This track shows locations of Sequence Tagged Sites (STS) \ along the rat draft assembly. These markers have been mapped using \ either genetic (Rat FHH x ACI F2 Intercross Genetic Map, Rat SHRSP x BN F2 Intercross Genetic Map) or radiation hybridization (RH Map 2.2) mapping techniques.
\\ Additional data on the individual maps can be found at the following links:\
\ map 1 gold Assembly bed 3 + Assembly from Fragments 0 10 150 100 30 230 170 40 0 0 0
This track shows the draft assembly of the $organism genome.\ Instead of a clone-by-clone assembly, the Rat genome assembly is pieced\ together from Bactig assemblies, which are reassemblies of the reads from\ sets of overlapping skimmed BAC clones, including mapped whole genome\ shotgun reads. The assembly process reworks the contigs (splitting some)\ and selects them under more stringent criteria than are used for the BAC\ submissions.
\In dense display mode, this track depicts the path through the draft and finished\ contigs used to create the assembled sequence. Where gaps\ exist in the path, spaces are shown between the blocks.\
\ map 1 gap Gap bed 3 + Gap Locations 1 11 0 0 0 127 127 127 0 0 0\ 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 known, it is a bridged gap and a white line is drawn \ through the black box representing the gap. \
\There are two principal types of gaps:\
\ Instead of clone-by-clone assemblies, the Rat genome assembly is\ pieced together from Bactig assemblies, which are reassemblies of the\ reads from sets of overlapping skimmed BAC clones, including mapped\ whole genome shotgun (WGS) reads. The assembly process reworks the contigs (splitting some)\ and selects them under more stringent criteria than used for the BAC\ submissions.
\ \\ Thanks to Paul Havlak at the \ \ Baylor College of Medicine Human Genome Sequencing Center \ for providing the data.
\ \ map 1 bacEndPairs BAC End Pairs bed 6 + BAC End Pairs 0 15 0 0 0 127 127 127 0 0 0Bacterial artificial chromosomes (BACs) are a key part of many large\ scale sequencing projects. A BAC typically consists of 50-300kb of\ DNA. During the early phase of a sequencing project, it is common\ to sequence a single read (approximately 500 bases) off each end of\ a large number of BACs. Later on in the project, these BAC end reads\ can be mapped to the genome sequence. \
\This track shows these mappings\ in cases where both ends could be mapped. These BAC end pairs can\ be useful for validating the assembly over relatively long ranges. In some\ cases, the BACs are useful biological reagents. This track can also be\ used for determining which BAC contains a given gene, useful information\ for certain wet lab experiments.\ \
A valid pair of BAC end sequences must be\ at least 50Kb but no more than 600Kb away from each other. \ The orientation of the first BAC end sequence must be "+" and\ the orientation of the second BAC end sequence must be "-".
\ \BAC end sequences are placed on the assembled sequence using\ Jim Kent's \ blat \ program.
\ \Additional information about the clone, including how it\ can be obtained, may be found at the \ NCBI Clone Registry.\ To view the registry entry for a specific clone, open the details page for the clone and click on its name at the top of the page.\
\ map 1 gcPercent GC Percent bed 4 + Percentage GC in 20,000 Base Windows 0 23 0 0 0 127 127 127 1 0 0\ The GC percent track shows the percentage of G (guanine) and C (cytosine) bases in\ a 20,000 base window. Windows with high GC content are drawn more darkly than windows\ with low GC content. High GC content is typically associated with gene rich areas.\
\\ This track was generated at UCSC.\ map 1 knownGene Known Genes genePred refPep refMrna Known Genes Based on SWISS-PROT, TrEMBL, mRNA, and RefSeq 3 34 12 12 120 133 133 187 0 0 0
\ The Known Genes track shows known protein coding genes based on \ proteins from SWISS-PROT, TrEMBL, and TrEMBL-NEW and their\ corresponding mRNAs from Genbank.\ Coding exons are displayed \ taller than 5' and 3' untranslated regions (UTR). Connecting introns \ are one-pixel lines with hatch marks indicating direction of transcription.\ Entries which have corresponding entries in PDB are colored black.\ Entries which either have corresponding proteins in SWISS-PROT or mRNAs that are \ NCBI Reference Sequences with a "Reviewed" status are colored dark blue.\ Entries which have mRNAs that are \ NCBI Reference Sequences with a "Provisional" status are colored lighter blue.\ Everything else is colored with lightest blue.
\ \\ All mRNAs of a species are aligned against the genome using the blat\ program. When a single mRNA aligns in multiple places, only\ the best alignments are kept. The alignments must also have \ at least 98% sequence identity to be kept. \ This set of mRNA alignments is further reduced by keeping only those mRNAs that \ are referenced by a protein in SWISS-PROT, TrEMBL, or TrEMBL-NEW.
\\ Among multiple mRNAs referenced by a single protein, the best mRNA is chosen based on \ a quality score, which depends on its length, how good its translation matches \ the protein sequence, and its release date.\ The list of mRNA and protein pairs are further cleaned up by removing \ short invalid entries and consolidating entries with identical CDS regions.
\\ Finally, RefSeq entries which are derived from DNA sequences instead of \ mRNA sequences are added. Disease annotations are from SWISS-PROT.
\ \\ The Known Genes track is produced at UCSC based primarily on cross-references \ between proteins from \ SWISS-PROT \ (also including TrEMBL and TrEMBL-NEW) and mRNAs from Genbank\ generated by scientists worldwide. Part of \ NCBI RefSeq \ data are also included in this track.
\ \\ The SWISS-PROT entries in this annotation track are copyrighted. They are \ produced through a collaboration \ between the Swiss Institute of Bioinformatics and the EMBL Outstation - the \ European Bioinformatics Institute. There are no restrictions on their use by \ non-profit institutions as long as their content is in no way modified and this \ statement is not removed. Usage by and for commercial entities requires a \ license agreement (see \ http://www.isb-sib.ch/announce/ or send an email to \ license@isb-sib.ch).
\ \ genes 1 hgGene on\ refGene RefSeq Genes genePred refPep refMrna RefSeq Genes 0 35 12 12 120 133 133 187 0 0 0\ The RefSeq Genes track shows known protein coding genes taken from mRNA \ reference sequences compiled at LocusLink. Coding exons are represented by \ blocks connected by horizontal lines representing introns. The 5' and 3' \ untranslated regions (UTRs) are displayed as shorter blocks on the leading \ and trailing ends of the aligning regions. In full display mode, arrowheads \ on the connecting intron lines indicate the direction of transcription.\
\\ Non-coding RNA genes have their own track in some assemblies.\
\\ Refseq mRNAs are aligned against the genome using the blat\ program. When a single mRNA aligns in multiple places, only\ the best alignments which also have at least 98% sequence identity are kept.\
\The track filter can be used to configure the labeling of the features within\ the track. 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.\ After you have made your selection, click Submit to return to the tracks display\ page.\
\ The RefSeq Genes track is produced at UCSC from mRNA sequence data\ generated by scientists worldwide and curated by the \ NCBI RefSeq project. \
\ genes 1 twinscan Twinscan genePred twinscanPep Twinscan Gene Predictions Using Rat/Human Homology 1 45 0 100 100 0 50 50 0 0 0\ The Twinscan program predicts genes in a manner similar to Genscan, except that\ Twinscan takes advantage of genome comparison to improve gene prediction\ accuracy. More information and a web server can be found at\ \ http://genes.cs.wustl.edu/. \
\\
The Twinscan algorithm is described in Korf I, Flicek P, Duan D, and Brent MR \
(2001), "
\ Thanks to Michael Brent's Computational Genomics Group at Washington University St. Louis for providing these data.\ genes 1 slamHuman Slam Human genePred Slam Gene Predictions Using Human/Rat Homology 0 45.5 100 50 0 175 150 128 0 0 0
\ Slam \ predicts coding exons and conserved noncoding regions in a pair of homologous \ DNA sequences, incorporating both statistical sequence properties and degree of \ conservation in making the predictions. The model is symmetric and the same \ gene structure (with possibly different exon lengths) is predicted in both \ sequences.
\\ The symmetry of the model gives it a higher degree of accuracy for regions where \ the true underlying gene structures contain the same number of coding exons, in \ cases where this is not true, or when one of the sequences is of lower quality \ and contains in-frame stop codons, the resulting predictions tend to have lower \ accuracy.
\ \\ More information on the accuracy of the predictions can be found at http://bio.math.berkeley.edu/slam/mouse. A web server for individual requests is available at http://bio.math.berkeley.edu/slam.
\ \\ M. Alexandersson, S. Cawley, L. Pachter (2003). SLAM - Cross-species Gene Finding and Alignment with a Generalized Pair Hidden Markov Model. Genome Research 13(3):496-502.
\\ L. Pachter, M. Alexandersson, S. Cawley (2001). \ Applications of Generalized Pair Hidden Markov Models to Alignment and Gene Finding Problems, \ Proceedings of the Fifth Annual International Conference on Computational Molecular Biology (RECOMB 2001).
\\ L. Pachter , M. Alexandersson, S. Cawley (2002). \ Applications of Generalized Pair Hidden Markov Models to Alignment and Gene Finding Problems, \ Journal of Computational Biology 9(2):389-400.
\ genes 1 sgpGene SGP Genes genePred sgpPep SGP Gene Predictions Using Rat/Human Homology 0 47 0 90 100 127 172 177 0 0 0\ This track shows gene predictions from the SGP program, which is being developed at \ the Grup de Recerca en\ Informàtica Biomèdica (GRIB) at Institut Municipal d'Investigació Mèdica (IMIM) in \ Barcelona. To predict genes in a genomic\ query, SGP combines geneid predictions with tblastx comparisons of the genomic query against other genomic sequences. In this particular annotation, the Jun. 2002\ (hg12) human assembly was used to find homology evidence between the two genomes.\ \ \ \ \ \ genes 1 softberryGene Fgenesh++ Genes genePred softberryPep Fgenesh++ Gene Predictions 1 48 0 100 0 127 177 127 0 0 0
Fgenesh++ predictions are based on Softberry's gene finding software.
\ \The Fgenesh++ gene predictions were produced by \ Softberry Inc. \ Commercial use of these predictions is restricted to viewing in \ this browser. Please contact Softberry Inc. to make arrangements for further commercial access.\ \ genes 1 geneid Geneid Genes genePred geneidPep Geneid Gene Predictions 0 49 0 90 100 127 172 177 0 0 0
\ This track shows gene predictions from the geneid program developed at the \ Grup de Recerca en\ Informàtica Biomèdica (GRIB) at Institut Municipal d'Investigació Mèdica (IMIM) in \ Barcelona. \
\\ Geneid is a program to predict genes in anonymous genomic sequences designed \ with a hierarchical structure. In the first step, splice sites, start and stop \ codons are predicted and scored along the sequence using Position Weight Arrays \ (PWAs). Next, exons are built from the sites. Exons are scored as the sum of the \ scores of the defining sites, plus the the log-likelihood ratio of a \ Markov Model for coding DNA. Finally, from the set of predicted exons, the gene \ structure is assembled, maximizing the sum of the scores of the assembled exons. \
\\ Thanks to GRIB for providing these data.\
\ genes 1 genscan Genscan Genes genePred genscanPep Genscan Gene Predictions 1 50 170 100 0 212 177 127 0 0 0This track shows predictions from the \ Genscan program written by Chris Burge.\
\Method
\
NCBI RefSeq's from curated RGD gene records were aligned against the genome \
using the \
blat \
algorithm. In cases where genes align at more than one location\
the best alignment was used.
Credits
\
The RGD Genes track is produced at UCSC from sequence data generated by the\
research community and curated at the Rat\
Genome Database at the Medical College of Wisconsin.
\
\ The $Organism mRNA track shows alignments between $organism mRNAs\ in Genbank and the genome. Aligning regions (usually exons)\ are shown as black boxes connected by lines for gaps (spliced\ out introns usually). In full display, arrows on the introns\ indicate the direction of transcription.
\\ Genbank $organism mRNAs are aligned against the genome using the \ blat\ program. When a single mRNA aligns in multiple places, \ the alignment having the highest base identity is found. \ Only alignments that have a base identity level within 1% of\ the best are kept. Alignments must also have at least 95%\ base identity to be kept.
\ \The track filter can be used to change the color or include/exclude a subset of individual \ items within a track. This is helpful when many items are shown in the track\ display, especially when only some are relevant to the current task. To use the\ filter:\
\ When you have finished configuring the filter, click the Submit button.
\ \\ The $Organism mRNA track is produced at UCSC from mRNA sequence data\ submitted to the international public sequence databases by \ scientists worldwide.
\ rna 1 intronGap 30\ intronEst Spliced ESTs psl est $Organism ESTs That Have Been Spliced 1 56 0 0 0 127 127 127 1 0 0The Spliced EST track displays Expressed Sequence Tags \ (ESTs) from Genbank that show signs of splicing when\ aligned against the genome. 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 \ $Organism EST track.
\ \Expressed sequence tags are single read (typically\ approximately 500 base) sequences which usually\ represent fragments of transcribed genes. Aligning \ regions (usually exons) are shown as black boxes \ connected by lines for gaps (usually spliced out introns). \ In full display mode, arrows on the introns\ indicate the direction of transcription. In the\ December 2001 assembly and later, this direction is\ taken by looking at the splice sites. In previous\ assemblies, the direction of transcription was taken from \ the Genbank annotations, which frequently were inaccurate.
\ \Strand information provided for ESTs (+/-) 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.\ \
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 taken from the 5'\ and/or 3' primer. For most - but not all - 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 are starting to hit transcription start\ reasonably well. Before the cap-selection techniques\ emerged, some projects used random rather than poly-A\ priming in an attempt to get 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. However, because the $organism 3' UTRs are quite\ long, the splicing requirement does eliminate many genuine 3'\ ESTs.
\ \To generate this track, $organism ESTs from Genbank are aligned \ against the genome using the \ blat \ program. Note that the maximum intron length\ allowed by blat is 500,000 bases, which may eliminate some ESTs with very \ long introns that might otherwise align. When a single \ EST aligns in multiple places, the alignment having the \ highest base identity is found. Only alignments that have \ a base identity level within 1% of the best are kept. \ Alignments must also have at least 93% base identity to be kept.
\ \The track filter can be used to change the color or include/exclude a subset of \ individual items within a track. This is helpful when many items are shown in the \ track display, especially when only some are relevant to the current task. To use the\ filter:\
\ When you have finished configuring the filter, click the Submit button.Credits\
\ The Spliced EST track is produced at UCSC from EST sequence data\ submitted to the international public sequence databases by \ scientists worldwide.
\ rna 1 intronGap 30\ est $Organism ESTs psl est $Organism ESTs Including Unspliced 0 57 0 0 0 127 127 127 1 0 0\ This track shows alignments between $organism Expressed\ Sequence Tags (ESTs) in Genbank and the genome.
\ \Expressed sequence tags are single read (typically\ approximately 500 base) sequences which usually\ represent fragments of transcribed genes. Aligning \ regions (usually exons) are shown as black boxes \ connected by lines for gaps (usually spliced out introns). \ In full display mode, arrows on the introns\ indicate the direction of transcription. In the\ December 2001 assembly and later, this direction is\ taken by looking at the splice sites. In previous\ assemblies, the direction of transcription was taken from \ the Genbank annotations, which frequently were inaccurate.\
\ \Strand information provided for ESTs (+/-) 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.\ \
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 taken from the 5'\ and/or 3' primer. For most - but not all - 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 are starting to hit transcription start\ reasonably well. Before the cap-selection techniques\ emerged, some projects used random rather than poly-A\ priming in an attempt to get 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. However, because the $organism 3' UTRs are quite\ long, the splicing requirement does eliminate many genuine 3'\ ESTs.
\ \To generate this track, $organism ESTs from Genbank are aligned \ against the genome using the \ blat \ program. Note that the maximum intron length\ allowed by blat is 500,000 bases, which may eliminate some ESTs with very \ long introns that might otherwise align. When a single \ EST aligns in multiple places, the alignment having the \ highest base identity is found. Only alignments that have \ a base identity level within 1% of the best are kept. \ Alignments must also have at least 93% base identity to be kept.
\ \The track filter can be used to change the color or include/exclude a subset of \ individual items within a track. This is helpful when many items are shown in the \ track display, especially when only some are relevant to the current task. To use the\ filter:\
\ When you have finished configuring the filter, click the Submit button.
\ \\ The $Organism EST track is produced at UCSC from EST sequence data\ submitted to the international public sequence databases by \ scientists worldwide.
\ rna 1 intronGap 30\ xenoMrna Non$Organism mRNAs psl xeno Non$Organism mRNAs from Genbank 1 63 0 0 0 127 127 127 1 0 0\ This track displays translated \ blat\ alignments of\ non-$organism vertebrate and invertebrate mRNA from Genbank.
\ \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 (+ or -).\ \ \
\ The alignments were passed through a near-best-in-genome filter.
\ \The track filter can be used to color, include, or exclude a subset of individual \ items within a track. This is helpful when many items are shown in the track\ display, especially when only some are relevant to the current task. To use the\ filter:\
When you have finished configuring the filter, click the Submit button.
\ rna 1 tigrGeneIndex TIGR Gene Index genePred Alignment of TIGR Gene Index TCs Against the $Organism Genome 0 68 100 0 0 177 127 127 0 0 0 http://www.tigr.org/tigr-scripts/tgi/tc_report.pl?$$This track displays alignments of the TIGR Gene Index (TGI)\ against the $organism genome. The TIGR Gene Index is based\ largely on assemblies of EST sequences in the public databases.\ See \ www.tigr.org for more information about TIGR and the Gene Index.
\Thanks to Foo Cheung for converting these data into a track\ for the browser.
\ rna 1 rnaCluster Gene Bounds bed 12 Gene Boundaries as Defined by RNA and Spliced EST Clusters 0 71 200 0 50 227 127 152 0 0 0\ This track shows the boundaries of genes and the direction of\ transcription as deduced from clustering spliced ESTs and mRNAs\ against the genome. When there are many spliced variants\ of the same gene, this track shows the variant that\ spans the greatest distance in the genome.
\ \\ ESTs and mRNAs from Genbank are aligned against the genome\ with the \ blat\ program, and filtered to keep only those alignments\ that have at least 97.5% base identity within the \ aligning blocks. When multiple alignments occur, only the\ alignments with a percentage identity within 0.2% of the\ best alignment are kept. ESTs that align without any\ introns are discarded. Blocks that are less than 130 bases\ and are not next to an intron are discarded. Blocks smaller\ than 10 bases are discarded. The orientations of the \ ESTs and mRNAs are deduced from the GT/AG splice sites\ at the introns, and ESTs and mRNAs with overlapping blocks\ on the same strand are merged into clusters. Only the\ extent and orientation of the clusters are shown here.\
\\ This track was generated at UCSC by Jim Kent using data\ submitted to Genbank by scientists worldwide.
\ rna 1 cpgIsland CpG Islands bed 4 + CpG Islands (Islands < 300 Bases are Light Green) 0 76 0 100 0 128 228 128 0 0 0\ 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 C's 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 C's tend to turn into T's because of spontaneous\ deamination. The result is that CpG's are relatively rare unless\ there is selective pressure to keep them or a region is not methylated\ for some reason, perhaps having to do with the regulation of gene\ expression. CpG islands are regions where CpG's are present at\ significantly higher levels than is typical for the genome as a whole.\
\ \\ CpG islands are 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 is then\ evaluated to determine GC content (>=50%), length (>200), and ratio of\ observed proportion of CG dinucleotides to the expected proportion on\ the basis of the GC content of the segment (>0.6). \
\ \\ This track was generated \ using a\ modification of a program developed by G. Miklem and L. Hillier. \
\ \ regulation 1 blatFugu Fugu Blat psl xeno Takifugu rubripes Translated Blat Alignments 1 113 0 60 120 200 220 255 1 0 0\ The Fugu v.3.0 (Aug. 2002) whole genome shotgun assembly was provided by the\ US \ DOE Joint Genome Institute (JGI). The assembly was constructed with the JGI\ assembler, JAZZ, from paired end sequencing reads produced at JGI, Myriad \ Genetics, and Celera Genomics, resulting in a sequence coverage of 5.7X. All reads are\ plasmid, cosmid, or BAC end sequences, with the predominant coverage\ derived from 2 Kb insert plasmids. This assembly contains 20,379\ scaffolds totaling 319 million base pairs. The largest 679 scaffolds\ total 160 million base pairs.\ \
The alignments were done \ with \ blat \ in translated protein mode requiring 2 nearby 4-mer matches\ to trigger a detailed alignment. The $organism\ genome was masked with \ RepeatMasker and \ Tandem Repeats\ Finder \ before running blat.
\ \The 3.0 draft from the\ \ JGI Fugu rubripes website was used in the\ UCSC Genome Browser Fugu blat alignments. These data have been provided freely by the JGI\ for use in this publication only.
\ compGeno 1 slamNonCodingHuman Slam Non Coding Human bed 5 Slam Predictions of Human/Rat Conserved Non-Coding Regions 0 120 30 130 210 200 220 255 1 0 0\ Slam \ predicts coding exons and conserved noncoding regions in a pair of\ homologous DNA sequences, incorporating both statistical sequence properties\ and degree of conservation into predictions. The model is symmetric and the\ same structure (with possibly different lengths) is predicted in both\ sequences.
\\ The CNS (conserved non-coding sequence) predictions are ab-initio\ predictions of conserved regions that do not fit in with a gene structure.\ Thus, slam is not simply trying to predict conserved regions to be coding,\ but is classifying such regions according to an overall probabilistic model\ of gene structure. The set of slam CNS predictions is therefore highly\ enriched for conserved non-coding regions.
\\
More information and a web server can be found at
\ M. Alexandersson, S. Cawley, L. Pachter (2003). SLAM - Cross-species Gene Finding and Alignment with a Generalized Pair Hidden Markov Model. Genome Research 13(3):496-502.
\\ L. Pachter, M. Alexandersson, S. Cawley (2001). \ Applications of Generalized Pair Hidden Markov Models to Alignment and Gene Finding Problems, \ Proceedings of the Fifth Annual International Conference on Computational Molecular Biology (RECOMB 2001).
\\ L. Pachter , M. Alexandersson, S. Cawley (2002). \ Applications of Generalized Pair Hidden Markov Models to Alignment and Gene Finding Problems, \ Journal of Computational Biology 9(2):389-400.
\ \ \ \ compGeno 1 rmsk RepeatMasker rmsk Repeating Elements by RepeatMasker 1 147 0 0 0 127 127 127 1 0 0\
This track was created by Arian Smit's
\ This track displays simple tandem repeats (possibly imperfect) located\ by Tandem Repeats\ Finder, which is specialized to this purpose. These repeats can\ occur within coding regions of genes and may be quite\ polymorphic. Repeat expansions are sometimes associated with specific\ diseases.
\ \\ For more information about the Tandem Repeats Finder, see G. Benson, "Tandem repeats finder: a program to analyze DNA sequences", Nucleic Acids \ Research, 1999, 27(2) 573-580.
\ \\ Tandem Repeats Finder was written by Dr. Gary Benson.
\ \ varRep 1