak tools suite was used (https://code.google.com/p/phantompeakqualtools/) to compute RSC and NSC. Please refer to the ReMap 2022, 2020, and 2018 publications for more details (citation below). This is a detailled view of the data increase in ReMap v2 with FOXA1 peaks at a specific location. --> Data Access ReMap Atlas of regulatory regions data can be explored interactively with the Table Browser and cross-referenced with the Data Integrator. For programmatic access, the track can be accessed using the Genome Browser&apos;s REST API. ReMap annotations can be downloaded from the Genome Browser's download server as a bigBed file. This compressed binary format can be remotely queried through command line utilities. Please note that some of the download files can be quite large. Individual BED files for specific TFs, cells/biotypes, or data sets can be found and downloaded on the ReMap website. References Ch&#232;neby J, Gheorghe M, Artufel M, Mathelier A, Ballester B. ReMap 2018: an updated atlas of regulatory regions from an integrative analysis of DNA-binding ChIP- seq experiments. Nucleic Acids Res. 2018 Jan 4;46(D1):D267-D275. PMID: 29126285; PMC: PMC5753247 Ch&#232;neby J, M&#233;n&#233;trier Z, Mestdagh M, Rosnet T, Douida A, Rhalloussi W, Bergon A, Lopez F, Ballester B. ReMap 2020: a database of regulatory regions from an integrative analysis of Human and Arabidopsis DNA-binding sequencing experiments. Nucleic Acids Res. 2020 Jan 8;48(D1):D180-D188. PMID: 31665499; PMC: PMC7145625 Griffon A, Barbier Q, Dalino J, van Helden J, Spicuglia S, Ballester B. Integrative analysis of public ChIP-seq experiments reveals a complex multi-cell regulatory landscape. Nucleic Acids Res. 2015 Feb 27;43(4):e27. PMID: 25477382; PMC: PMC4344487 Hammal F, de Langen P, Bergon A, Lopez F, Ballester B. ReMap 2022: a database of Human, Mouse, Drosophila and Arabidopsis regulatory regions from an integrative analysis of DNA-binding sequencing experiments. Nucleic Acids Res. 2022 Jan 7;50(D1):D316-D325. PMID: 34751401; PMC: PMC8728178 
simpleRepeat Simple Repeats Simple Tandem Repeats by TRF Variation and Repeats Description This track displays simple tandem repeats (possibly imperfect repeats) located by Tandem Repeats Finder (TRF) which is specialized for this purpose. These repeats can occur within coding regions of genes and may be quite polymorphic. Repeat expansions are sometimes associated with specific diseases. Methods For more information about the TRF program, see Benson (1999). Credits TRF was written by Gary Benson. References Benson G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 1999 Jan 15;27(2):573-80. PMID: 9862982; PMC: PMC148217 
uniprot UniProt UniProt SwissProt/TrEMBL Protein Annotations Genes and Gene Predictions Description This track shows protein sequences and annotations on them from the UniProt/SwissProt database, mapped to genomic coordinates. UniProt/SwissProt data has been curated from scientific publications by the UniProt staff, UniProt/TrEMBL data has been predicted by various computational algorithms. The annotations are divided into multiple subtracks, based on their &quot;feature type&quot; in UniProt. The first two subtracks below - one for SwissProt, one for TrEMBL - show the alignments of protein sequences to the genome, all other tracks below are the protein annotations mapped through these alignments to the genome. Track Name Description UCSC Alignment, SwissProt = curated protein sequences Protein sequences from SwissProt mapped to the genome. All other tracks are (start,end) SwissProt annotations on these sequences mapped through this alignment. Even protein sequences without a single curated annotation (splice isoforms) are visible in this track. Each UniProt protein has one main isoform, which is colored in dark. Alternative isoforms are sequences that do not have annotations on them and are colored in light-blue. They can be hidden with the TrEMBL/Isoform filter (see below). UCSC Alignment, TrEMBL = predicted protein sequences Protein sequences from TrEMBL mapped to the genome. All other tracks below are (start,end) TrEMBL annotations mapped to the genome using this track. This track is hidden by default. To show it, click its checkbox on the track configuration page. UniProt Signal Peptides Regions found in proteins destined to be secreted, generally cleaved from mature protein. UniProt Extracellular Domains Protein domains with the comment &quot;Extracellular&quot;. UniProt Transmembrane Domains Protein domains of the type &quot;Transmembrane&quot;. UniProt Cytoplasmic Domains Protein domains with the comment &quot;Cytoplasmic&quot;. UniProt Polypeptide Chains Polypeptide chain in mature protein after post-processing. UniProt Regions of Interest Regions that have been experimentally defined, such as the role of a region in mediating protein-protein interactions or some other biological process. UniProt Domains Protein domains, zinc finger regions and topological domains. UniProt Disulfide Bonds Disulfide bonds. UniProt Amino Acid Modifications Glycosylation sites, modified residues and lipid moiety-binding regions. UniProt Amino Acid Mutations Mutagenesis sites and sequence variants. UniProt Protein Primary/Secondary Structure Annotations Beta strands, helices, coiled-coil regions and turns. UniProt Sequence Conflicts Differences between Genbank sequences and the UniProt sequence. UniProt Repeats Regions of repeated sequence motifs or repeated domains. UniProt Other Annotations All other annotations, e.g. compositional bias For consistency and convenience for users of mutation-related tracks, the subtrack &quot;UniProt/SwissProt Variants&quot; is a copy of the track &quot;UniProt Variants&quot; in the track group &quot;Phenotype and Literature&quot;, or &quot;Variation and Repeats&quot;, depending on the assembly. Display Conventions and Configuration Genomic locations of UniProt/SwissProt annotations are labeled with a short name for the type of annotation (e.g. &quot;glyco&quot;, &quot;disulf bond&quot;, &quot;Signal peptide&quot; etc.). A click on them shows the full annotation and provides a link to the UniProt/SwissProt record for more details. TrEMBL annotations are always shown in light blue, except in the Signal Peptides, Extracellular Domains, Transmembrane Domains, and Cytoplamsic domains subtracks. Mouse over a feature to see the full UniProt annotation comment. For variants, the mouse over will show the full name of the UniProt disease acronym. The subtracks for domains related to subcellular location are sorted from outside to inside of the cell: Signal peptide, extracellular, transmembrane, and cytoplasmic. In the &quot;UniProt Modifications&quot; track, lipoification sites are highlighted in dark blue, glycosylation sites in dark green, and phosphorylation in light green. Duplicate annotations are removed as far as possible: if a TrEMBL annotation has the same genome position and same feature type, comment, disease and mutated amino acids as a SwissProt annotation, it is not shown again. Two annotations mapped through different protein sequence alignments but with the same genome coordinates are only shown once. On the configuration page of this track, you can choose to hide any TrEMBL annotations. This filter will also hide the UniProt alternative isoform protein sequences because both types of information are less relevant to most users. Please contact us if you want more detailed filtering features. Note that for the human hg38 assembly and SwissProt annotations, there also is a public track hub prepared by UniProt itself, with genome annotations maintained by UniProt using their own mapping method based on those Gencode/Ensembl gene models that are annotated in UniProt for a given protein. For proteins that differ from the genome, UniProt's mapping method will, in most cases, map a protein and its annotations to an unexpected location (see below for details on UCSC's mapping method). Methods Briefly, UniProt protein sequences were aligned to the transcripts associated with the protein, the top-scoring alignments were retained, and the result was projected to the genome through a transcript-to-genome alignment. Depending on the genome, the transcript-genome alignments was either provided by the source database (NBCI RefSeq), created at UCSC (UCSC RefSeq) or derived from the transcripts (Ensembl/Augustus). The transcript set is NCBI RefSeq for hg38, UCSC RefSeq for hg19 (due to alt/fix haplotype misplacements in the NCBI RefSeq set on hg19). For other genomes, RefSeq, Ensembl and Augustus are tried, in this order. The resulting protein-genome alignments of this process are available in the file formats for liftOver or pslMap from our data archive (see "Data Access" section below). An important step of the mapping process protein -&gt; transcript -&gt; genome is filtering the alignment from protein to transcript. Due to differences between the UniProt proteins and the transcripts (proteins were made many years before the transcripts were made, and human genomes have variants), the transcript with the highest BLAST score when aligning the protein to all transcripts is not always the correct transcript for a protein sequence. Therefore, the protein sequence is aligned to only a very short list of one or sometimes more transcripts, selected by a three-step procedure: Use transcripts directly annotated by UniProt: for organisms that have a RefSeq transcript track, proteins are aligned to the RefSeq transcripts that are annotated by UniProt for this particular protein. Use transcripts for NCBI Gene ID annotated by UniProt: If no transcripts are annotated on the protein, or the annotated ones have been deprecated by NCBI, but a NCBI Gene ID is annotated, the RefSeq transcripts for this Gene ID are used. This can result in multiple matching transcripts for a protein. Use best matching transcript: If no NCBI Gene is annotated, then BLAST scores are used to pick the transcripts. There can be multiple transcripts for one protein, as their coding sequences can be identical. All transcripts within 1% of the highest observed BLAST score are used. For strategy 2 and 3, many of the transcripts found do not differ in coding sequence, so the resulting alignments on the genome will be identical. Therefore, any identical alignments are removed in a final filtering step. The details page of these alignments will contain a list of all transcripts that result in the same protein-genome alignment. On hg38, only a handful of edge cases (pseudogenes, very recently added proteins) remain in 2023 where strategy 3 has to be used. In other words, when an NCBI or UCSC RefSeq track is used for the mapping and to align a protein sequence to the correct transcript, we use a three stage process: If UniProt has annotated a given RefSeq transcript for a given protein sequence, the protein is aligned to this transcript. Any difference in the version suffix is tolerated in this comparison. If no transcript is annotated or the transcript cannot be found in the NCBI/UCSC RefSeq track, the UniProt-annotated NCBI Gene ID is resolved to a set of NCBI RefSeq transcript IDs via the most current version of NCBI genes tables. Only the top match of the resulting alignments and all others within 1% of its score are used for the mapping. If no transcript can be found after step (2), the protein is aligned to all transcripts, the top match, and all others within 1% of its score are used. This system was designed to resolve the problem of incorrect mappings of proteins, mostly on hg38, due to differences between the SwissProt sequences and the genome reference sequence, which has changed since the proteins were defined. The problem is most pronounced for gene families composed of either very repetitive or very similar proteins. To make sure that the alignments always go to the best chromosome location, all _alt and _fix reference patch sequences are ignored for the alignment, so the patches are entirely free of UniProt annotations. Please contact us if you have feedback on this process or example edge cases. We are not aware of a way to evaluate the results completely and in an automated manner. Proteins were aligned to transcripts with TBLASTN, converted to PSL, filtered with pslReps (93% query coverage, keep alignments within top 1% score), lifted to genome positions with pslMap and filtered again with pslReps. UniProt annotations were obtained from the UniProt XML file. The UniProt annotations were then mapped to the genome through the alignment described above using the pslMap program. This approach draws heavily on the LS-SNP pipeline by Mark Diekhans. Like all Genome Browser source code, the main script used to build this track can be found on Github. Older releases This track is automatically updated on an ongoing basis, every 2-3 months. The current version name is always shown on the track details page, it includes the release of UniProt, the version of the transcript set and a unique MD5 that is based on the protein sequences, the transcript sequences, the mapping file between both and the transcript-genome alignment. The exact transcript that was used for the alignment is shown when clicking a protein alignment in one of the two alignment tracks. For reproducibility of older analysis results and for manual inspection, previous versions of this track are available for browsing in the form of the UCSC UniProt Archive Track Hub (click this link to connect the hub now). The underlying data of all releases of this track (past and current) can be obtained from our downloads server, including the UniProt protein-to-genome alignment. Data Access The raw data of the current track can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the genome annotation is stored in a bigBed file that can be downloaded from the download server. The exact filenames can be found in the track configuration file. Annotations can be converted to ASCII text by our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, for example: bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/dm6/uniprot/unipStruct.bb -chrom=chr6 -start=0 -end=1000000 stdout Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Lifting from UniProt to genome coordinates in pipelines To facilitate mapping protein coordinates to the genome, we provide the alignment files in formats that are suitable for our command line tools. Our command line programs liftOver or pslMap can be used to map coordinates on protein sequences to genome coordinates. The filenames are unipToGenome.over.chain.gz (liftOver) and unipToGenomeLift.psl.gz (pslMap). Example commands: wget -q https://hgdownload.soe.ucsc.edu/goldenPath/archive/hg38/uniprot/2022_03/unipToGenome.over.chain.gz wget -q https://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/liftOver chmod a+x liftOver echo 'Q99697 1 10 annotationOnProtein' &gt; prot.bed liftOver prot.bed unipToGenome.over.chain.gz genome.bed cat genome.bed Credits This track was created by Maximilian Haeussler at UCSC, with a lot of input from Chris Lee, Mark Diekhans and Brian Raney, feedback from the UniProt staff, Alejo Mujica, Regeneron Pharmaceuticals and Pia Riestra, GeneDx. Thanks to UniProt for making all data available for download. References UniProt Consortium. Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic Acids Res. 2012 Jan;40(Database issue):D71-5. PMID: 22102590; PMC: PMC3245120 Yip YL, Scheib H, Diemand AV, Gattiker A, Famiglietti LM, Gasteiger E, Bairoch A. The Swiss-Prot variant page and the ModSNP database: a resource for sequence and structure information on human protein variants. Hum Mutat. 2004 May;23(5):464-70. PMID: 15108278 
unipConflict Seq. Conflicts UniProt Sequence Conflicts Genes and Gene Predictions 
unipRepeat Repeats UniProt Repeats Genes and Gene Predictions 
unipStruct Structure UniProt Protein Primary/Secondary Structure Annotations Genes and Gene Predictions 
unipOther Other Annot. UniProt Other Annotations Genes and Gene Predictions 
unipMut Mutations UniProt Amino Acid Mutations Genes and Gene Predictions 
unipModif AA Modifications UniProt Amino Acid Modifications Genes and Gene Predictions 
unipDomain Domains UniProt Domains Genes and Gene Predictions 
unipDisulfBond Disulf. Bonds UniProt Disulfide Bonds Genes and Gene Predictions 
unipChain Chains UniProt Mature Protein Products (Polypeptide Chains) Genes and Gene Predictions 
unipLocCytopl Cytoplasmic UniProt Cytoplasmic Domains Genes and Gene Predictions 
unipLocTransMemb Transmembrane UniProt Transmembrane Domains Genes and Gene Predictions 
unipInterest Interest UniProt Regions of Interest Genes and Gene Predictions 
unipLocExtra Extracellular UniProt Extracellular Domain Genes and Gene Predictions 
unipLocSignal Signal Peptide UniProt Signal Peptides Genes and Gene Predictions 
unipAliTrembl TrEMBL Aln. UCSC alignment of TrEMBL proteins to genome Genes and Gene Predictions 
unipAliSwissprot SwissProt Aln. UCSC alignment of SwissProt proteins to genome (dark blue: main isoform, light blue: alternative isoforms) Genes and Gene Predictions 
spMut UniProt Variants UniProt/SwissProt Amino Acid Substitutions Variation and Repeats Description NOTE: This track is intended for use primarily by physicians and other professionals concerned with genetic disorders, by genetics researchers, and by advanced students in science and medicine. While the genome browser database is open to the public, users seeking information about a personal medical or genetic condition are urged to consult with a qualified physician for diagnosis and for answers to personal questions. This track shows the genomic positions of natural and artifical amino acid variants in the UniProt/SwissProt database. The data has been curated from scientific publications by the UniProt staff. Display Conventions and Configuration Genomic locations of UniProt/SwissProt variants are labeled with the amino acid change at a given position and, if known, the abbreviated disease name. A &quot;?&quot; is used if there is no disease annotated at this location, but the protein is described as being linked to only a single disease in UniProt. Mouse over a mutation to see the UniProt comments. Artificially-introduced mutations are colored green and naturally-occurring variants are colored red. For full information about a particular variant, click the &quot;UniProt variant&quot; linkout. The &quot;UniProt record&quot; linkout lists all variants of a particular protein sequence. The &quot;Source articles&quot; linkout lists the articles in PubMed that originally described the variant(s) and were used as evidence by the UniProt curators. Methods UniProt sequences were aligned to RefSeq sequences first with BLAT, then lifted to genome positions with pslMap. UniProt variants were parsed from the UniProt XML file. The variants were then mapped to the genome through the alignment using the pslMap program. This mapping approach draws heavily on the LS-SNP pipeline by Mark Diekhans. The complete script is part of the kent source tree and is located in src/hg/utils/uniprotMutations. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the genome annotation is stored in a bigBed file that can be downloaded from the download server. The underlying data file for this track is called spMut.bb. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, for example: bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/dm6/bbi/uniprot/spMut.bb -chrom=chr6 -start=0 -end=1000000 stdout Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credits This track was created by Maximilian Haeussler, with advice from Mark Diekhans and Brian Raney. References UniProt Consortium. Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic Acids Res. 2012 Jan;40(Database issue):D71-5. PMID: 22102590; PMC: PMC3245120 Yip YL, Scheib H, Diemand AV, Gattiker A, Famiglietti LM, Gasteiger E, Bairoch A. The Swiss-Prot variant page and the ModSNP database: a resource for sequence and structure information on human protein variants. Hum Mutat. 2004 May;23(5):464-70. PMID: 15108278 
windowmaskerSdust WM + SDust Genomic Intervals Masked by WindowMasker + SDust Variation and Repeats Description This track depicts masked sequence as determined by WindowMasker. The WindowMasker tool is included in the NCBI C++ toolkit. The source code for the entire toolkit is available from the NCBI FTP site. Methods To create this track, WindowMasker was run with the following parameters: windowmasker -mk_counts true -input dm6.fa -output wm_counts windowmasker -ustat wm_counts -sdust true -input dm6.fa -output repeats.bed The repeats.bed (BED3) file was loaded into the &quot;windowmaskerSdust&quot; table for this track. References Morgulis A, Gertz EM, Sch&auml;ffer AA, Agarwala R. WindowMasker: window-based masker for sequenced genomes. Bioinformatics. 2006 Jan 15;22(2):134-41. PMID: 16287941 
rmsk RepeatMasker Repeating Elements by RepeatMasker Variation and Repeats Description This track was created by using Arian Smit's RepeatMasker program, which screens DNA sequences for interspersed repeats and low complexity DNA sequences. The program outputs a detailed annotation of the repeats that are present in the query sequence (represented by this track), as well as a modified version of the query sequence in which all the annotated repeats have been masked (generally available on the Downloads page). RepeatMasker uses the Repbase Update library of repeats from the Genetic Information Research Institute (GIRI). Repbase Update is described in Jurka, J. (2000) in the References section below. Display Conventions and Configuration In full display mode, this track displays up to ten different classes of repeats: Short interspersed nuclear elements (SINE), which include ALUs Long interspersed nuclear elements (LINE) Long terminal repeat elements (LTR), which include retroposons DNA repeat elements (DNA) Simple repeats (micro-satellites) Low complexity repeats Satellite repeats RNA repeats (including RNA, tRNA, rRNA, snRNA, scRNA) Other repeats, which includes class RC (Rolling Circle) Unknown The level of color shading in the graphical display reflects the amount of base mismatch, base deletion, and base insertion associated with a repeat element. The higher the combined number of these, the lighter the shading. Methods UCSC has used the most current versions of the RepeatMasker software and repeat libraries available to generate these data. Note that these versions may be newer than those that are publicly available on the Internet. Data are generated using the RepeatMasker -s flag. Additional flags may be used for certain organisms. Repeats are soft-masked. Alignments may extend through repeats, but are not permitted to initiate in them. See the FAQ for more information. Credits Thanks to Arian Smit and GIRI for providing the tools and repeat libraries used to generate this track. References Jurka J. Repbase update: a database and an electronic journal of repetitive elements. Trends Genet. 2000 Sep;16(9):418-20. PMID: 10973072