catLiftOffGenesV1 CAT/Liftoff Genes CAT + Liftoff Gene Annotations genes Description This track represents the gene models for the T2T CHM13 assembly generated using the CAT (Compartive Annotation Toolkit) software with genes that CAT could not be mapped as well as novel paralogs, filled in from the LiftOff mappings. The reference annotations are from GENCODE V35. Display Conventions and Configuration This track follows the display conventions for gene prediction tracks. The exons for putative non-coding genes and untranslated regions are represented by relatively thin blocks, while those for coding open reading frames are thicker. Gene names are displayed in 'pack' or 'full' mode. More information about each gene can be found by clicking on the specific gene/transcript model. The following color key is used: Blue: protein coding Green: non-coding Purple: novel predictions from PacBio Iso-Seq and LiftOff --> Pink: pseudogenes Methods This tracks combines gene annotations generated by two methods. First the Comparative Annotation Toolkit (CAT) was used to Liftoff was then used as a second annotation method to map genes missed by CAT and additional gene paralogs. Comparative Annotation Toolkit Genome annotation for T2T CHM13 assembly was performed using Comparative Annotation Toolkit (CAT). CAT leverages whole-genome alignments generated by Cactus to transfer annotations from one source genome to one or more target genomes. For this annotation set, CAT lifted over the reference GENCODE v35 annotations onto the T2T genome. CAT also incorporated Iso-Seq data, first assembled into transcripts with StringTie2, to make the final consensus annotation set. Liftoff Liftoff uses Minimap2 to align reference gene DNA sequences to the target genome and selects the alignment(s) concordant with the intron/exon structure with the highest sequence identity. A minimum sequence identity of 95% was required to annotate gene paralogs. After running Liftoff, we identified genes that did not overlap any CAT annotations using bedtools intersect. These were combined with the CAT annotation to create the final annotation. Credits This track was provide by Marina Haukness <mhauknes@ucsc.edu> of UC Santa Cruz and Alaina Shumate <ashumat2@jhmi.edu> of Johns Hopkins University. References Fiddes IT, Armstrong J, Diekhans M, Nachtweide S, Kronenberg ZN, Underwood JG, Gordon D, Earl D, Keane T, Eichler EE et al. Comparative Annotation Toolkit (CAT)-simultaneous clade and personal genome annotation. Genome Res. 2018 Jul;28(7):1029-1038. PMID: 29884752; PMC: PMC6028123 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, Steinkamp R, Waack S, Morgenstern B. AUGUSTUS: a web server for gene finding in eukaryotes. Nucleic Acids Res. 2004 Jul 1;32(Web Server issue):W309-12. PMID: 15215400; PMC: PMC441517 Armstrong J, Hickey G, Diekhans M, Fiddes IT, Novak AM, Deran A, Fang Q, Xie D, Feng S, Stiller J et al. Progressive Cactus is a multiple-genome aligner for the thousand-genome era. Nature. 2020 Nov;587(7833):246-251. PMID: 33177663; PMC: PMC7673649 Shumate A, Salzberg SL. Liftoff: accurate mapping of gene annotations. Bioinformatics. 2020 Dec 15;. PMID: 33320174; PMC: PMC8289374 
cactus Cactus Alignment Cactus Alignment compGeno Description Cactus reference-free alignments of GRCh38 and T2T CHM13 v2.0, using chimp (GCF_002880755.1/panTro6) as an out-group. Display Conventions and Configuration This track uses the Snake tracks display conventions and configuration. Methods Alignments were generated using the Cactus reference alignment package and are stored as a HAL file. Credits This track was created by Marina Haukness <mhauknes@ucsc.edu> if the UC Santa Cruz Computational Genomics Lab. References Armstrong J, Hickey G, Diekhans M, Fiddes IT, Novak AM, Deran A, Fang Q, Xie D, Feng S, Stiller J et al. Progressive Cactus is a multiple-genome aligner for the thousand-genome era. Nature. 2020 Nov;587(7833):246-251. PMID: 33177663; PMC: PMC7673649 Hickey G, Paten B, Earl D, Zerbino D, Haussler D. HAL: a hierarchical format for storing and analyzing multiple genome alignments. Bioinformatics. 2013 May 15;29(10):1341-2. PMID: 23505295; PMC: PMC3654707 
cactusAlignments Alignments Cactus Alignment compGeno 
snakehs1 CHM13/hs1 Cactus CHM13/hs1 compGeno 
snakeHg38 hg38 Cactus GRCh37/hg38 compGeno 
censat CenSat Annotation Centromeric Satellite Annotation map Description Centromeric and Pericentromeric Satellite Annotation (cenSat) Methods Satellite array annotations are defined by intersecting information across alpha HOR annotation track, repeatmasker tracks, and human satellite annotation tracks. The broad definition of "peri/centromeric regions" on each chromosome includes the satellite-rich regions and 5 Mb of sequence on the p-arm and q-arm. Although the distal ends of acrocentric short arms are not truly pericentromeric, the vast majority of satellite DNAs present in these arms are highly enriched in peri/centromeric regions on other chromosomes (e.g. HSat1-3, Beta satellites (βSat), Alpha satellites (αSat)). Therefore, acrocentric short arms are included in the cenSat annotation track in their entirety. The Y chromosome peri/centromeric region includes 5 Mb to either side of the active αSat Higher Order Repeat (HOR) array, but we have included satellite array annotations across the entire chromosome. NB: Satellite array annotations typically merge across inserted transposons. Strand information is not included (all annotations are set to + strand) Display Conventions and Configuration Colors Active αSat HOR (hor ... L) red Inactive αSat HOR (hor) orange Divergent αSat HOR (dhor) dark red Monomeric αSat (mon) peach/yellow Classical Human Satellite 1A (hsat1A) light green Classical Human Satellite 1B (hsat1B) dark green Classical Human Satellite 2 (hsat2) light blue Classical Human Satellite 3 (hsat3) blue Beta Satellite (bsat) pink Gamma Satellite (gsat) purple Other centromeric satellites (censat) teal Centromeric transition regions (ct) grey Credits Karen Miga <khmiga@ucsc.edu>, Nicolas Altemose, Ivan A. Alexandrov References Altemose N, Logsdon GA, Bzikadze AV, Sidhwani P, Langley SA, Caldas GV, Hoyt SJ, Uralsky L, Ryabov FD, Shew CJ et al. Complete genomic and epigenetic maps of human centromeres. Science. 2022 Apr;376(6588):eabl4178. PMID: 35357911; PMC: PMC9233505 
refSeqComposite NCBI RefSeq RefSeq gene predictions from NCBI genes Description The NCBI RefSeq Genes composite track shows 24 Jan 2022 Homo sapiens/GCF_009914755.1_T2T-CHM13v2.0 protein-coding and non-protein-coding genes taken from the NCBI RNA reference sequences collection (RefSeq). All subtracks use coordinates provided by RefSeq. 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 To show only a selected set of subtracks, uncheck the boxes next to the tracks that you wish to hide. The tracks available here can include (not all may be present): RefSeq annotations and alignments RefSeq All – all curated and predicted annotations provided by RefSeq. RefSeq Curated – 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.) RefSeq Predicted – subset of RefSeq All that includes those annotations whose accessions begin with XM or XR. RefSeq Other – 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. RefSeq Alignments – alignments of RefSeq RNAs to the 24 Jan 2022 Homo sapiens/GCF_009914755.1_T2T-CHM13v2.0 genome provided by the RefSeq group. The RefSeq All, RefSeq Curated and RefSeq Predicted, 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 RefSeq Alignments track follows the display conventions for PSL tracks. The item labels and codon display properties for features within this track can be configured through the 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. Methods The RefSeq annotation and RefSeq RNA alignment tracks were created at UCSC using data from the NCBI RefSeq project. GFF format data files were downloaded from the file GCF_009914755.1_T2T-CHM13v2.0_genomic.gff.gz delivered with the NCBI RefSeq genome assemblies at the FTP location: ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/009/914/755/GCF_009914755.1_T2T-CHM13v2.0/ The GFF file was converted to the genePred and PSL table formats for display in the Genome Browser. Information about the NCBI annotation pipeline can be found here. Track statistics summary Total genome size: 3,117,292,070 bases Curated and Predicted Gene count: 108,944 Bases in these genes: 1,612,562,606 Percent genome coverage: % 51.730 Curated gene count: 82,572 Bases in curated genes: 1,377,848,543 Percent genome coverage: % 44.200 Predicted gene count: 26,372 Bases in genes: 287,621,756 Percent genome coverage: % 9.227 Other annotation count: 16,326 Bases in other annotations: 32,222,985 Percent genome coverage: % 1.034 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 
ncbiRefSeqGenomicDiff RefSeq Diffs Differences between NCBI RefSeq Transcripts and the Reference Genome genes 
ncbiRefSeqPsl RefSeq Alignments RefSeq Alignments of RNAs genes 
ncbiRefSeqOther RefSeq Other NCBI RefSeq other annotations (not NM_*, NR_*, XM_*, XR_*, NP_* or YP_*) genes 
ncbiRefSeqPredicted RefSeq Predicted NCBI RefSeq genes, predicted subset (XM_* or XR_*) genes 
ncbiRefSeqCurated RefSeq Curated NCBI RefSeq genes, curated subset (NM_*, NR_*, NP_* or YP_*) genes 
ncbiRefSeq RefSeq All NCBI RefSeq genes, curated and predicted sets (NM_*, XM_*, NR_*, XR_*, NP_* or YP_*) genes 
hgUnique CHM13 unique CHM13 unique in comparison to GRCh38/hg38 and GRCh37/hg19 map Description These tracks show the regions unique to the T2T-CHM13 v2.0 assembly compared to the GRCh38/hg38 and GRCh37/hg19 reference assemblies. Methods Converting a chain file to the PAF format We used the `to_paf.py` script from chaintools (https://doi.org/10.5281/zenodo.6342391, v0.1) to convert the v1_nfLO chains to the PAF format. Obtaining unique regions We used the follwing commands to obtain the regions unique to GRCh38/hg38 and GRCh37/hg19 in the BED format. cut -f 1,3,4 grch38-chm13v2.paf \ | bedtools sort -i - -g chm13v2.0.fasta.fai \ | bedtools merge \ | bedtools complement -g chm13v2.0.fasta.fai -i - \ | bedtools merge \ > T2T-CHM13v2.0_unique_regions_hg38.bed cut -f 1,3,4 hg19-chm13v2.paf | bedtools sort -i - -g chm13v2.0.fasta.fai \ | bedtools merge \ | bedtools complement -g chm13v2.0.fasta.fai -i - \ | bedtools merge \ > T2T-CHM13v2.0_unique__regions_hg19.bed Credits The unique region annotations were generated by Nae-Chyun Chen<naechyun.chen@gmail.com> and Mitchell Vollger<mvollger@uw.edu> References Nurk S, Koren S, Rhie A, Rautiainen M, et al. The complete sequence of a human genome. bioRxiv, 2021. 
hgUniquehg19 CHM13 unique for hg19 CHM13 unique in comparison to GRCh37/hg19 compGeno 
hgUniqueHg38 CHM13 unique for hg38 CHM13 unique in comparison to GRCh38/hg38 compGeno 
rdnaModel rDNA models Consensus rDNA models map Description Extent of consensus rDNA models, within which small variants between duplicate copies may be lost. Copy number of the units within each model are estimates, but the order of the models is arbitrary and does not reflect the true structure within the larger arrays. Credits Track supplied by Adam Phillippy. References Nurk S, Koren S, Rhie A, Rautiainen M, et al. The complete sequence of a human genome. bioRxiv, 2021. 
proseq CHM13 PROseq CHM13 PROseq stranded with unique genome-wide kmer filtering rna Description CHM13 PRO-seq (Precision Run-On sequencing) Bowtie2 alignments to CHM13v2.0 (minus chrY) and unique genome-wide 21mer filtering (stranded) PRO-seq detects nascent transcription (including from non-coding) from RNA polymerases with nucleotide resolution at genome-scale. Methods The PRO-seq experiment was done on CHM13 cells (in duplicate, A and B) and sequenced from the 3' end for 75bp single-ended reads. Reads were adapter trimmed, quality filtered (-q 20), and length filtered (-m 20) with Cutadapt (v2.7). Trimmed reads were then reverse complemented since they were sequenced in the 3'-->5' direction. D. melanogaster spike-ins were removed with Bowtie2 (v2.3.5.1) and samtools view -f 4 (v1.9). Reads were then mapped with either Bowtie2 (v2.3.5.1) default or -k 100 (allowing up to 100 multi-mappers). Unique genome-wide 21mers were generated through Meryl (https://github.com/marbl/meryl). The reads mapped with -k 100 were filtered with these unique genome-wide 21mers through one of two methods: Locus-specific unique genome-wide 21mer filtering (overlapSelect -overlapBases=21; UCSC tools (GenomeBrowser/20180626)) Read- and locus-specific unique genome-wide 21mer filtering (https://github.com/arangrhie/T2T-Polish/tree/master/marker_assisted, overlapSelect -overlapBases=21) Display Conventions and Configuration The tracks labeled as "neg" are negated for viewing. Data access Raw PRO-seq data filled under BioProject PRJNA559484 Release history CHM13v2.0 assembly (minus chrY) Credits Savannah Hoyt <savannah.klein@uconn.edu> (PROseq library prep & sequencing; mapping & locus-specific filtering) Rachel O'Neill <rachel.oneill@uconn.edu> Arang Rhie <rhiea@nih.gov> (For generation of unique 21mers & read-specific filtering) References For generation of unique genome-wide 21mers: T2T-Polish GitHub repository Mc Cartney, A.M., et al. Chasing perfection: validation and polishing strategies for telomere-to-telomere genome assemblies. bioRxiv 2021 For the PRO-seq experimental, mapping, and filtering methods: Hoyt SJ, et al. From telomere to telomere: the transcriptional and epigenetic state of human repeat elements. bioRxiv. 2021 Hoyt SJ, et al. From telomere to telomere: the transcriptional and epigenetic state of human repeat elements analysis code: T2T-CHM13. bioRxiv. 2021 
proseq_k100-dual-21mer_POS PROseq k100-dual-21mer POS PROseq k100 POS dual 21mer filtering rna 
proseq_k100-dual-21mer_NEG PROseq k100-dual-21mer NEG PROseq k100 NEG dual 21mer filtering rna 
proseq_k100-21mer_POS PROseq k100-21mer POS PROseq k100 POS 21mer filtering rna 
proseq_k100-21mer_NEG PROseq k100-21mer NEG PROseq k100 NEG 21mer filtering rna 
proseq_k100_POS PROseq k100 POS PROseq k100 POS no kmer filtering rna 
proseq_k100_NEG PROseq k100 NEG PROseq k100 NEG no kmer filtering rna 
proseq_default_POS PROseq default POS PROseq default POS no kmer filtering rna 
proseq_default_NEG PROseq default NEG PROseq default NEG no kmer filtering rna 
rnaseq CHM13 RNA-Seq CHM13 RNA-Seq (paired-end) unique genome-wide kmer filtering (unstranded) rna Description CHM13 RNA-seq Bowtie2 alignments to CHM13v2.0 (minus chrY) and unique genome-wide 21mer filtering (unstranded) Methods Poly-A+ RNA-seq was performed on CHM13 in duplicate (A and B) and sequenced with 150-bp paired-ended reads. Reads were adapter trimmed, quality filtered (-q 20), and length filtered (-m 100) with Cutadapt (v2.7). Reads were then mapped with either Bowtie2 (v2.3.5.1) default or -k 100 (allowing up to 100 multi-mappers) and then filtered with samtools view F1548. Unique genome-wide 21mers were generated through Meryl (https://github.com/marbl/meryl). The reads mapped with -k 100 were filtered with these unique genome-wide 21mers through one of two methods: Locus-specific unique genome-wide 21mer filtering (overlapSelect -overlapBases=21; UCSC tools (GenomeBrowser/20180626)) Read- and locus-specific unique genome-wide 21mer filtering ( https://github.com/arangrhie/T2T-Polish/tree/master/marker_assisted overlapSelect -overlapBases=21) Display Conventions and Configuration Data access Raw RNA-seq data filled under BioProject PRJNA559484 Release history CHM13v2.0 assembly (minus chrY) Credits Megan Dennis <mydennis@ucdavis.edu> (RNA-seq library prep & sequencing) Savannah Hoyt <savannah.klein@uconn.edu> (PROseq library prep & sequencing; mapping & locus-specific filtering) Rachel O'Neill <rachel.oneill@uconn.edu> Arang Rhie <rhiea@nih.gov> (For generation of unique 21mers & read-specific filtering) References For generation of unique genome-wide 21mers: T2T-Polish GitHub repository Mc Cartney, A.M., et al. Chasing perfection: validation and polishing strategies for telomere-to-telomere genome assemblies. bioRxiv 2021 For the RNA-seq experimental methods: Altemose, N, et al. Genetic and epigenetic maps of endogenous human centromeres. bioRxiv. 2021 Jul. For the RNA-seq mapping and filtering methods: Hoyt SJ, et al. From telomere to telomere: the transcriptional and epigenetic state of human repeat elements. bioRxiv. 2021 Hoyt SJ, et al. From telomere to telomere: the transcriptional and epigenetic state of human repeat elements analysis code: T2T-CHM13. bioRxiv. 2021 
rnaseq_k100-dual-21mer RNA-Seq k100 dual 21mer RNA-Seq k100 dual 21mer filtering rna 
rnaseq_k100-21mer RNA-Seq k100 21mer RNA-Seq k100 21mer filtering rna 
rnaseq_k100 RNA-Seq k100 RNA-Seq k100 no kmer filtering rna 
rnaseq_default RNA-Seq default RNA-Seq default no kmer filtering rna 
assembly Assembly Assembly from NCBI Genbank Sequences map Description This track shows the sequences used in the 24 Jan 2022 Homo sapiens/GCA_009914755.4_T2T-CHM13v2.0 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: GCA_009914755.4_T2T-CHM13v2.0.agp.gz 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. This assembly has 25 component parts, with the following principal types of parts: O - other sequence (count: 25) 
clinVar20220313 ClinVar Variants ClinVar Variants 20220313 (lifted) phenDis Description The March 13th, 2022 release of ClinVar (ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/weekly/) was lifted over from GRCh38 to the T2T-CHM13v2.0 assembly. Only variants on the primary assemblies for Chromosomes 1-22, Chromsome X and Chromosome Y were lifted over. This track contains ClinVar variants that lifted over from GRCh38 to the T2T-CHM13 assembly. This includes variants that lifted over perfectly, as well as variants that failed initial liftover due to reference/alternative allele swaps but were recovered on subsequent liftover, with reference and alternative alleles swapped appropriately. These two sets of variants are included together in this track. If you are interested in downloading these sets separately (i.e., variants that lifted over perfectly vs. recovered variants with ref/alt allele swaps) they can be accessed here: https://s3-us-west-2.amazonaws.com/human-pangenomics/index.html?prefix=T2T/CHM13/assemblies/annotation/liftover/. Methods We performed liftover using the GATK release 4.1.9 LiftoverVcf (Picard Version 2.23.3) tool with the default parameters. This successfully lifts over variants that map exactly from GRCh38 to T2T-CHM13v2.0 but does not recover variants with swapped reference and alternative alleles. To recover variants with swapped reference/alternative alleles, we ran LiftoverVCF again, with the RECOVER_SWAPPED_REF_ALT flag. Notably, this feature does not recover multiallelic variants, so to recover these variants, we first separated them into multiple biallelic variants, performed liftover using the RECOVER_SWAPPED_REF_ALT tag, and converted them back to their multiallelic representations. Contacts Dylan Taylor <dtaylo95 at jhu.edu> Rajiv McCoy <rmccoy22 at jhu.edu> References Van der Auwera GA & O'Connor BD. (2020). Genomics in the Cloud: Using Docker, GATK, and WDL in Terra (1st Edition). O'Reilly Media. 
cpgIslands CpG Islands CpG Islands (Islands < 300 Bases are Light Green) regulation Description This track shows the CpG annotations on the 24 Jan 2022 Homo sapiens/GCA_009914755.4_T2T-CHM13v2.0 genome assembly. 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. 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. CpG item counts masked sequence: item count: 30,616 bases covered: 25,842,223 unmasked sequence: item count: 52,408 bases covered: 43,007,100 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 
cpgIslandExtUnmasked Unmasked CpG CpG Islands on All Sequence (Islands < 300 Bases are Light Green) regulation 
cpgIslandExt CpG Islands CpG Islands (Islands < 300 Bases are Light Green) 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. 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 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. 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 
crisprHs1 CRISPR Targets CRISPR/Cas9 -NGG Targets, whole genome genes Description This track shows the DNA sequences targetable by CRISPR RNA guides using the Cas9 enzyme from S. pyogenes (PAM: NGG) over the entire human (hs1) genome. CRISPR target sites were annotated with predicted specificity (off-target effects) and predicted efficiency (on-target cleavage) by various algorithms through the tool CRISPOR. Sp-Cas9 usually cuts double-stranded DNA three or four base pairs 5' of the PAM site. Display Conventions and Configuration The track &quot;CRISPR Targets&quot; shows all potential -NGG target sites across the genome. 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 available 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. For storage and performance reasons, on the level of individual off-targets, only CFD scores are available. 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 The entire human (hs1) genome was scanned 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. Note that the Doench 2016 scores were updated by the Broad institute in 2017 (&quot;Azimuth&quot; update). As a result, earlier versions of the track show the old Doench 2016 scores and this version of the track shows new Doench 2016 scores. Old and new scores are almost identical, they are correlated to 0.99 and for more than 80% of the guides the difference is below 0.02. However, for very few guides, the difference can be bigger. In case of doubt, we recommend the new scores. Crispor.org can display both scores and many more with the &quot;Show all scores&quot; link. Data Access Positional data can be explored interactively with the Table Browser or the Data Integrator. For small programmatic positional queries, the track can be accessed using our REST API. For genome-wide data or automated analysis, CRISPR genome annotations can be downloaded from our download server as a bigBedFile. The files for this track are called crispr.bb, which lists positions and scores, and crisprDetails.tab, which has information about off-target matches. Individual regions or whole genome annotations can be obtained using our tool bigBedToBed, which can be compiled from the source code or downloaded as a pre-compiled 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/hs1/crisprHs1/crispr.bb -chrom=chr21 -start=0 -end=1000000 stdout Credits Track created by Maximilian Haeussler, 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 
cytoBandIdeo Cytobands (Ideogram) Cytobands Mapped from hg38/GRCh38 map 
dbSNP155 dbSNP 155 dbSNP 155 (lifted) varRep Description dbSNP build 155 (ftp.ncbi.nih.gov/snp/archive/b155/) was lifted over from GRCh38 to the T2T-CHM13v2.0 assembly. Only variants on the primary assemblies for Chromosomes 1-22, Chromsome X and Chromosome Y were lifted over. This track contains dbSNP variants that lifted over from GRCh38 to the T2T-CHM13 assembly. This includes variants that lifted over perfectly, as well as variants that failed initial liftover due to reference/alternative allele swaps but were recovered on subsequent liftover, with reference and alternative alleles swapped appropriately. These two sets of variants are included together in this track. If you are interested in downloading these sets separately (i.e., variants that lifted over perfectly vs. recovered variants with ref/alt allele swaps) they can be accessed here: https://s3-us-west-2.amazonaws.com/human-pangenomics/index.html?prefix=T2T/CHM13/assemblies/annotation/liftover/. Methods We performed liftover using the GATK release 4.1.9 LiftoverVcf (Picard Version 2.23.3) tool with the default parameters. This successfully lifts over variants that map exactly from GRCh38 to T2T-CHM13v2.0 but does not recover variants with swapped reference and alternative alleles. To recover variants with swapped reference/alternative alleles, we ran LiftoverVCF again, with the RECOVER_SWAPPED_REF_ALT flag. Notably, this feature does not recover multiallelic variants, so to recover these variants, we first separated them into multiple biallelic variants, performed liftover using the RECOVER_SWAPPED_REF_ALT tag, and converted them back to their multiallelic representations. Contacts Dylan Taylor &lt;dtaylo95 at jhu.edu&gt; Rajiv McCoy &lt;rmccoy22 at jhu.edu&gt; References Van der Auwera GA & O'Connor BD. (2020). Genomics in the Cloud: Using Docker, GATK, and WDL in Terra (1st Edition). O'Reilly Media. 
gc5Base GC Percent GC Percent in 5-Base Windows map Description The GC percent track shows the percentage of G (guanine) and C (cytosine) bases in 5-base windows on the 24 Jan 2022 Homo sapiens/GCA_009914755.4_T2T-CHM13v2.0/GCA_009914755.4 genome assembly. High GC content is typically associated with gene-rich areas. The average overall GC percent for the entire assembly is % 40.75. This track may be configured in a variety of ways to highlight different aspects 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. 
gwasSNPs2022-03-08 GWAS Variants GWAS Variants 2022-03-08 (lifted) phenDis Description A GWAS Catalog VCF was generated by intersecting RefSeq IDs in the GWAS Catalog associations v1.0 file (ebi.ac.uk/gwas/api/search/downloads/full, accessed 2022-03-08) with all dbSNP build 155 variants on the primary contigs for Chromosomes 1-22, Chromsome X and Chromosome Y. These variants were lifted over from GRCh38 to the T2T-CHM13 assembly. This track contains GWAS Catalog variants that lifted over from GRCh38 to the T2T-CHM13v2.0 assembly. This includes variants that lifted over perfectly, as well as variants that failed initial liftover due to reference/alternative allele swaps but were recovered on subsequent liftover, with reference and alternative alleles swapped appropriately. These two sets of variants are included together in this track. If you are interested in downloading these sets separately (i.e., variants that lifted over perfectly vs. recovered variants with ref/alt allele swaps) they can be accessed here: https://s3-us-west-2.amazonaws.com/human-pangenomics/index.html?prefix=T2T/CHM13/assemblies/annotation/liftover/. Methods We performed liftover using the GATK release 4.1.9 LiftoverVcf (Picard Version 2.23.3) tool with the default parameters. This successfully lifts over variants that map exactly from GRCh38 to T2T-CHM13v2.0 but does not recover variants with swapped reference and alternative alleles. To recover variants with swapped reference/alternative alleles, we ran LiftoverVCF again, with the RECOVER_SWAPPED_REF_ALT flag. Notably, this feature does not recover multiallelic variants, so to recover these variants, we first separated them into multiple biallelic variants, performed liftover using the RECOVER_SWAPPED_REF_ALT tag, and converted them back to their multiallelic representations. Contacts Dylan Taylor &lt;dtaylo95 at jhu.edu&gt; Rajiv McCoy &lt;rmccoy22 at jhu.edu&gt; References Van der Auwera GA & O'Connor BD. (2020). Genomics in the Cloud: Using Docker, GATK, and WDL in Terra (1st Edition). O'Reilly Media. 
hgLiftOver Human liftOver LiftOver alignments from CHM13 to hg19/hg38 and HG002 with two different pipelines map Description LiftOver alignments are used to map annotations from one human assembly to another one. The subtracks of this track were created by the T2T consortium using the minimap2 aligner and strong filters; it maps CHM13 coordinates to the human assemblies hg19 and hg38. The T2T pipeline used the minimap2 aligner which outputs long alignments that do not require "chaining" of alignments into longer ones, then removed alignments that go to other chromosomes and removed all alignments to alternate haplotypes, fixes (corrections to the assembly) and unplaced contig sequences. This means that the T2T alignments are tuned for high specificity. These alignments are probably best used for mapping annotations to hg38 in automated pipelines and in cases where the final processing on hg38 does not use alts/fixes/unplaced sequences and when one wants to be sure that annotations that are mapped are as reliable as possible. Here is an example to illustrate the liftOver track: Example 1: The acrocentric arms of chromosomes 13, 14, 15, 21, 22 and Y where not sequenced in hg38 at all but they are present in CHM13. The T2T liftOver shows that little is mappable there, as the sequence is entirely new. A note on genes: As the link above shows, even though the sequence is new, the T2T group mapped hg38 Gencode 35 gene models into these regions using CAT/LiftOff. This is because CAT and liftOff are using approaches for their lifting of genes / mapping that are not based on the liftOver alignments but sequence homology. Also, we created dot plots from these alignments: Dot-plots for the T2T minimap2 liftOver alignments Display Conventions The track displays boxes joined together by either single or double lines, with the boxes represent aligning regions, single lines indicating gaps that are largely due to a deletion in the CHM13 v2.0 assembly or an insertion in the GRCh38 or GRCh37, and double lines representing more complex gaps that involve substantial sequence in both assembly. LiftOver chain file downloads One-to-one liftOver chain files to and from GRCh38/hg38 and GRCh37/hg19 are available here: T2T CHM13 v2.0 to GRCh38/hg38 chm13v2-hg38.over.chain.gz T2T CHM13 v2.0 to GRCh37/hg19 chm13v2-hg19.over.chain.gz GRCh38/hg38 to T2T CHM13 v2.0 hg38-chm13v2.over.chain.gz GRCh37/hg19 to T2T CHM13 v2.0 hg19-chm13v2.over.chain.gz The mask file for GRCh38/hg38 is hg38.liftover-mask.bed. Methods T2T GRCh38/hg38 pre-processing To prevent ambiguous alignments, all false duplications, as determined by the Genome in a Bottle Consortium (GCA_000001405.15_GRCh38_GRC_exclusions_T2Tv2.bed), as well as the GRCh38 modeled centromeres, were masked from the GRCh38/hg38 primary assembly. In addition, unlocalized and unplaced (random) contigs were removed. T2T GRCh37/hg19 pre-processing Unlocalized and unplaced (random) contigs were removed from the GRCh37/hg19 assembly. T2T Alignment and Chain Creation For the minimap2-based pipeline, the initial chain file was generated using nf-LO v1.5.1 with minimap2 v2.24 alignments. These chains were then split at all locations that contained unaligned segments greater than 1kbp or gaps greater than 10kbp. Split chain files were then converted to PAF format with extended CIGAR strings using chaintools (https://doi.org/10.5281/zenodo.6342391, v0.1), and alignments between nonhomologous chromosomes were removed. The trim-paf operation of rustybam (https://zenodo.org/record/6342176, v0.1.29) was next used to remove overlapping alignments in the query sequence, and then the target sequence, to create 1:1 alignments. PAF alignments were converted back to the chain format with paf2chain commit f68eeca, and finally, chaintools was used to generate the inverted chain file. Full commands with parameters used were: nextflow run main.nf --source GRCh38.fa --target chm13v2.0.fasta --outdir dir -profile local --aligner minimap2 python chaintools/src/split.py -c input.chain -o input-split.chain python chaintools/src/to_paf.py -c input-split.chain -t target.fa -q query.fa -o input-split.paf awk '$1==$6' input-split.paf | rb break-paf --max-size 10000 | rb trim-paf -r | rb invert | rb trim-paf -r | rb invert > out.paf paf2chain -i out.paf > out.chain python chaintools/src/invert.py -c out.chain -o out_inverted.chain The above process does not add chain ids or scores. The UCSC utilities chainMergeSort and chainScore are used to update the chains: chainMergeSort out.chain | chainScore stdin chm13v2.0.2bit hg38.2bit chm13v2.0-hg38.chain chainMergeSort out_inverted.chain | chainScore stdin hg38.2bit chm13v2.0.2bit hg38-chm13v2.0.chain Rustybam trim-paf uses dynamic programming and the CIGAR string to find an optimal splitting point between overlapping alignments in the query sequence. It starts its trimming with the largest overlap and then recursively trims smaller overlaps. Results were validated by using chaintools to confirm that there were no overlapping sequences with respect to both CHM13v2.0 and GRCh38 in the released chain file. In addition, trimmed alignments were visually inspected with SafFire to confirm their quality. Credits The T2T v1_nflo liftOver chains were generated by Nae-Chyun Chen&lt;naechyun.chen@gmail.com&gt; and Mitchell Vollger&lt;mvollger@uw.edu&gt;. The UCSC liftOver chains and the dot-plots were created by Hiram Clawson. lastz was developed by Robert Harris, Pennsylvania State University. 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 Nurk S, Koren S, Rhie A, Rautiainen M, et al. The complete sequence of a human genome. bioRxiv, 2021. Harris, R.S. (2007) Improved pairwise alignment of genomic DNA Ph.D. Thesis, The Pennsylvania State University 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 
t2tHg19LiftOver T2T hg19 liftOver T2T GRCh37/hg19 liftOver alignments: minimap2, no haps/alts, only same chrom map 
t2tHg38LiftOver T2T hg38 liftOver T2T GRCh38/hg38 liftOver alignments: minimap2, no haps/alts, only same chrom map 
mappability Mappability Single-read and multi-read mappability by Umap map Description Umap single-read and multi-read mappability Umap single-read mappability These tracks mark any region of the genome that is uniquely mappable by at least one k-mer. To calculate the single-read mappability, you must find the overlap of a given region with this track. Umap multi-read mappability These tracks represent the probability that a randomly selected k-mer which overlaps with a given position is uniquely mappable. For greater detail and explanatory diagrams, see the publication, the Umap and Bismap project website, or the Umap and Bismap software documentation. You can use these tracks for many purposes, including filtering unreliable signal from sequencing assays. Data Access The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, genome annotation is stored in a bigBed or bigWig file that can be downloaded from the download server. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed or bigWigToWig, 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 -chrom=chr6 -start=0 -end=1000000 http://hgdownload.soe.ucsc.edu/gbdb/hs1/hoffmanMappability/k24.Unique.Mappability.bb stdout bigWigToWig -chrom=chr6 -start=0 -end=1000000 http://hgdownload.soe.ucsc.edu/gbdb/hs1/hoffmanMappability/k24.Umap.MultiTrackMappability.bw stdout Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Credits Anshul Kundaje (Stanford University) created the original Umap software in MATLAB. The original Umap repository is available here. Mehran Karimzadeh (Michael Hoffman lab, Princess Margaret Cancer Centre) implemented the Python version of Umap and added features, including Bismap. References Karimzadeh M, Ernst C, Kundaje A, Hoffman MM. Umap and Bismap: quantifying genome and methylome mappability. Nucleic Acids Res. 2018 Nov 16;46(20):e120. PMID: 30169659; PMC: PMC6237805 
umapBigWig Mappability Single-read and multi-read mappability by Umap map 
umap250Quantitative Umap M250 Multi-read mappability with 250-mers map 
umap150Quantitative Umap M150 Multi-read mappability with 150-mers map 
umap100Quantitative Umap M100 Multi-read mappability with 100-mers map 
umap50Quantitative Umap M50 Multi-read mappability with 50-mers map 
umap36Quantitative Umap M36 Multi-read mappability with 36-mers map 
umap24Quantitative Umap M24 Multi-read mappability with 24-mers map 
umapBigBed Mappability Single-read and multi-read mappability by Umap map 
umap250 Umap S250 Single-read mappability with 250-mers map 
umap150 Umap S150 Single-read mappability with 150-mers map 
umap100 Umap S100 Single-read mappability with 100-mers map 
umap50 Umap S50 Single-read mappability with 50-mers map 
umap36 Umap S36 Single-read mappability with 36-mers map 
umap24 Umap S24 Single-read mappability with 24-mers map 
microsatellites Microsatellites Microsatellite repeats map Description This track represents % of simple-sequence (2-mer) repeat pattern. Sequences composed of GA/TC/GC/AT bases are counted if one of the bases repeats (e.g. AAAATTTTAAATT are counted as 13 ATs). Patterns are obtained from every non-verlapping 128 bp window. Values displayed are the % of bases forming the specific repeat type, with a maximum value of 100. These sequence patterns are useful for predicting HiFi or ONT coverage biases, as described in Nurk et al. 2022 (Fig. 3) and Mc Cartney et al., 2022. Display Conventions and Configuration &nbsp; &nbsp; - GA &nbsp; &nbsp; - TC &nbsp; &nbsp; - GC &nbsp; &nbsp; - AT Methods The track was generated using Seqrequester, a Meryl and Canu utility module. The tracks can be generated using seqrequester microsatellite. The code to generate these patterns can be found at T2T-Polish/pattern. Release history 2021/05/24, Microsatellite tracks for chm13.v1.0 assembly. 2021/10/13, Microsatellite tracks for chm13.v1.1 assembly. 2022/03/29, Microsatellite tracks for chm13.v2.0 assembly. Credits For inquiries, please contact us at Seqrequester or T2T-Polish. References Nurk S, Koren S, Rhie A, Rautiainen M et al. The complete sequence of a human genome. Science (2022) doi: 10.1126/science.abj6987 Mc Cartney AM, Shafin K, Alonge M et al. Chasing perfection: validation and polishing strategies for telomere-to-telomere genome assemblies. Nat. Methods (2022) doi: 10.1038/s41592-022-01440-3 
microsatellites_AT AT Microsatellites AT Microsatellite repeats map 
microsatellites_GC GC Microsatellites GC Microsatellite repeats map 
microsatellites_TC TC Microsatellites TC Microsatellite repeats map 
microsatellites_GA GA Microsatellites GA Microsatellite repeats map 
xenoRefGene RefSeq mRNAs RefSeq mRNAs mapped to this assembly rna Description The RefSeq mRNAs gene track for the 24 Jan 2022 Homo sapiens/GCA_009914755.4_T2T-CHM13v2.0 genome assembly displays translated blat alignments of vertebrate and invertebrate mRNA in GenBank. Track statistics summary Total genome size: 3,117,292,070 Gene count: 20,844 Bases in genes: 1,125,310,304 Percent genome coverage: % 36.099 Search tips Please note, the name searching system is not completely case insensitive. When in doubt, enter search names in all lower case to find gene names. Methods The mRNAs were aligned against the Homo sapiens/GCA_009914755.4_T2T-CHM13v2.0 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. Specifically, the translated blat command is: blat -noHead -q=rnax -t=dnax -mask=lower target.fa query.fa target.query.psl where target.fa is one of the chromosome sequence of the genome assembly, and the query.fa is the mRNAs from RefSeq The resulting PSL outputs are filtered: pslCDnaFilter -minId=0.35 -minCover=0.25 -globalNearBest=0.0100 -minQSize=20 -ignoreIntrons -repsAsMatch -ignoreNs -bestOverlap all.results.psl GCA_009914755.4_T2T-CHM13v2.0.xenoRefGene.psl The filtered GCA_009914755.4_T2T-CHM13v2.0.xenoRefGene.psl is converted to genePred data to display for this track. 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, Cavanaugh M, Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW. GenBank. Nucleic Acids Res. 2013 Jan;41(Database issue):D36-42. PMID: 23193287; PMC: PMC3531190 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 
t2tRepeatMasker RepeatMasker RepeatMasker Repetitive Elements varRep Description Repetitive genomic elements including Transposable Element (TE) families, Satellite, Short Tandem Repeats, and low complexity DNA as annotated by RepeatMasker. These tracks were constructed with the NCBI BLAST-derived search engine RMBlast and Dfam 3.3 database (plus T2T-CHM13-derived entries submitted to the Dfam 3.6 data release in April 2022, and HG002 chrY-derived entries not yet submitted). Individual tracks are identified using the three main components of the analysis, the version of RepeatMasker, the search engine used, and finally the repeat library version. RepeatMasker version April 01 2021 open-4.1.2-p1 Search Engine: RMBlast (-e ncbi) [ 2.10.0+ (March 2020) ] RepeatMasker Database: Dfam_3.3 (plus T2T-CHM13-derived entries submitted to the Dfam 3.6 data release in April 2022, and HG002 chrY-derived entries not yet submitted) Display Conventions and Configuration Context Sensitive Zooming This track employs a technique which chooses the appropriate visual representation for the data based on the zoom scale, and or the number of annotations currently in view. The track will automatically switch from the most detailed visualization ('Full' mode) to the denser view ('Pack' mode) when the window size is greater than 45kb of sequence. It will further switch to the even denser single line view ('Dense' mode) if more than 500 annotations are present in the current view. Dense Mode Visualization In dense display mode, a single line is displayed denoting the coverage of repeats using a series of colored boxes. The boxes are colored based on the classification of the repeat (see below for legend). Pack Mode Visualization In pack mode, repeats are represented as sets of joined features. These are color coded as above based on the class of the repeat, and the further details such as orientation (denoted by chevrons) and a family label are provided. This family label may be optionally turned off in the track configuration. The pack display mode may also be configured to resemble the original UCSC repeat track. In this visualization repeat features are grouped by classes (see below), and displayed on seperate track lines. The repeat ranges are denoted as grayscale boxes, reflecting both the size of the repeat and 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. Full Mode Visualization In the most detailed visualization repeats are displayed as chevron boxes, indicating the size and orientation of the repeat. The interior grayscale shading represents the divergence of the repeat (see above) while the outline color represents the class of the repeat. Dotted lines above the repeat and extending left or right indicate the length of unaligned repeat model sequence and provide context for where a repeat fragment originates in its consensus or pHMM model. If the length of the unaligned sequence is large, an iterruption line and bp size is indicated instead of drawing the extension to scale. For example, the following repeat is a SINE element in the forward orientation with average divergence. Only the 5' proximal fragment of the consensus sequence is aligned to the genome. The 3' unaligned length (384bp) is not drawn to scale and is instead displayed using a set of interruption lines along with the length of the unaligned sequence. Repeats that have been fragmented by insertions or large internal deletions are now represented by join lines. In the example below, a LINE element is found as two fragments. The solid connection lines indicate that there are no unaligned consensus bases between the two fragments. Also note these fragments form the 3' extremity of the repeat, as there is no unaligned consensus sequence following the last fragment. In cases where there is unaligned consensus sequence between the fragments, the repeat will look like the following. The dotted line indicates the length of the unaligned sequence between the two fragments. In this case the unaligned consensus is longer than the actual genomic distance between these two fragments. If there is consensus overlap between the two fragments, the joining lines will be drawn to indicate how much of the left fragment is repeated in the right fragment. The following table lists the repeat class colors: Color Repeat Class SINE - Short Interspersed Nuclear Element LINE - Long Interspersed Nuclear Element LTR - Long Terminal Repeat DNA - DNA Transposon Simple - Single Nucleotide Stretches and Tandem Repeats Low_complexity - Low Complexity DNA Satellite - Satellite Repeats RNA - RNA Repeats (including RNA, tRNA, rRNA, snRNA, scRNA, srpRNA) Other - Other Repeats (including class RC - Rolling Circle) Unknown - Unknown Classification A &quot;?&quot; at the end of the &quot;Family&quot; or &quot;Class&quot; (for example, DNA?) signifies that the curator was unsure of the classification. At some point in the future, either the &quot;?&quot; will be removed or the classification will be changed. Methods The RepeatMasker (www.repeatmasker.org) tool was used to generate the datasets found on this track hub. References Smit AFA, Hubley R, Green P. RepeatMasker Open-3.0. http://www.repeatmasker.org. 1996-2010. For the discovery of the additional T2T-CHM13-derived repeats included in this track, as well as the methods (and scripts) for masking the assembly with these T2T-CHM13-derived repeats and previously known repeats: Hoyt SJ, et al. From telomere to telomere: the transcriptional and epigenetic state of human repeat elements. bioRxiv. 2022 Apr 1. Hoyt SJ, et al. From telomere to telomere: the transcriptional and epigenetic state of human repeat elements analysis code: T2T-CHM13. bioRxiv. 2022 Apr 1. Dfam is described in: Wheeler TJ, Clements J, Eddy SR, Hubley R, Jones TA, Jurka J, Smit AF, Finn RD. Dfam: a database of repetitive DNA based on profile hidden Markov models. Nucleic Acids Res. 2013 Jan;41(Database issue):D70-82. PMID: 23203985; PMC: PMC3531169 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 
sedefSegDups SEDEF Segmental Dups SEDEF Segmental Duplications varRep Description A track showing segmental duplications as generated by sedef on the assembly: T2T-CHM13-v2.0 Column descriptions can be found here: columns. Display Conventions and Configuration This section describes track configuration controls, or any special display conventions such as the meaning of different colors in your tracks. The colours used for level of similary segmentation standard: less than 90% similarityPurple 90 - 98% similarityLight to dark gray 98 - 99% similarityYellow greater than 99% similarityOrange Methods Sedef was run with default parameters on a RepeatMasked genome assembly. The resulting output (final.bed) was then converted into a browser friendly format (bed9 + extra fields). Credits Mitchell R. Vollger &lt;mvollger@uw.edu&gt;, University of Washington References M. R. Vollger et al., Segmental duplications and their variation in a complete human genome. Science. 2022 April 1; eabj6965. DOI: 10.1126/science.abj6965 Numanagic I, G&#246;kkaya AS, Zhang L, Berger B, Alkan C, Hach F. Fast characterization of segmental duplications in genome assemblies. Bioinformatics. 2018 Sep 1;34(17):i706-i714. PMID: 30423092; PMC: PMC6129265 
simpleRepeat Simple Repeats Simple Tandem Repeats by TRF varRep Description This track displays simple tandem repeats (possibly imperfect repeats) on the 24 Jan 2022 Homo sapiens/GCA_009914755.4_T2T-CHM13v2.0/GCA_009914755.4 genome assembly, 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. There are 1,155,353 items in the track covering 277,065,041 bases, assembly size 3,117,292,070 bases, percent coverage % 8.89. 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 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 
T2T_Encode T2T Encode T2T Encode Reanalysis regulation Description These tracks represent a reanalysis of ENCODE data against the T2T chm13 genome. All ChIP-seq experiments with pair-end data and read lengths of 100bp or greater are included. Track types include: Coverage pileups of mapped and filtered reads Enrichment of mapped reads relative to a control ChIP-seq peaks as called by MACS2 ChIP-seq peaks as called by MACS2 in GRCh38 and lifted over to chm13 Methods Prior to mapping, reads originating from a single library were combined. Reads were mapped with Bowtie2 (v2.4.1) as paired-end with the arguments &quot;--no-discordant --no-mixed --very-sensitive --no-unal --omit-sec-seq --xeq --reorder&quot;. Alignments were filtered using SAMtools (v1.10) using the arguments &quot;-F 1804 -f 2 -q 2&quot; to remove unmapped or single end mapped reads and those with a mapping quality score less than 2. PCR duplicates were identified and removed with the Picard tools &quot;mark duplicates&quot; command (v2.22.1) and the arguments &quot;VALIDATION_STRINGENCY=LENIENT ASSUME_SORT_ORDER=queryname REMOVE_DUPLICATES = true&quot;. Alignments were then filtered for the presence of unique k-mers. Specifically, for each alignment, reference sequences aligned with template ends were compared to a database of minimum unique k-mer lengths. The size of the k-mers in the k-mer filtering step are dependent on the length of the mapped reference sequence. Alignments were discarded if no unique k-mers occurred in either end of the read. The minimum unique k-mer length database was generated using scripts found here. Alignments from replicates were then pooled. Bigwig coverage tracks were created using deepTools bamCoverage (v3.4.3) with a bin size of 1bp and default for all other parameters. Enrichment tracks were created using deepTools bamCompare with a bin size of 50bp, a pseudo-count of 1, and excluding bins with zero counts in both target and control tracks. Peak calls were made using MACS2 (v2.2.7.1) with default parameters and estimated genome sizes 3.03e9 and 2.79e9 for chm13 and GRCh38, respectively. GRCh38 peak calls were lifted over to chm13 using the UCSC liftOver utility, the chain file created by the T2T consortium, and the parameter "-minMatch=0.2". Credits Data were processed by Michael Sauria at Johns Hopkins University. For inquiries, please contact us at the following address: msauria@jhu.edu References Gershman A, Sauria MEG, Guitart X, Vollger MR, Hook PW, Hoyt SJ, Jain M, Shumate A, Razaghi R, Koren S, Altemose N, Caldas GV, Logsdon GA, Rhie A, Eichler EE, Schatz MC, O'Neill RJ, Phillippy AM, Miga KH, Timp W. Epigenetic patterns in a complete human genome. Science. 2022 Apr;376(6588):eabj5089. doi: 10.1126/science.abj5089. Epub 2022 Apr 1. PMID: 35357915. 
T2T_Encode_Peaks Encode Peaks Encode Peaks regulation 
T2T_Encode_Peaks_epithelial_cell_of_prostate_male.H3K27ac ecopm H3K27ac epithelial_cell_of_prostate_male H3K27ac Macs2 Peaks regulation 
T2T_Encode_Peaks_epithelial_cell_of_prostate_male.CTCF ecopm CTCF epithelial_cell_of_prostate_male CTCF Macs2 Peaks regulation 
T2T_Encode_Peaks_brain_microvascular_endothelial_cell.H3K36me3 bmec H3K36me3 brain_microvascular_endothelial_cell H3K36me3 Macs2 Peaks regulation 
T2T_Encode_Peaks_brain_microvascular_endothelial_cell.H3K27me3 bmec H3K27me3 brain_microvascular_endothelial_cell H3K27me3 Macs2 Peaks regulation 
T2T_Encode_Peaks_brain_microvascular_endothelial_cell.H3K27ac bmec H3K27ac brain_microvascular_endothelial_cell H3K27ac Macs2 Peaks regulation 
T2T_Encode_Peaks_VCaP.H3K27ac VCaP H3K27ac VCaP H3K27ac Macs2 Peaks regulation 
T2T_Encode_Peaks_VCaP.CTCF VCaP CTCF VCaP CTCF Macs2 Peaks regulation 
T2T_Encode_Peaks_SJSA1.H3K9me3 SJSA1 H3K9me3 SJSA1 H3K9me3 Macs2 Peaks regulation 
T2T_Encode_Peaks_SJSA1.H3K4me3 SJSA1 H3K4me3 SJSA1 H3K4me3 Macs2 Peaks regulation 
T2T_Encode_Peaks_SJSA1.H3K36me3 SJSA1 H3K36me3 SJSA1 H3K36me3 Macs2 Peaks regulation 
T2T_Encode_Peaks_SJSA1.H3K27me3 SJSA1 H3K27me3 SJSA1 H3K27me3 Macs2 Peaks regulation 
T2T_Encode_Peaks_SJSA1.H3K27ac SJSA1 H3K27ac SJSA1 H3K27ac Macs2 Peaks regulation 
T2T_Encode_Peaks_SJCRH30.H3K9me3 SJCRH30 H3K9me3 SJCRH30 H3K9me3 Macs2 Peaks regulation 
T2T_Encode_Peaks_SJCRH30.H3K4me3 SJCRH30 H3K4me3 SJCRH30 H3K4me3 Macs2 Peaks regulation 
T2T_Encode_Peaks_SJCRH30.H3K4me1 SJCRH30 H3K4me1 SJCRH30 H3K4me1 Macs2 Peaks regulation 
T2T_Encode_Peaks_SJCRH30.H3K36me3 SJCRH30 H3K36me3 SJCRH30 H3K36me3 Macs2 Peaks regulation 
T2T_Encode_Peaks_SJCRH30.H3K27me3 SJCRH30 H3K27me3 SJCRH30 H3K27me3 Macs2 Peaks regulation 
T2T_Encode_Peaks_SJCRH30.H3K27ac SJCRH30 H3K27ac SJCRH30 H3K27ac Macs2 Peaks regulation 
T2T_Encode_Peaks_RWPE2.H3K36me3 RWPE2 H3K36me3 RWPE2 H3K36me3 Macs2 Peaks regulation 
T2T_Encode_Peaks_RWPE2.H3K27ac RWPE2 H3K27ac RWPE2 H3K27ac Macs2 Peaks regulation 
T2T_Encode_Peaks_RWPE2.CTCF RWPE2 CTCF RWPE2 CTCF Macs2 Peaks regulation 
T2T_Encode_Peaks_RWPE1.H3K27ac RWPE1 H3K27ac RWPE1 H3K27ac Macs2 Peaks regulation 
T2T_Encode_Peaks_RWPE1.CTCF RWPE1 CTCF RWPE1 CTCF Macs2 Peaks regulation 
T2T_Encode_Peaks_MG63.H3K9me3 MG63 H3K9me3 MG63 H3K9me3 Macs2 Peaks regulation 
T2T_Encode_Peaks_MG63.H3K4me3 MG63 H3K4me3 MG63 H3K4me3 Macs2 Peaks regulation 
T2T_Encode_Peaks_MG63.H3K36me3 MG63 H3K36me3 MG63 H3K36me3 Macs2 Peaks regulation 
T2T_Encode_Peaks_MG63.H3K27me3 MG63 H3K27me3 MG63 H3K27me3 Macs2 Peaks regulation 
T2T_Encode_Peaks_HL-60.H3K27ac HL-60 H3K27ac HL-60 H3K27ac Macs2 Peaks regulation 
T2T_Encode_Peaks_HAP-1.H3K9me3 HAP-1 H3K9me3 HAP-1 H3K9me3 Macs2 Peaks regulation 
T2T_Encode_Peaks_HAP-1.H3K4me3 HAP-1 H3K4me3 HAP-1 H3K4me3 Macs2 Peaks regulation 
T2T_Encode_Peaks_HAP-1.H3K4me1 HAP-1 H3K4me1 HAP-1 H3K4me1 Macs2 Peaks regulation 
T2T_Encode_Peaks_HAP-1.H3K36me3 HAP-1 H3K36me3 HAP-1 H3K36me3 Macs2 Peaks regulation 
T2T_Encode_Peaks_HAP-1.H3K27me3 HAP-1 H3K27me3 HAP-1 H3K27me3 Macs2 Peaks regulation 
T2T_Encode_Peaks_HAP-1.H3K27ac HAP-1 H3K27ac HAP-1 H3K27ac Macs2 Peaks regulation 
T2T_Encode_Peaks_Caco-2.H3K9me3 Caco-2 H3K9me3 Caco-2 H3K9me3 Macs2 Peaks regulation 
T2T_Encode_Peaks_Caco-2.H3K4me1 Caco-2 H3K4me1 Caco-2 H3K4me1 Macs2 Peaks regulation 
T2T_Encode_Peaks_22Rv1.H3K27ac 22Rv1 H3K27ac 22Rv1 H3K27ac Macs2 Peaks regulation 
T2T_Encode_Peaks_22Rv1.CTCF 22Rv1 CTCF 22Rv1 CTCF Macs2 Peaks regulation 
T2T_Encode_Peaks_C4-2B.H3K27ac C4-2B H3K27ac C4-2B H3K27ac Macs2 Peaks regulation 
T2T_Encode_Peaks_C4-2B.CTCF C4-2B CTCF C4-2B CTCF Macs2 Peaks regulation 
T2T_Encode_Peaks_BE2C.H3K9me3 BE2C H3K9me3 BE2C H3K9me3 Macs2 Peaks regulation 
T2T_Encode_Peaks_BE2C.H3K4me1 BE2C H3K4me1 BE2C H3K4me1 Macs2 Peaks regulation 
T2T_Encode_Peaks_BE2C.H3K36me3 BE2C H3K36me3 BE2C H3K36me3 Macs2 Peaks regulation 
T2T_Encode_Peaks_BE2C.H3K27me3 BE2C H3K27me3 BE2C H3K27me3 Macs2 Peaks regulation 
T2T_Encode_LOPeaks Encode hg38 LO Peaks Encode hg38 liftover Peaks regulation 
T2T_Encode_LOPeaks_epithelial_cell_of_prostate_male.H3K27ac ecopm H3K27ac hg38LO epithelial_cell_of_prostate_male H3K27ac hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_epithelial_cell_of_prostate_male.CTCF ecopm CTCF hg38LO epithelial_cell_of_prostate_male CTCF hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_brain_microvascular_endothelial_cell.H3K36me3 bmec H3K36me3 hg38LO brain_microvascular_endothelial_cell H3K36me3 hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_brain_microvascular_endothelial_cell.H3K27me3 bmec H3K27me3 hg38LO brain_microvascular_endothelial_cell H3K27me3 hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_brain_microvascular_endothelial_cell.H3K27ac bmec H3K27ac hg38LO brain_microvascular_endothelial_cell H3K27ac hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_VCaP.H3K27ac VCaP H3K27ac hg38LO VCaP H3K27ac hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_VCaP.CTCF VCaP CTCF hg38LO VCaP CTCF hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_SJSA1.H3K9me3 SJSA1 H3K9me3 hg38LO SJSA1 H3K9me3 hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_SJSA1.H3K4me3 SJSA1 H3K4me3 hg38LO SJSA1 H3K4me3 hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_SJSA1.H3K36me3 SJSA1 H3K36me3 hg38LO SJSA1 H3K36me3 hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_SJSA1.H3K27me3 SJSA1 H3K27me3 hg38LO SJSA1 H3K27me3 hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_SJSA1.H3K27ac SJSA1 H3K27ac hg38LO SJSA1 H3K27ac hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_SJCRH30.H3K9me3 SJCRH30 H3K9me3 hg38LO SJCRH30 H3K9me3 hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_SJCRH30.H3K4me3 SJCRH30 H3K4me3 hg38LO SJCRH30 H3K4me3 hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_SJCRH30.H3K4me1 SJCRH30 H3K4me1 hg38LO SJCRH30 H3K4me1 hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_SJCRH30.H3K36me3 SJCRH30 H3K36me3 hg38LO SJCRH30 H3K36me3 hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_SJCRH30.H3K27me3 SJCRH30 H3K27me3 hg38LO SJCRH30 H3K27me3 hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_SJCRH30.H3K27ac SJCRH30 H3K27ac hg38LO SJCRH30 H3K27ac hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_RWPE2.H3K36me3 RWPE2 H3K36me3 hg38LO RWPE2 H3K36me3 hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_RWPE2.H3K27ac RWPE2 H3K27ac hg38LO RWPE2 H3K27ac hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_RWPE2.CTCF RWPE2 CTCF hg38LO RWPE2 CTCF hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_RWPE1.H3K27ac RWPE1 H3K27ac hg38LO RWPE1 H3K27ac hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_RWPE1.CTCF RWPE1 CTCF hg38LO RWPE1 CTCF hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_MG63.H3K9me3 MG63 H3K9me3 hg38LO MG63 H3K9me3 hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_MG63.H3K4me3 MG63 H3K4me3 hg38LO MG63 H3K4me3 hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_MG63.H3K36me3 MG63 H3K36me3 hg38LO MG63 H3K36me3 hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_MG63.H3K27me3 MG63 H3K27me3 hg38LO MG63 H3K27me3 hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_HL-60.H3K27ac HL-60 H3K27ac hg38LO HL-60 H3K27ac hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_HAP-1.H3K9me3 HAP-1 H3K9me3 hg38LO HAP-1 H3K9me3 hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_HAP-1.H3K4me3 HAP-1 H3K4me3 hg38LO HAP-1 H3K4me3 hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_HAP-1.H3K4me1 HAP-1 H3K4me1 hg38LO HAP-1 H3K4me1 hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_HAP-1.H3K36me3 HAP-1 H3K36me3 hg38LO HAP-1 H3K36me3 hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_HAP-1.H3K27me3 HAP-1 H3K27me3 hg38LO HAP-1 H3K27me3 hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_HAP-1.H3K27ac HAP-1 H3K27ac hg38LO HAP-1 H3K27ac hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_Caco-2.H3K9me3 Caco-2 H3K9me3 hg38LO Caco-2 H3K9me3 hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_Caco-2.H3K4me1 Caco-2 H3K4me1 hg38LO Caco-2 H3K4me1 hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_22Rv1.H3K27ac 22Rv1 H3K27ac hg38LO 22Rv1 H3K27ac hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_22Rv1.CTCF 22Rv1 CTCF hg38LO 22Rv1 CTCF hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_C4-2B.H3K27ac C4-2B H3K27ac hg38LO C4-2B H3K27ac hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_C4-2B.CTCF C4-2B CTCF hg38LO C4-2B CTCF hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_BE2C.H3K9me3 BE2C H3K9me3 hg38LO BE2C H3K9me3 hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_BE2C.H3K4me1 BE2C H3K4me1 hg38LO BE2C H3K4me1 hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_BE2C.H3K36me3 BE2C H3K36me3 hg38LO BE2C H3K36me3 hg38 liftover Macs2 Peaks regulation 
T2T_Encode_LOPeaks_BE2C.H3K27me3 BE2C H3K27me3 hg38LO BE2C H3K27me3 hg38 liftover Macs2 Peaks regulation 
T2T_Encode_Enrichment Encode Enrichment Encode Enrichment regulation 
T2T_Encode_Enrichment_epithelial_cell_of_prostate_male.H3K27ac ecopm H3K27ac epithelial_cell_of_prostate_male H3K27ac Enrichment regulation 
T2T_Encode_Enrichment_epithelial_cell_of_prostate_male.CTCF ecopm CTCF epithelial_cell_of_prostate_male CTCF Enrichment regulation 
T2T_Encode_Enrichment_brain_microvascular_endothelial_cell.H3K36me3 bmec H3K36me3 brain_microvascular_endothelial_cell H3K36me3 Enrichment regulation 
T2T_Encode_Enrichment_brain_microvascular_endothelial_cell.H3K27me3 bmec H3K27me3 brain_microvascular_endothelial_cell H3K27me3 Enrichment regulation 
T2T_Encode_Enrichment_brain_microvascular_endothelial_cell.H3K27ac bmec H3K27ac brain_microvascular_endothelial_cell H3K27ac Enrichment regulation 
T2T_Encode_Enrichment_VCaP.H3K27ac VCaP H3K27ac VCaP H3K27ac Enrichment regulation 
T2T_Encode_Enrichment_VCaP.CTCF VCaP CTCF VCaP CTCF Enrichment regulation 
T2T_Encode_Enrichment_SJSA1.H3K9me3 SJSA1 H3K9me3 SJSA1 H3K9me3 Enrichment regulation 
T2T_Encode_Enrichment_SJSA1.H3K4me3 SJSA1 H3K4me3 SJSA1 H3K4me3 Enrichment regulation 
T2T_Encode_Enrichment_SJSA1.H3K36me3 SJSA1 H3K36me3 SJSA1 H3K36me3 Enrichment regulation 
T2T_Encode_Enrichment_SJSA1.H3K27me3 SJSA1 H3K27me3 SJSA1 H3K27me3 Enrichment regulation 
T2T_Encode_Enrichment_SJSA1.H3K27ac SJSA1 H3K27ac SJSA1 H3K27ac Enrichment regulation 
T2T_Encode_Enrichment_SJCRH30.H3K9me3 SJCRH30 H3K9me3 SJCRH30 H3K9me3 Enrichment regulation 
T2T_Encode_Enrichment_SJCRH30.H3K4me3 SJCRH30 H3K4me3 SJCRH30 H3K4me3 Enrichment regulation 
T2T_Encode_Enrichment_SJCRH30.H3K4me1 SJCRH30 H3K4me1 SJCRH30 H3K4me1 Enrichment regulation 
T2T_Encode_Enrichment_SJCRH30.H3K36me3 SJCRH30 H3K36me3 SJCRH30 H3K36me3 Enrichment regulation 
T2T_Encode_Enrichment_SJCRH30.H3K27me3 SJCRH30 H3K27me3 SJCRH30 H3K27me3 Enrichment regulation 
T2T_Encode_Enrichment_SJCRH30.H3K27ac SJCRH30 H3K27ac SJCRH30 H3K27ac Enrichment regulation 
T2T_Encode_Enrichment_RWPE2.H3K36me3 RWPE2 H3K36me3 RWPE2 H3K36me3 Enrichment regulation 
T2T_Encode_Enrichment_RWPE2.H3K27ac RWPE2 H3K27ac RWPE2 H3K27ac Enrichment regulation 
T2T_Encode_Enrichment_RWPE2.CTCF RWPE2 CTCF RWPE2 CTCF Enrichment regulation 
T2T_Encode_Enrichment_RWPE1.H3K27ac RWPE1 H3K27ac RWPE1 H3K27ac Enrichment regulation 
T2T_Encode_Enrichment_RWPE1.CTCF RWPE1 CTCF RWPE1 CTCF Enrichment regulation 
T2T_Encode_Enrichment_MG63.H3K9me3 MG63 H3K9me3 MG63 H3K9me3 Enrichment regulation 
T2T_Encode_Enrichment_MG63.H3K4me3 MG63 H3K4me3 MG63 H3K4me3 Enrichment regulation 
T2T_Encode_Enrichment_MG63.H3K36me3 MG63 H3K36me3 MG63 H3K36me3 Enrichment regulation 
T2T_Encode_Enrichment_MG63.H3K27me3 MG63 H3K27me3 MG63 H3K27me3 Enrichment regulation 
T2T_Encode_Enrichment_HL-60.H3K27ac HL-60 H3K27ac HL-60 H3K27ac Enrichment regulation 
T2T_Encode_Enrichment_HAP-1.H3K9me3 HAP-1 H3K9me3 HAP-1 H3K9me3 Enrichment regulation 
T2T_Encode_Enrichment_HAP-1.H3K4me3 HAP-1 H3K4me3 HAP-1 H3K4me3 Enrichment regulation 
T2T_Encode_Enrichment_HAP-1.H3K4me1 HAP-1 H3K4me1 HAP-1 H3K4me1 Enrichment regulation 
T2T_Encode_Enrichment_HAP-1.H3K36me3 HAP-1 H3K36me3 HAP-1 H3K36me3 Enrichment regulation 
T2T_Encode_Enrichment_HAP-1.H3K27me3 HAP-1 H3K27me3 HAP-1 H3K27me3 Enrichment regulation 
T2T_Encode_Enrichment_HAP-1.H3K27ac HAP-1 H3K27ac HAP-1 H3K27ac Enrichment regulation 
T2T_Encode_Enrichment_Caco-2.H3K9me3 Caco-2 H3K9me3 Caco-2 H3K9me3 Enrichment regulation 
T2T_Encode_Enrichment_Caco-2.H3K4me1 Caco-2 H3K4me1 Caco-2 H3K4me1 Enrichment regulation 
T2T_Encode_Enrichment_22Rv1.H3K27ac 22Rv1 H3K27ac 22Rv1 H3K27ac Enrichment regulation 
T2T_Encode_Enrichment_22Rv1.CTCF 22Rv1 CTCF 22Rv1 CTCF Enrichment regulation 
T2T_Encode_Enrichment_C4-2B.H3K27ac C4-2B H3K27ac C4-2B H3K27ac Enrichment regulation 
T2T_Encode_Enrichment_C4-2B.CTCF C4-2B CTCF C4-2B CTCF Enrichment regulation 
T2T_Encode_Enrichment_BE2C.H3K9me3 BE2C H3K9me3 BE2C H3K9me3 Enrichment regulation 
T2T_Encode_Enrichment_BE2C.H3K4me1 BE2C H3K4me1 BE2C H3K4me1 Enrichment regulation 
T2T_Encode_Enrichment_BE2C.H3K36me3 BE2C H3K36me3 BE2C H3K36me3 Enrichment regulation 
T2T_Encode_Enrichment_BE2C.H3K27me3 BE2C H3K27me3 BE2C H3K27me3 Enrichment regulation 
T2T_Encode_Coverage Encode Coverage Encode Coverage regulation 
T2T_Encode_Coverage_epithelial_cell_of_prostate_male.H3K27ac ecopm H3K27ac epithelial_cell_of_prostate_male H3K27ac Coverage regulation 
T2T_Encode_Coverage_epithelial_cell_of_prostate_male.Control ecopm Control epithelial_cell_of_prostate_male Control Coverage regulation 
T2T_Encode_Coverage_epithelial_cell_of_prostate_male.CTCF ecopm CTCF epithelial_cell_of_prostate_male CTCF Coverage regulation 
T2T_Encode_Coverage_brain_microvascular_endothelial_cell.H3K36me3 bmec H3K36me3 brain_microvascular_endothelial_cell H3K36me3 Coverage regulation 
T2T_Encode_Coverage_brain_microvascular_endothelial_cell.H3K27me3 bmec H3K27me3 brain_microvascular_endothelial_cell H3K27me3 Coverage regulation 
T2T_Encode_Coverage_brain_microvascular_endothelial_cell.H3K27ac bmec H3K27ac brain_microvascular_endothelial_cell H3K27ac Coverage regulation 
T2T_Encode_Coverage_brain_microvascular_endothelial_cell.Control bmec Control brain_microvascular_endothelial_cell Control Coverage regulation 
T2T_Encode_Coverage_VCaP.H3K27ac VCaP H3K27ac VCaP H3K27ac Coverage regulation 
T2T_Encode_Coverage_VCaP.Control VCaP Control VCaP Control Coverage regulation 
T2T_Encode_Coverage_VCaP.CTCF VCaP CTCF VCaP CTCF Coverage regulation 
T2T_Encode_Coverage_SJSA1.H3K9me3 SJSA1 H3K9me3 SJSA1 H3K9me3 Coverage regulation 
T2T_Encode_Coverage_SJSA1.H3K4me3 SJSA1 H3K4me3 SJSA1 H3K4me3 Coverage regulation 
T2T_Encode_Coverage_SJSA1.H3K36me3 SJSA1 H3K36me3 SJSA1 H3K36me3 Coverage regulation 
T2T_Encode_Coverage_SJSA1.H3K27me3 SJSA1 H3K27me3 SJSA1 H3K27me3 Coverage regulation 
T2T_Encode_Coverage_SJSA1.H3K27ac SJSA1 H3K27ac SJSA1 H3K27ac Coverage regulation 
T2T_Encode_Coverage_SJSA1.Control SJSA1 Control SJSA1 Control Coverage regulation 
T2T_Encode_Coverage_SJCRH30.H3K9me3 SJCRH30 H3K9me3 SJCRH30 H3K9me3 Coverage regulation 
T2T_Encode_Coverage_SJCRH30.H3K4me3 SJCRH30 H3K4me3 SJCRH30 H3K4me3 Coverage regulation 
T2T_Encode_Coverage_SJCRH30.H3K4me1 SJCRH30 H3K4me1 SJCRH30 H3K4me1 Coverage regulation 
T2T_Encode_Coverage_SJCRH30.H3K36me3 SJCRH30 H3K36me3 SJCRH30 H3K36me3 Coverage regulation 
T2T_Encode_Coverage_SJCRH30.H3K27me3 SJCRH30 H3K27me3 SJCRH30 H3K27me3 Coverage regulation 
T2T_Encode_Coverage_SJCRH30.H3K27ac SJCRH30 H3K27ac SJCRH30 H3K27ac Coverage regulation 
T2T_Encode_Coverage_SJCRH30.Control SJCRH30 Control SJCRH30 Control Coverage regulation 
T2T_Encode_Coverage_RWPE2.H3K36me3 RWPE2 H3K36me3 RWPE2 H3K36me3 Coverage regulation 
T2T_Encode_Coverage_RWPE2.H3K27ac RWPE2 H3K27ac RWPE2 H3K27ac Coverage regulation 
T2T_Encode_Coverage_RWPE2.Control RWPE2 Control RWPE2 Control Coverage regulation 
T2T_Encode_Coverage_RWPE2.CTCF RWPE2 CTCF RWPE2 CTCF Coverage regulation 
T2T_Encode_Coverage_RWPE1.H3K27ac RWPE1 H3K27ac RWPE1 H3K27ac Coverage regulation 
T2T_Encode_Coverage_RWPE1.Control RWPE1 Control RWPE1 Control Coverage regulation 
T2T_Encode_Coverage_RWPE1.CTCF RWPE1 CTCF RWPE1 CTCF Coverage regulation 
T2T_Encode_Coverage_MG63.H3K9me3 MG63 H3K9me3 MG63 H3K9me3 Coverage regulation 
T2T_Encode_Coverage_MG63.H3K4me3 MG63 H3K4me3 MG63 H3K4me3 Coverage regulation 
T2T_Encode_Coverage_MG63.H3K36me3 MG63 H3K36me3 MG63 H3K36me3 Coverage regulation 
T2T_Encode_Coverage_MG63.H3K27me3 MG63 H3K27me3 MG63 H3K27me3 Coverage regulation 
T2T_Encode_Coverage_MG63.Control MG63 Control MG63 Control Coverage regulation 
T2T_Encode_Coverage_HL-60.H3K27ac HL-60 H3K27ac HL-60 H3K27ac Coverage regulation 
T2T_Encode_Coverage_HL-60.Control HL-60 Control HL-60 Control Coverage regulation 
T2T_Encode_Coverage_HAP-1.H3K9me3 HAP-1 H3K9me3 HAP-1 H3K9me3 Coverage regulation 
T2T_Encode_Coverage_HAP-1.H3K4me3 HAP-1 H3K4me3 HAP-1 H3K4me3 Coverage regulation 
T2T_Encode_Coverage_HAP-1.H3K4me1 HAP-1 H3K4me1 HAP-1 H3K4me1 Coverage regulation 
T2T_Encode_Coverage_HAP-1.H3K36me3 HAP-1 H3K36me3 HAP-1 H3K36me3 Coverage regulation 
T2T_Encode_Coverage_HAP-1.H3K27me3 HAP-1 H3K27me3 HAP-1 H3K27me3 Coverage regulation 
T2T_Encode_Coverage_HAP-1.H3K27ac HAP-1 H3K27ac HAP-1 H3K27ac Coverage regulation 
T2T_Encode_Coverage_HAP-1.Control HAP-1 Control HAP-1 Control Coverage regulation 
T2T_Encode_Coverage_Caco-2.H3K9me3 Caco-2 H3K9me3 Caco-2 H3K9me3 Coverage regulation 
T2T_Encode_Coverage_Caco-2.H3K4me1 Caco-2 H3K4me1 Caco-2 H3K4me1 Coverage regulation 
T2T_Encode_Coverage_Caco-2.Control Caco-2 Control Caco-2 Control Coverage regulation 
T2T_Encode_Coverage_22Rv1.H3K27ac 22Rv1 H3K27ac 22Rv1 H3K27ac Coverage regulation 
T2T_Encode_Coverage_22Rv1.Control 22Rv1 Control 22Rv1 Control Coverage regulation 
T2T_Encode_Coverage_22Rv1.CTCF 22Rv1 CTCF 22Rv1 CTCF Coverage regulation 
T2T_Encode_Coverage_C4-2B.H3K27ac C4-2B H3K27ac C4-2B H3K27ac Coverage regulation 
T2T_Encode_Coverage_C4-2B.Control C4-2B Control C4-2B Control Coverage regulation 
T2T_Encode_Coverage_C4-2B.CTCF C4-2B CTCF C4-2B CTCF Coverage regulation 
T2T_Encode_Coverage_BE2C.H3K9me3 BE2C H3K9me3 BE2C H3K9me3 Coverage regulation 
T2T_Encode_Coverage_BE2C.H3K4me1 BE2C H3K4me1 BE2C H3K4me1 Coverage regulation 
T2T_Encode_Coverage_BE2C.H3K36me3 BE2C H3K36me3 BE2C H3K36me3 Coverage regulation 
T2T_Encode_Coverage_BE2C.H3K27me3 BE2C H3K27me3 BE2C H3K27me3 Coverage regulation 
T2T_Encode_Coverage_BE2C.Control BE2C Control BE2C Control Coverage regulation 
windowMasker WM + SDust Genomic Intervals Masked by WindowMasker + SDust varRep Description This track depicts masked sequence as determined by WindowMasker on the the 24 Jan 2022 Homo sapiens/GCA_009914755.4_T2T-CHM13v2.0/GCA_009914755.4 genome assembly. 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 GCA_009914755.4_T2T-CHM13v2.0.unmasked.fa -output wm_counts windowmasker -ustat wm_counts -sdust true -input GCA_009914755.4_T2T-CHM13v2.0.unmasked.fa -output windowmasker.intervals The windowmasker.sdust.bed included masking for areas of the assembly that are gap. The file was 'cleaned' to remove those areas of masking in gaps, leaving only the sequence masking. The final result covers 1,297,712,987 bases in the assembly size 3,117,292,070 for a percent coverage of % 41.63. 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 
sgdpCopyNumber SGDP copy number SGDP copy number estimates varRep Description This track represents copy number estimates form the Simons Genome Diversity Project. Copy number is estimated over 500 bp windows of uniquely mappable sequence. Sequences are colored from cold to hot (0 - 120+) and exact copy can be found by clicking on the region of interest. Code Availability GitHub Copy Number Key Copy numberColor0&#9632;1&#9632;2&#9632;3&#9632;4&#9632;5&#9632;6&#9632;7&#9632;8&#9632;9&#9632;10&#9632;20&#9632;30&#9632;40&#9632;50&#9632;60&#9632;70&#9632;80&#9632;90&#9632;100&#9632;110&#9632;120&#9632; Credits Please feel free to contact William Harvey or Mitchell Vollger with any questions and/or concerns regarding this track. References Bailey JA, Gu Z, Clark RA, Reinert K, Samonte RV, Schwartz S, Adams MD, Myers EW, Li PW, Eichler EE. Recent segmental duplications in the human genome. Science 2002 Pendleton AL, Shen F, Taravella AM, Emery S, Veeramah KR, Boyko AR, Kidd JM. Comparison of village dog and wolf genomes highlights the role of the neural crest in dog domestication. BMC Biol. 2018 Sudmant PH, Mallick S, Nelson BJ, Hormozdiari F, Krumm N, Huddleston J, et al. Global diversity, population stratification, and selection of human copy-number variation. Science. 2015 Sudmant PH, Kitzman JO, Antonacci F, Alkan C, Malig M, Tsalenko A, et al. Diversity of human copy number. Science. 2010 Sample table SampleRead file LP6005441-DNA_A01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_A01.srt.aln.bam LP6005441-DNA_A03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_A03.srt.aln.bam LP6005441-DNA_A04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_A04.srt.aln.bam LP6005441-DNA_A05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_A05.srt.aln.bam LP6005441-DNA_A06/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_A06.srt.aln.bam LP6005441-DNA_A08/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_A08.srt.aln.bam LP6005441-DNA_A09/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_A09.srt.aln.bam LP6005441-DNA_A10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_A10.srt.aln.bam LP6005441-DNA_A11/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_A11.srt.aln.bam LP6005441-DNA_A12/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_A12.srt.aln.bam LP6005441-DNA_B01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_B01.srt.aln.bam LP6005441-DNA_B02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_B02.srt.aln.bam LP6005441-DNA_B03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_B03.srt.aln.bam LP6005441-DNA_B04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_B04.srt.aln.bam LP6005441-DNA_B05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_B05.srt.aln.bam LP6005441-DNA_B06/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_B06.srt.aln.bam LP6005441-DNA_B07/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_B07.srt.aln.bam LP6005441-DNA_B08/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_B08.srt.aln.bam LP6005441-DNA_B09/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_B09.srt.aln.bam LP6005441-DNA_B10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_B10.srt.aln.bam LP6005441-DNA_B11/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_B11.srt.aln.bam LP6005441-DNA_B12/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_B12.srt.aln.bam LP6005441-DNA_C02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_C02.srt.aln.bam LP6005441-DNA_C03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_C03.srt.aln.bam LP6005441-DNA_C05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_C05.srt.aln.bam LP6005441-DNA_C06/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_C06.srt.aln.bam LP6005441-DNA_C07/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_C07.srt.aln.bam LP6005441-DNA_C08/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_C08.srt.aln.bam LP6005441-DNA_C09/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_C09.srt.aln.bam LP6005441-DNA_C10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_C10.srt.aln.bam LP6005441-DNA_C11/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_C11.srt.aln.bam LP6005441-DNA_D01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_D01.srt.aln.bam LP6005441-DNA_D02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_D02.srt.aln.bam LP6005441-DNA_D03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_D03.srt.aln.bam LP6005441-DNA_D04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_D04.srt.aln.bam LP6005441-DNA_D05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_D05.srt.aln.bam LP6005441-DNA_D06/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_D06.srt.aln.bam LP6005441-DNA_D07/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_D07.srt.aln.bam LP6005441-DNA_D08/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_D08.srt.aln.bam LP6005441-DNA_D09/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_D09.srt.aln.bam LP6005441-DNA_D10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_D10.srt.aln.bam LP6005441-DNA_D11/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_D11.srt.aln.bam LP6005441-DNA_D12/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_D12.srt.aln.bam LP6005441-DNA_E02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_E02.srt.aln.bam LP6005441-DNA_E03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_E03.srt.aln.bam LP6005441-DNA_E05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_E05.srt.aln.bam LP6005441-DNA_E06/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_E06.srt.aln.bam LP6005441-DNA_E07/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_E07.srt.aln.bam LP6005441-DNA_E08/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_E08.srt.aln.bam LP6005441-DNA_E09/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_E09.srt.aln.bam LP6005441-DNA_E10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_E10.srt.aln.bam LP6005441-DNA_F01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_F01.srt.aln.bam LP6005441-DNA_F02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_F02.srt.aln.bam LP6005441-DNA_F03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_F03.srt.aln.bam LP6005441-DNA_F04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_F04.srt.aln.bam LP6005441-DNA_F05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_F05.srt.aln.bam LP6005441-DNA_F06/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_F06.srt.aln.bam LP6005441-DNA_F07/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_F07.srt.aln.bam LP6005441-DNA_F08/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_F08.srt.aln.bam LP6005441-DNA_F09/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_F09.srt.aln.bam LP6005441-DNA_F10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_F10.srt.aln.bam LP6005441-DNA_F12/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_F12.srt.aln.bam 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LP6005441-DNA_G11/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_G11.srt.aln.bam LP6005441-DNA_G12/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_G12.srt.aln.bam LP6005441-DNA_H02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_H02.srt.aln.bam LP6005441-DNA_H03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_H03.srt.aln.bam LP6005441-DNA_H05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_H05.srt.aln.bam LP6005441-DNA_H06/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_H06.srt.aln.bam LP6005441-DNA_H07/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_H07.srt.aln.bam LP6005441-DNA_H08/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_H08.srt.aln.bam LP6005441-DNA_H09/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_H09.srt.aln.bam 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LP6005519-DNA_B05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_B05.srt.aln.bam LP6005519-DNA_B06/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_B06.srt.aln.bam LP6005519-DNA_B08/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_B08.srt.aln.bam LP6005519-DNA_B10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_B10.srt.aln.bam LP6005519-DNA_B11/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_B11.srt.aln.bam LP6005519-DNA_B12/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_B12.srt.aln.bam LP6005519-DNA_C03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_C03.srt.aln.bam LP6005519-DNA_C04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_C04.srt.aln.bam LP6005519-DNA_C05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_C05.srt.aln.bam LP6005519-DNA_C06/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_C06.srt.aln.bam LP6005519-DNA_C10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_C10.srt.aln.bam LP6005519-DNA_C11/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_C11.srt.aln.bam LP6005519-DNA_C12/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_C12.srt.aln.bam LP6005519-DNA_D01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_D01.srt.aln.bam LP6005519-DNA_D03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_D03.srt.aln.bam LP6005519-DNA_D04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_D04.srt.aln.bam LP6005519-DNA_D05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_D05.srt.aln.bam LP6005519-DNA_D06/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_D06.srt.aln.bam LP6005519-DNA_D08/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_D08.srt.aln.bam LP6005519-DNA_D09/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_D09.srt.aln.bam LP6005519-DNA_D10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_D10.srt.aln.bam LP6005519-DNA_D11/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_D11.srt.aln.bam LP6005519-DNA_E04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_E04.srt.aln.bam LP6005519-DNA_E05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_E05.srt.aln.bam LP6005519-DNA_E06/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_E06.srt.aln.bam LP6005519-DNA_E08/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_E08.srt.aln.bam LP6005519-DNA_E09/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_E09.srt.aln.bam LP6005519-DNA_E10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_E10.srt.aln.bam LP6005519-DNA_E11/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_E11.srt.aln.bam LP6005519-DNA_F03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_F03.srt.aln.bam LP6005519-DNA_F04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_F04.srt.aln.bam LP6005519-DNA_F06/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_F06.srt.aln.bam LP6005519-DNA_F08/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_F08.srt.aln.bam LP6005519-DNA_F09/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_F09.srt.aln.bam LP6005519-DNA_F10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_F10.srt.aln.bam LP6005519-DNA_F11/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_F11.srt.aln.bam LP6005519-DNA_G02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_G02.srt.aln.bam LP6005519-DNA_G03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_G03.srt.aln.bam LP6005519-DNA_G04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_G04.srt.aln.bam LP6005519-DNA_G06/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_G06.srt.aln.bam LP6005519-DNA_G08/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_G08.srt.aln.bam LP6005519-DNA_G09/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_G09.srt.aln.bam LP6005519-DNA_G10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_G10.srt.aln.bam LP6005519-DNA_G11/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_G11.srt.aln.bam LP6005519-DNA_H03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_H03.srt.aln.bam LP6005519-DNA_H04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_H04.srt.aln.bam LP6005519-DNA_H09/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_H09.srt.aln.bam LP6005519-DNA_H10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_H10.srt.aln.bam LP6005519-DNA_H12/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_H12.srt.aln.bam LP6005592-DNA_A02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_A02.srt.aln.bam LP6005592-DNA_A04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_A04.srt.aln.bam LP6005592-DNA_B01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_B01.srt.aln.bam LP6005592-DNA_B02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_B02.srt.aln.bam LP6005592-DNA_B03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_B03.srt.aln.bam LP6005592-DNA_B04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_B04.srt.aln.bam LP6005592-DNA_C01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_C01.srt.aln.bam LP6005592-DNA_C02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_C02.srt.aln.bam LP6005592-DNA_C03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_C03.srt.aln.bam LP6005592-DNA_C05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_C05.srt.aln.bam LP6005592-DNA_D01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_D01.srt.aln.bam LP6005592-DNA_D03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_D03.srt.aln.bam LP6005592-DNA_D04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_D04.srt.aln.bam LP6005592-DNA_E01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_E01.srt.aln.bam LP6005592-DNA_E02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_E02.srt.aln.bam LP6005592-DNA_E05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_E05.srt.aln.bam LP6005592-DNA_F01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_F01.srt.aln.bam LP6005592-DNA_F03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_F03.srt.aln.bam LP6005592-DNA_F05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_F05.srt.aln.bam LP6005592-DNA_G01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_G01.srt.aln.bam LP6005592-DNA_G03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_G03.srt.aln.bam LP6005592-DNA_G05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_G05.srt.aln.bam LP6005592-DNA_H01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_H01.srt.aln.bam LP6005592-DNA_H03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_H03.srt.aln.bam LP6005619-DNA_A01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005619-DNA_A01.srt.aln.bam LP6005619-DNA_B01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005619-DNA_B01.srt.aln.bam LP6005619-DNA_C01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005619-DNA_C01.srt.aln.bam LP6005619-DNA_D01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005619-DNA_D01.srt.aln.bam LP6005677-DNA_A02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_A02.srt.aln.bam LP6005677-DNA_A03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_A03.srt.aln.bam LP6005677-DNA_A04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_A04.srt.aln.bam LP6005677-DNA_B01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_B01.srt.aln.bam LP6005677-DNA_B02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_B02.srt.aln.bam LP6005677-DNA_B04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_B04.srt.aln.bam LP6005677-DNA_C03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_C03.srt.aln.bam LP6005677-DNA_C04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_C04.srt.aln.bam LP6005677-DNA_D01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_D01.srt.aln.bam LP6005677-DNA_D03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_D03.srt.aln.bam LP6005677-DNA_D04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_D04.srt.aln.bam LP6005677-DNA_E01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_E01.srt.aln.bam LP6005677-DNA_E03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_E03.srt.aln.bam LP6005677-DNA_E04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_E04.srt.aln.bam LP6005677-DNA_F01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_F01.srt.aln.bam LP6005677-DNA_F03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_F03.srt.aln.bam LP6005677-DNA_G01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_G01.srt.aln.bam LP6005677-DNA_G03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_G03.srt.aln.bam LP6005677-DNA_H03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_H03.srt.aln.bam LP6007068-DNA_A01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6007068-DNA_A01.srt.aln.bam LP6007069-DNA_A01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6007069-DNA_A01.srt.aln.bam SS6004471/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/SS6004471.srt.aln.bam SS6004477/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/SS6004477.srt.aln.bam SS6004478/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/SS6004478.srt.aln.bam SS6004480/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/SS6004480.srt.aln.bam CHM13_kmer/net/eichler/vol27/projects/hprc/nobackups/analysis/kmer_fastcn/input/kmer/CHM13_v2.0/CHM13_v2.0.fa.gz GRCh38_kmer/net/eichler/vol27/projects/hprc/nobackups/analysis/kmer_fastcn/input/kmer/GRCh38/GRCh38.fa.gz 
LP6005619-DNA_D01_wssd LP6005619-DNA_D01 wssd CN LP6005619-DNA_D01 Copy Number varRep 
GRCh38_kmer_wssd GRCh38_kmer wssd CN GRCh38_kmer Copy Number varRep 
CHM13_kmer_wssd CHM13_kmer wssd CN CHM13_kmer Copy Number varRep 
SIB_Yakut_LP6005443-DNA_D02_wssd SIB_Yakut_LP6005443-DNA_D02 wssd CN SIB_Yakut_LP6005443-DNA_D02 Copy Number varRep 
SIB_Yakut_LP6005442-DNA_F01_wssd SIB_Yakut_LP6005442-DNA_F01 wssd CN SIB_Yakut_LP6005442-DNA_F01 Copy Number varRep 
SIB_Ulchi_LP6005442-DNA_H12_wssd SIB_Ulchi_LP6005442-DNA_H12 wssd CN SIB_Ulchi_LP6005442-DNA_H12 Copy Number varRep 
SIB_Ulchi_LP6005442-DNA_G12_wssd SIB_Ulchi_LP6005442-DNA_G12 wssd CN SIB_Ulchi_LP6005442-DNA_G12 Copy Number varRep 
SIB_Tubalar_LP6005442-DNA_F12_wssd SIB_Tubalar_LP6005442-DNA_F12 wssd CN SIB_Tubalar_LP6005442-DNA_F12 Copy Number varRep 
SIB_Tubalar_LP6005442-DNA_E12_wssd SIB_Tubalar_LP6005442-DNA_E12 wssd CN SIB_Tubalar_LP6005442-DNA_E12 Copy Number varRep 
SIB_Tlingit_LP6005443-DNA_E05_wssd SIB_Tlingit_LP6005443-DNA_E05 wssd CN SIB_Tlingit_LP6005443-DNA_E05 Copy Number varRep 
SIB_Tlingit_LP6005443-DNA_D05_wssd SIB_Tlingit_LP6005443-DNA_D05 wssd CN SIB_Tlingit_LP6005443-DNA_D05 Copy Number varRep 
SIB_Mongola_LP6005441-DNA_F08_wssd SIB_Mongola_LP6005441-DNA_F08 wssd CN SIB_Mongola_LP6005441-DNA_F08 Copy Number varRep 
SIB_Mongola_LP6005441-DNA_E08_wssd SIB_Mongola_LP6005441-DNA_E08 wssd CN SIB_Mongola_LP6005441-DNA_E08 Copy Number varRep 
SIB_Mansi_LP6005443-DNA_G04_wssd SIB_Mansi_LP6005443-DNA_G04 wssd CN SIB_Mansi_LP6005443-DNA_G04 Copy Number varRep 
SIB_Mansi_LP6005443-DNA_F04_wssd SIB_Mansi_LP6005443-DNA_F04 wssd CN SIB_Mansi_LP6005443-DNA_F04 Copy Number varRep 
SIB_Kyrgyz_LP6005677-DNA_B02_wssd SIB_Kyrgyz_LP6005677-DNA_B02 wssd CN SIB_Kyrgyz_LP6005677-DNA_B02 Copy Number varRep 
SIB_Kyrgyz_LP6005677-DNA_A02_wssd SIB_Kyrgyz_LP6005677-DNA_A02 wssd CN SIB_Kyrgyz_LP6005677-DNA_A02 Copy Number varRep 
SIB_Itelman_LP6005443-DNA_D04_wssd SIB_Itelman_LP6005443-DNA_D04 wssd CN SIB_Itelman_LP6005443-DNA_D04 Copy Number varRep 
SIB_Even_LP6005592-DNA_F03_wssd SIB_Even_LP6005592-DNA_F03 wssd CN SIB_Even_LP6005592-DNA_F03 Copy Number varRep 
SIB_Even_LP6005443-DNA_C04_wssd SIB_Even_LP6005443-DNA_C04 wssd CN SIB_Even_LP6005443-DNA_C04 Copy Number varRep 
SIB_Even_LP6005443-DNA_B04_wssd SIB_Even_LP6005443-DNA_B04 wssd CN SIB_Even_LP6005443-DNA_B04 Copy Number varRep 
SIB_Eskimo_Sireniki_LP6005443-DNA_H03_wssd SIB_Eskimo_Sireniki_LP6005443-DNA_H03 wssd CN SIB_Eskimo_Sireniki_LP6005443-DNA_H03 Copy Number varRep 
SIB_Eskimo_Sireniki_LP6005443-DNA_B03_wssd SIB_Eskimo_Sireniki_LP6005443-DNA_B03 wssd CN SIB_Eskimo_Sireniki_LP6005443-DNA_B03 Copy Number varRep 
SIB_Eskimo_Naukan_LP6005443-DNA_G03_wssd SIB_Eskimo_Naukan_LP6005443-DNA_G03 wssd CN SIB_Eskimo_Naukan_LP6005443-DNA_G03 Copy Number varRep 
SIB_Eskimo_Naukan_LP6005443-DNA_F03_wssd SIB_Eskimo_Naukan_LP6005443-DNA_F03 wssd CN SIB_Eskimo_Naukan_LP6005443-DNA_F03 Copy Number varRep 
SIB_Eskimo_Chaplin_LP6005443-DNA_D03_wssd SIB_Eskimo_Chaplin_LP6005443-DNA_D03 wssd CN SIB_Eskimo_Chaplin_LP6005443-DNA_D03 Copy Number varRep 
SIB_Chukchi_LP6005443-DNA_C03_wssd SIB_Chukchi_LP6005443-DNA_C03 wssd CN SIB_Chukchi_LP6005443-DNA_C03 Copy Number varRep 
SIB_Altaian_LP6005442-DNA_F02_wssd SIB_Altaian_LP6005442-DNA_F02 wssd CN SIB_Altaian_LP6005442-DNA_F02 Copy Number varRep 
SIB_Aleut_LP6005443-DNA_H02_wssd SIB_Aleut_LP6005443-DNA_H02 wssd CN SIB_Aleut_LP6005443-DNA_H02 Copy Number varRep 
SIB_Aleut_LP6005443-DNA_A03_wssd SIB_Aleut_LP6005443-DNA_A03 wssd CN SIB_Aleut_LP6005443-DNA_A03 Copy Number varRep 
SAS_Yadava_LP6005519-DNA_D04_wssd SAS_Yadava_LP6005519-DNA_D04 wssd CN SAS_Yadava_LP6005519-DNA_D04 Copy Number varRep 
SAS_Yadava_LP6005519-DNA_C04_wssd SAS_Yadava_LP6005519-DNA_C04 wssd CN SAS_Yadava_LP6005519-DNA_C04 Copy Number varRep 
SAS_Tibetan_LP6005619-DNA_A01_wssd SAS_Tibetan_LP6005619-DNA_A01 wssd CN SAS_Tibetan_LP6005619-DNA_A01 Copy Number varRep 
SAS_Tibetan_LP6005592-DNA_G05_wssd SAS_Tibetan_LP6005592-DNA_G05 wssd CN SAS_Tibetan_LP6005592-DNA_G05 Copy Number varRep 
SAS_Sindhi_LP6005441-DNA_H11_wssd SAS_Sindhi_LP6005441-DNA_H11 wssd CN SAS_Sindhi_LP6005441-DNA_H11 Copy Number varRep 
SAS_Sindhi_LP6005441-DNA_G11_wssd SAS_Sindhi_LP6005441-DNA_G11 wssd CN SAS_Sindhi_LP6005441-DNA_G11 Copy Number varRep 
SAS_Sherpa_LP6005592-DNA_F05_wssd SAS_Sherpa_LP6005592-DNA_F05 wssd CN SAS_Sherpa_LP6005592-DNA_F05 Copy Number varRep 
SAS_Sherpa_LP6005592-DNA_E05_wssd SAS_Sherpa_LP6005592-DNA_E05 wssd CN SAS_Sherpa_LP6005592-DNA_E05 Copy Number varRep 
SAS_Relli_LP6005519-DNA_B05_wssd SAS_Relli_LP6005519-DNA_B05 wssd CN SAS_Relli_LP6005519-DNA_B05 Copy Number varRep 
SAS_Relli_LP6005519-DNA_A05_wssd SAS_Relli_LP6005519-DNA_A05 wssd CN SAS_Relli_LP6005519-DNA_A05 Copy Number varRep 
SAS_Punjabi_LP6005592-DNA_B04_wssd SAS_Punjabi_LP6005592-DNA_B04 wssd CN SAS_Punjabi_LP6005592-DNA_B04 Copy Number varRep 
SAS_Punjabi_LP6005592-DNA_A04_wssd SAS_Punjabi_LP6005592-DNA_A04 wssd CN SAS_Punjabi_LP6005592-DNA_A04 Copy Number varRep 
SAS_Punjabi_LP6005442-DNA_B12_wssd SAS_Punjabi_LP6005442-DNA_B12 wssd CN SAS_Punjabi_LP6005442-DNA_B12 Copy Number varRep 
SAS_Punjabi_LP6005442-DNA_A12_wssd SAS_Punjabi_LP6005442-DNA_A12 wssd CN SAS_Punjabi_LP6005442-DNA_A12 Copy Number varRep 
SAS_Pathan_LP6005441-DNA_D10_wssd SAS_Pathan_LP6005441-DNA_D10 wssd CN SAS_Pathan_LP6005441-DNA_D10 Copy Number varRep 
SAS_Pathan_LP6005441-DNA_C10_wssd SAS_Pathan_LP6005441-DNA_C10 wssd CN SAS_Pathan_LP6005441-DNA_C10 Copy Number varRep 
SAS_Mala_LP6005519-DNA_F04_wssd SAS_Mala_LP6005519-DNA_F04 wssd CN SAS_Mala_LP6005519-DNA_F04 Copy Number varRep 
SAS_Mala_LP6005519-DNA_E04_wssd SAS_Mala_LP6005519-DNA_E04 wssd CN SAS_Mala_LP6005519-DNA_E04 Copy Number varRep 
SAS_Makrani_LP6005441-DNA_D07_wssd SAS_Makrani_LP6005441-DNA_D07 wssd CN SAS_Makrani_LP6005441-DNA_D07 Copy Number varRep 
SAS_Makrani_LP6005441-DNA_C07_wssd SAS_Makrani_LP6005441-DNA_C07 wssd CN SAS_Makrani_LP6005441-DNA_C07 Copy Number varRep 
SAS_Madiga_LP6005519-DNA_H04_wssd SAS_Madiga_LP6005519-DNA_H04 wssd CN SAS_Madiga_LP6005519-DNA_H04 Copy Number varRep 
SAS_Madiga_LP6005519-DNA_G04_wssd SAS_Madiga_LP6005519-DNA_G04 wssd CN SAS_Madiga_LP6005519-DNA_G04 Copy Number varRep 
SAS_Kusunda_LP6005443-DNA_D09_wssd SAS_Kusunda_LP6005443-DNA_D09 wssd CN SAS_Kusunda_LP6005443-DNA_D09 Copy Number varRep 
SAS_Kusunda_LP6005443-DNA_C09_wssd SAS_Kusunda_LP6005443-DNA_C09 wssd CN SAS_Kusunda_LP6005443-DNA_C09 Copy Number varRep 
SAS_Khonda_Dora_LP6005519-DNA_E05_wssd SAS_Khonda_Dora_LP6005519-DNA_E05 wssd CN SAS_Khonda_Dora_LP6005519-DNA_E05 Copy Number varRep 
SAS_Kapu_LP6005519-DNA_B04_wssd SAS_Kapu_LP6005519-DNA_B04 wssd CN SAS_Kapu_LP6005519-DNA_B04 Copy Number varRep 
SAS_Kapu_LP6005519-DNA_A04_wssd SAS_Kapu_LP6005519-DNA_A04 wssd CN SAS_Kapu_LP6005519-DNA_A04 Copy Number varRep 
SAS_Kalash_LP6005441-DNA_F06_wssd SAS_Kalash_LP6005441-DNA_F06 wssd CN SAS_Kalash_LP6005441-DNA_F06 Copy Number varRep 
SAS_Kalash_LP6005441-DNA_E06_wssd SAS_Kalash_LP6005441-DNA_E06 wssd CN SAS_Kalash_LP6005441-DNA_E06 Copy Number varRep 
SAS_Irula_LP6005519-DNA_D05_wssd SAS_Irula_LP6005519-DNA_D05 wssd CN SAS_Irula_LP6005519-DNA_D05 Copy Number varRep 
SAS_Irula_LP6005519-DNA_C05_wssd SAS_Irula_LP6005519-DNA_C05 wssd CN SAS_Irula_LP6005519-DNA_C05 Copy Number varRep 
SAS_Hazara_LP6005441-DNA_F05_wssd SAS_Hazara_LP6005441-DNA_F05 wssd CN SAS_Hazara_LP6005441-DNA_F05 Copy Number varRep 
SAS_Hazara_LP6005441-DNA_E05_wssd SAS_Hazara_LP6005441-DNA_E05 wssd CN SAS_Hazara_LP6005441-DNA_E05 Copy Number varRep 
SAS_Burusho_LP6005441-DNA_F03_wssd SAS_Burusho_LP6005441-DNA_F03 wssd CN SAS_Burusho_LP6005441-DNA_F03 Copy Number varRep 
SAS_Burusho_LP6005441-DNA_E03_wssd SAS_Burusho_LP6005441-DNA_E03 wssd CN SAS_Burusho_LP6005441-DNA_E03 Copy Number varRep 
SAS_Brahui_LP6005441-DNA_D03_wssd SAS_Brahui_LP6005441-DNA_D03 wssd CN SAS_Brahui_LP6005441-DNA_D03 Copy Number varRep 
SAS_Brahui_LP6005441-DNA_C03_wssd SAS_Brahui_LP6005441-DNA_C03 wssd CN SAS_Brahui_LP6005441-DNA_C03 Copy Number varRep 
SAS_Brahmin_LP6005519-DNA_H03_wssd SAS_Brahmin_LP6005519-DNA_H03 wssd CN SAS_Brahmin_LP6005519-DNA_H03 Copy Number varRep 
SAS_Brahmin_LP6005519-DNA_G03_wssd SAS_Brahmin_LP6005519-DNA_G03 wssd CN SAS_Brahmin_LP6005519-DNA_G03 Copy Number varRep 
SAS_Bengali_LP6005442-DNA_H09_wssd SAS_Bengali_LP6005442-DNA_H09 wssd CN SAS_Bengali_LP6005442-DNA_H09 Copy Number varRep 
SAS_Bengali_LP6005442-DNA_G09_wssd SAS_Bengali_LP6005442-DNA_G09 wssd CN SAS_Bengali_LP6005442-DNA_G09 Copy Number varRep 
SAS_Balochi_LP6005441-DNA_D01_wssd SAS_Balochi_LP6005441-DNA_D01 wssd CN SAS_Balochi_LP6005441-DNA_D01 Copy Number varRep 
OCN_Papuan_LP6005443-DNA_H07_wssd OCN_Papuan_LP6005443-DNA_H07 wssd CN OCN_Papuan_LP6005443-DNA_H07 Copy Number varRep 
OCN_Papuan_LP6005443-DNA_G07_wssd OCN_Papuan_LP6005443-DNA_G07 wssd CN OCN_Papuan_LP6005443-DNA_G07 Copy Number varRep 
OCN_Papuan_LP6005443-DNA_F08_wssd OCN_Papuan_LP6005443-DNA_F08 wssd CN OCN_Papuan_LP6005443-DNA_F08 Copy Number varRep 
OCN_Papuan_LP6005443-DNA_F07_wssd OCN_Papuan_LP6005443-DNA_F07 wssd CN OCN_Papuan_LP6005443-DNA_F07 Copy Number varRep 
OCN_Papuan_LP6005443-DNA_E08_wssd OCN_Papuan_LP6005443-DNA_E08 wssd CN OCN_Papuan_LP6005443-DNA_E08 Copy Number varRep 
OCN_Papuan_LP6005443-DNA_E07_wssd OCN_Papuan_LP6005443-DNA_E07 wssd CN OCN_Papuan_LP6005443-DNA_E07 Copy Number varRep 
OCN_Papuan_LP6005443-DNA_D08_wssd OCN_Papuan_LP6005443-DNA_D08 wssd CN OCN_Papuan_LP6005443-DNA_D08 Copy Number varRep 
OCN_Papuan_LP6005443-DNA_D07_wssd OCN_Papuan_LP6005443-DNA_D07 wssd CN OCN_Papuan_LP6005443-DNA_D07 Copy Number varRep 
OCN_Papuan_LP6005443-DNA_C08_wssd OCN_Papuan_LP6005443-DNA_C08 wssd CN OCN_Papuan_LP6005443-DNA_C08 Copy Number varRep 
OCN_Papuan_LP6005443-DNA_C07_wssd OCN_Papuan_LP6005443-DNA_C07 wssd CN OCN_Papuan_LP6005443-DNA_C07 Copy Number varRep 
OCN_Papuan_LP6005443-DNA_B08_wssd OCN_Papuan_LP6005443-DNA_B08 wssd CN OCN_Papuan_LP6005443-DNA_B08 Copy Number varRep 
OCN_Papuan_LP6005443-DNA_A08_wssd OCN_Papuan_LP6005443-DNA_A08 wssd CN OCN_Papuan_LP6005443-DNA_A08 Copy Number varRep 
OCN_Papuan_LP6005441-DNA_B10_wssd OCN_Papuan_LP6005441-DNA_B10 wssd CN OCN_Papuan_LP6005441-DNA_B10 Copy Number varRep 
OCN_Papuan_LP6005441-DNA_A10_wssd OCN_Papuan_LP6005441-DNA_A10 wssd CN OCN_Papuan_LP6005441-DNA_A10 Copy Number varRep 
OCN_Maori_LP6005592-DNA_B02_wssd OCN_Maori_LP6005592-DNA_B02 wssd CN OCN_Maori_LP6005592-DNA_B02 Copy Number varRep 
OCN_Igorot_LP6005519-DNA_D06_wssd OCN_Igorot_LP6005519-DNA_D06 wssd CN OCN_Igorot_LP6005519-DNA_D06 Copy Number varRep 
OCN_Igorot_LP6005519-DNA_C06_wssd OCN_Igorot_LP6005519-DNA_C06 wssd CN OCN_Igorot_LP6005519-DNA_C06 Copy Number varRep 
OCN_Hawaiian_LP6005592-DNA_H03_wssd OCN_Hawaiian_LP6005592-DNA_H03 wssd CN OCN_Hawaiian_LP6005592-DNA_H03 Copy Number varRep 
OCN_Dusun_LP6005519-DNA_F06_wssd OCN_Dusun_LP6005519-DNA_F06 wssd CN OCN_Dusun_LP6005519-DNA_F06 Copy Number varRep 
OCN_Dusun_LP6005519-DNA_E06_wssd OCN_Dusun_LP6005519-DNA_E06 wssd CN OCN_Dusun_LP6005519-DNA_E06 Copy Number varRep 
OCN_Bougainville_LP6005441-DNA_B03_wssd OCN_Bougainville_LP6005441-DNA_B03 wssd CN OCN_Bougainville_LP6005441-DNA_B03 Copy Number varRep 
OCN_Bougainville_LP6005441-DNA_A03_wssd OCN_Bougainville_LP6005441-DNA_A03 wssd CN OCN_Bougainville_LP6005441-DNA_A03 Copy Number varRep 
OCN_Australian_SS6004478_wssd OCN_Australian_SS6004478 wssd CN OCN_Australian_SS6004478 Copy Number varRep 
OCN_Australian_SS6004477_wssd OCN_Australian_SS6004477 wssd CN OCN_Australian_SS6004477 Copy Number varRep 
OCN_Australian_LP6005442-DNA_A09_wssd OCN_Australian_LP6005442-DNA_A09 wssd CN OCN_Australian_LP6005442-DNA_A09 Copy Number varRep 
EUR_Yemenite_Jew_LP6005592-DNA_H01_wssd EUR_Yemenite_Jew_LP6005592-DNA_H01 wssd CN EUR_Yemenite_Jew_LP6005592-DNA_H01 Copy Number varRep 
EUR_Yemenite_Jew_LP6005592-DNA_G01_wssd EUR_Yemenite_Jew_LP6005592-DNA_G01 wssd CN EUR_Yemenite_Jew_LP6005592-DNA_G01 Copy Number varRep 
EUR_Tuscan_LP6005441-DNA_H12_wssd EUR_Tuscan_LP6005441-DNA_H12 wssd CN EUR_Tuscan_LP6005441-DNA_H12 Copy Number varRep 
EUR_Tuscan_LP6005441-DNA_G12_wssd EUR_Tuscan_LP6005441-DNA_G12 wssd CN EUR_Tuscan_LP6005441-DNA_G12 Copy Number varRep 
EUR_Turkish_LP6005677-DNA_C03_wssd EUR_Turkish_LP6005677-DNA_C03 wssd CN EUR_Turkish_LP6005677-DNA_C03 Copy Number varRep 
EUR_Turkish_LP6005677-DNA_A03_wssd EUR_Turkish_LP6005677-DNA_A03 wssd CN EUR_Turkish_LP6005677-DNA_A03 Copy Number varRep 
EUR_Tajik_LP6005519-DNA_D03_wssd EUR_Tajik_LP6005519-DNA_D03 wssd CN EUR_Tajik_LP6005519-DNA_D03 Copy Number varRep 
EUR_Tajik_LP6005519-DNA_C03_wssd EUR_Tajik_LP6005519-DNA_C03 wssd CN EUR_Tajik_LP6005519-DNA_C03 Copy Number varRep 
EUR_Spanish_LP6005442-DNA_B11_wssd EUR_Spanish_LP6005442-DNA_B11 wssd CN EUR_Spanish_LP6005442-DNA_B11 Copy Number varRep 
EUR_Spanish_LP6005442-DNA_A11_wssd EUR_Spanish_LP6005442-DNA_A11 wssd CN EUR_Spanish_LP6005442-DNA_A11 Copy Number varRep 
EUR_Sardinian_LP6005441-DNA_D11_wssd EUR_Sardinian_LP6005441-DNA_D11 wssd CN EUR_Sardinian_LP6005441-DNA_D11 Copy Number varRep 
EUR_Sardinian_LP6005441-DNA_C11_wssd EUR_Sardinian_LP6005441-DNA_C11 wssd CN EUR_Sardinian_LP6005441-DNA_C11 Copy Number varRep 
EUR_Samaritan_LP6005592-DNA_D04_wssd EUR_Samaritan_LP6005592-DNA_D04 wssd CN EUR_Samaritan_LP6005592-DNA_D04 Copy Number varRep 
EUR_Saami_LP6005592-DNA_D01_wssd EUR_Saami_LP6005592-DNA_D01 wssd CN EUR_Saami_LP6005592-DNA_D01 Copy Number varRep 
EUR_Saami_LP6005592-DNA_C01_wssd EUR_Saami_LP6005592-DNA_C01 wssd CN EUR_Saami_LP6005592-DNA_C01 Copy Number varRep 
EUR_Russian_LP6005441-DNA_H10_wssd EUR_Russian_LP6005441-DNA_H10 wssd CN EUR_Russian_LP6005441-DNA_H10 Copy Number varRep 
EUR_Russian_LP6005441-DNA_G10_wssd EUR_Russian_LP6005441-DNA_G10 wssd CN EUR_Russian_LP6005441-DNA_G10 Copy Number varRep 
EUR_Polish_LP6005592-DNA_E02_wssd EUR_Polish_LP6005592-DNA_E02 wssd CN EUR_Polish_LP6005592-DNA_E02 Copy Number varRep 
EUR_Palestinian_LP6005592-DNA_B03_wssd EUR_Palestinian_LP6005592-DNA_B03 wssd CN EUR_Palestinian_LP6005592-DNA_B03 Copy Number varRep 
EUR_Palestinian_LP6005441-DNA_H09_wssd EUR_Palestinian_LP6005441-DNA_H09 wssd CN EUR_Palestinian_LP6005441-DNA_H09 Copy Number varRep 
EUR_Palestinian_LP6005441-DNA_G09_wssd EUR_Palestinian_LP6005441-DNA_G09 wssd CN EUR_Palestinian_LP6005441-DNA_G09 Copy Number varRep 
EUR_Orcadian_LP6005441-DNA_D09_wssd EUR_Orcadian_LP6005441-DNA_D09 wssd CN EUR_Orcadian_LP6005441-DNA_D09 Copy Number varRep 
EUR_Orcadian_LP6005441-DNA_C09_wssd EUR_Orcadian_LP6005441-DNA_C09 wssd CN EUR_Orcadian_LP6005441-DNA_C09 Copy Number varRep 
EUR_Norwegian_LP6005592-DNA_B01_wssd EUR_Norwegian_LP6005592-DNA_B01 wssd CN EUR_Norwegian_LP6005592-DNA_B01 Copy Number varRep 
EUR_North_Ossetian_LP6005443-DNA_F10_wssd EUR_North_Ossetian_LP6005443-DNA_F10 wssd CN EUR_North_Ossetian_LP6005443-DNA_F10 Copy Number varRep 
EUR_North_Ossetian_LP6005443-DNA_E10_wssd EUR_North_Ossetian_LP6005443-DNA_E10 wssd CN EUR_North_Ossetian_LP6005443-DNA_E10 Copy Number varRep 
EUR_Lezgin_LP6005442-DNA_H04_wssd EUR_Lezgin_LP6005442-DNA_H04 wssd CN EUR_Lezgin_LP6005442-DNA_H04 Copy Number varRep 
EUR_Lezgin_LP6005442-DNA_G04_wssd EUR_Lezgin_LP6005442-DNA_G04 wssd CN EUR_Lezgin_LP6005442-DNA_G04 Copy Number varRep 
EUR_Jordanian_LP6005592-DNA_G03_wssd EUR_Jordanian_LP6005592-DNA_G03 wssd CN EUR_Jordanian_LP6005592-DNA_G03 Copy Number varRep 
EUR_Jordanian_LP6005442-DNA_F04_wssd EUR_Jordanian_LP6005442-DNA_F04 wssd CN EUR_Jordanian_LP6005442-DNA_F04 Copy Number varRep 
EUR_Jordanian_LP6005442-DNA_E04_wssd EUR_Jordanian_LP6005442-DNA_E04 wssd CN EUR_Jordanian_LP6005442-DNA_E04 Copy Number varRep 
EUR_Iraqi_Jew_LP6005592-DNA_F01_wssd EUR_Iraqi_Jew_LP6005592-DNA_F01 wssd CN EUR_Iraqi_Jew_LP6005592-DNA_F01 Copy Number varRep 
EUR_Iraqi_Jew_LP6005592-DNA_E01_wssd EUR_Iraqi_Jew_LP6005592-DNA_E01 wssd CN EUR_Iraqi_Jew_LP6005592-DNA_E01 Copy Number varRep 
EUR_Iranian_LP6005443-DNA_B10_wssd EUR_Iranian_LP6005443-DNA_B10 wssd CN EUR_Iranian_LP6005443-DNA_B10 Copy Number varRep 
EUR_Iranian_LP6005442-DNA_C04_wssd EUR_Iranian_LP6005442-DNA_C04 wssd CN EUR_Iranian_LP6005442-DNA_C04 Copy Number varRep 
EUR_Icelandic_LP6005443-DNA_B06_wssd EUR_Icelandic_LP6005443-DNA_B06 wssd CN EUR_Icelandic_LP6005443-DNA_B06 Copy Number varRep 
EUR_Icelandic_LP6005442-DNA_D08_wssd EUR_Icelandic_LP6005442-DNA_D08 wssd CN EUR_Icelandic_LP6005442-DNA_D08 Copy Number varRep 
EUR_Hungarian_LP6005442-DNA_B08_wssd EUR_Hungarian_LP6005442-DNA_B08 wssd CN EUR_Hungarian_LP6005442-DNA_B08 Copy Number varRep 
EUR_Hungarian_LP6005442-DNA_A08_wssd EUR_Hungarian_LP6005442-DNA_A08 wssd CN EUR_Hungarian_LP6005442-DNA_A08 Copy Number varRep 
EUR_Greek_LP6005443-DNA_A06_wssd EUR_Greek_LP6005443-DNA_A06 wssd CN EUR_Greek_LP6005443-DNA_A06 Copy Number varRep 
EUR_Greek_LP6005442-DNA_G07_wssd EUR_Greek_LP6005442-DNA_G07 wssd CN EUR_Greek_LP6005442-DNA_G07 Copy Number varRep 
EUR_Georgian_LP6005442-DNA_B04_wssd EUR_Georgian_LP6005442-DNA_B04 wssd CN EUR_Georgian_LP6005442-DNA_B04 Copy Number varRep 
EUR_Georgian_LP6005442-DNA_A04_wssd EUR_Georgian_LP6005442-DNA_A04 wssd CN EUR_Georgian_LP6005442-DNA_A04 Copy Number varRep 
EUR_French_LP6005441-DNA_B05_wssd EUR_French_LP6005441-DNA_B05 wssd CN EUR_French_LP6005441-DNA_B05 Copy Number varRep 
EUR_French_LP6005441-DNA_A05_wssd EUR_French_LP6005441-DNA_A05 wssd CN EUR_French_LP6005441-DNA_A05 Copy Number varRep 
EUR_Finnish_LP6005592-DNA_A02_wssd EUR_Finnish_LP6005592-DNA_A02 wssd CN EUR_Finnish_LP6005592-DNA_A02 Copy Number varRep 
EUR_Finnish_LP6005442-DNA_D10_wssd EUR_Finnish_LP6005442-DNA_D10 wssd CN EUR_Finnish_LP6005442-DNA_D10 Copy Number varRep 
EUR_Finnish_LP6005442-DNA_C10_wssd EUR_Finnish_LP6005442-DNA_C10 wssd CN EUR_Finnish_LP6005442-DNA_C10 Copy Number varRep 
EUR_Estonian_LP6005442-DNA_H03_wssd EUR_Estonian_LP6005442-DNA_H03 wssd CN EUR_Estonian_LP6005442-DNA_H03 Copy Number varRep 
EUR_Estonian_LP6005442-DNA_G03_wssd EUR_Estonian_LP6005442-DNA_G03 wssd CN EUR_Estonian_LP6005442-DNA_G03 Copy Number varRep 
EUR_English_LP6005442-DNA_F10_wssd EUR_English_LP6005442-DNA_F10 wssd CN EUR_English_LP6005442-DNA_F10 Copy Number varRep 
EUR_English_LP6005442-DNA_E10_wssd EUR_English_LP6005442-DNA_E10 wssd CN EUR_English_LP6005442-DNA_E10 Copy Number varRep 
EUR_Druze_LP6005443-DNA_D01_wssd EUR_Druze_LP6005443-DNA_D01 wssd CN EUR_Druze_LP6005443-DNA_D01 Copy Number varRep 
EUR_Druze_LP6005441-DNA_G04_wssd EUR_Druze_LP6005441-DNA_G04 wssd CN EUR_Druze_LP6005441-DNA_G04 Copy Number varRep 
EUR_Czech_LP6005443-DNA_H05_wssd EUR_Czech_LP6005443-DNA_H05 wssd CN EUR_Czech_LP6005443-DNA_H05 Copy Number varRep 
EUR_Crete_LP6007069-DNA_A01_wssd EUR_Crete_LP6007069-DNA_A01 wssd CN EUR_Crete_LP6007069-DNA_A01 Copy Number varRep 
EUR_Crete_LP6007068-DNA_A01_wssd EUR_Crete_LP6007068-DNA_A01 wssd CN EUR_Crete_LP6007068-DNA_A01 Copy Number varRep 
EUR_Chechen_LP6005442-DNA_D03_wssd EUR_Chechen_LP6005442-DNA_D03 wssd CN EUR_Chechen_LP6005442-DNA_D03 Copy Number varRep 
EUR_Bulgarian_LP6005442-DNA_B03_wssd EUR_Bulgarian_LP6005442-DNA_B03 wssd CN EUR_Bulgarian_LP6005442-DNA_B03 Copy Number varRep 
EUR_Bulgarian_LP6005442-DNA_A03_wssd EUR_Bulgarian_LP6005442-DNA_A03 wssd CN EUR_Bulgarian_LP6005442-DNA_A03 Copy Number varRep 
EUR_Bergamo_LP6005441-DNA_B06_wssd EUR_Bergamo_LP6005441-DNA_B06 wssd CN EUR_Bergamo_LP6005441-DNA_B06 Copy Number varRep 
EUR_Bergamo_LP6005441-DNA_A06_wssd EUR_Bergamo_LP6005441-DNA_A06 wssd CN EUR_Bergamo_LP6005441-DNA_A06 Copy Number varRep 
EUR_BedouinB_LP6005441-DNA_F02_wssd EUR_BedouinB_LP6005441-DNA_F02 wssd CN EUR_BedouinB_LP6005441-DNA_F02 Copy Number varRep 
EUR_BedouinB_LP6005441-DNA_E02_wssd EUR_BedouinB_LP6005441-DNA_E02 wssd CN EUR_BedouinB_LP6005441-DNA_E02 Copy Number varRep 
EUR_Basque_LP6005441-DNA_D02_wssd EUR_Basque_LP6005441-DNA_D02 wssd CN EUR_Basque_LP6005441-DNA_D02 Copy Number varRep 
EUR_Basque_LP6005441-DNA_C02_wssd EUR_Basque_LP6005441-DNA_C02 wssd CN EUR_Basque_LP6005441-DNA_C02 Copy Number varRep 
EUR_Armenian_LP6005519-DNA_F03_wssd EUR_Armenian_LP6005519-DNA_F03 wssd CN EUR_Armenian_LP6005519-DNA_F03 Copy Number varRep 
EUR_Armenian_LP6005442-DNA_G02_wssd EUR_Armenian_LP6005442-DNA_G02 wssd CN EUR_Armenian_LP6005442-DNA_G02 Copy Number varRep 
EUR_Albanian_LP6005677-DNA_B01_wssd EUR_Albanian_LP6005677-DNA_B01 wssd CN EUR_Albanian_LP6005677-DNA_B01 Copy Number varRep 
EUR_Adygei_LP6005441-DNA_B01_wssd EUR_Adygei_LP6005441-DNA_B01 wssd CN EUR_Adygei_LP6005441-DNA_B01 Copy Number varRep 
EUR_Adygei_LP6005441-DNA_A01_wssd EUR_Adygei_LP6005441-DNA_A01 wssd CN EUR_Adygei_LP6005441-DNA_A01 Copy Number varRep 
EUR_Abkhasian_LP6005442-DNA_D02_wssd EUR_Abkhasian_LP6005442-DNA_D02 wssd CN EUR_Abkhasian_LP6005442-DNA_D02 Copy Number varRep 
EUR_Abkhasian_LP6005442-DNA_C02_wssd EUR_Abkhasian_LP6005442-DNA_C02 wssd CN EUR_Abkhasian_LP6005442-DNA_C02 Copy Number varRep 
EAS_Yi_LP6005442-DNA_H01_wssd EAS_Yi_LP6005442-DNA_H01 wssd CN EAS_Yi_LP6005442-DNA_H01 Copy Number varRep 
EAS_Yi_LP6005442-DNA_G01_wssd EAS_Yi_LP6005442-DNA_G01 wssd CN EAS_Yi_LP6005442-DNA_G01 Copy Number varRep 
EAS_Xibo_LP6005443-DNA_C02_wssd EAS_Xibo_LP6005443-DNA_C02 wssd CN EAS_Xibo_LP6005443-DNA_C02 Copy Number varRep 
EAS_Xibo_LP6005442-DNA_D01_wssd EAS_Xibo_LP6005442-DNA_D01 wssd CN EAS_Xibo_LP6005442-DNA_D01 Copy Number varRep 
EAS_Uygur_LP6005443-DNA_B02_wssd EAS_Uygur_LP6005443-DNA_B02 wssd CN EAS_Uygur_LP6005443-DNA_B02 Copy Number varRep 
EAS_Uygur_LP6005442-DNA_B01_wssd EAS_Uygur_LP6005442-DNA_B01 wssd CN EAS_Uygur_LP6005442-DNA_B01 Copy Number varRep 
EAS_Tujia_LP6005443-DNA_A02_wssd EAS_Tujia_LP6005443-DNA_A02 wssd CN EAS_Tujia_LP6005443-DNA_A02 Copy Number varRep 
EAS_Tujia_LP6005441-DNA_F12_wssd EAS_Tujia_LP6005441-DNA_F12 wssd CN EAS_Tujia_LP6005441-DNA_F12 Copy Number varRep 
EAS_Tu_LP6005443-DNA_H01_wssd EAS_Tu_LP6005443-DNA_H01 wssd CN EAS_Tu_LP6005443-DNA_H01 Copy Number varRep 
EAS_Tu_LP6005441-DNA_D12_wssd EAS_Tu_LP6005441-DNA_D12 wssd CN EAS_Tu_LP6005441-DNA_D12 Copy Number varRep 
EAS_Thai_LP6005443-DNA_B07_wssd EAS_Thai_LP6005443-DNA_B07 wssd CN EAS_Thai_LP6005443-DNA_B07 Copy Number varRep 
EAS_Thai_LP6005443-DNA_A07_wssd EAS_Thai_LP6005443-DNA_A07 wssd CN EAS_Thai_LP6005443-DNA_A07 Copy Number varRep 
EAS_She_LP6005443-DNA_G01_wssd EAS_She_LP6005443-DNA_G01 wssd CN EAS_She_LP6005443-DNA_G01 Copy Number varRep 
EAS_She_LP6005443-DNA_F01_wssd EAS_She_LP6005443-DNA_F01 wssd CN EAS_She_LP6005443-DNA_F01 Copy Number varRep 
EAS_Oroqen_LP6005441-DNA_F09_wssd EAS_Oroqen_LP6005441-DNA_F09 wssd CN EAS_Oroqen_LP6005441-DNA_F09 Copy Number varRep 
EAS_Oroqen_LP6005441-DNA_E09_wssd EAS_Oroqen_LP6005441-DNA_E09 wssd CN EAS_Oroqen_LP6005441-DNA_E09 Copy Number varRep 
EAS_Naxi_LP6005443-DNA_E09_wssd EAS_Naxi_LP6005443-DNA_E09 wssd CN EAS_Naxi_LP6005443-DNA_E09 Copy Number varRep 
EAS_Naxi_LP6005441-DNA_B09_wssd EAS_Naxi_LP6005441-DNA_B09 wssd CN EAS_Naxi_LP6005441-DNA_B09 Copy Number varRep 
EAS_Naxi_LP6005441-DNA_A09_wssd EAS_Naxi_LP6005441-DNA_A09 wssd CN EAS_Naxi_LP6005441-DNA_A09 Copy Number varRep 
EAS_Miao_LP6005441-DNA_D08_wssd EAS_Miao_LP6005441-DNA_D08 wssd CN EAS_Miao_LP6005441-DNA_D08 Copy Number varRep 
EAS_Miao_LP6005441-DNA_C08_wssd EAS_Miao_LP6005441-DNA_C08 wssd CN EAS_Miao_LP6005441-DNA_C08 Copy Number varRep 
EAS_Lahu_LP6005443-DNA_E01_wssd EAS_Lahu_LP6005443-DNA_E01 wssd CN EAS_Lahu_LP6005443-DNA_E01 Copy Number varRep 
EAS_Lahu_LP6005441-DNA_B07_wssd EAS_Lahu_LP6005441-DNA_B07 wssd CN EAS_Lahu_LP6005441-DNA_B07 Copy Number varRep 
EAS_Korean_LP6005443-DNA_D06_wssd EAS_Korean_LP6005443-DNA_D06 wssd CN EAS_Korean_LP6005443-DNA_D06 Copy Number varRep 
EAS_Korean_LP6005443-DNA_C06_wssd EAS_Korean_LP6005443-DNA_C06 wssd CN EAS_Korean_LP6005443-DNA_C06 Copy Number varRep 
EAS_Kinh_LP6005442-DNA_D11_wssd EAS_Kinh_LP6005442-DNA_D11 wssd CN EAS_Kinh_LP6005442-DNA_D11 Copy Number varRep 
EAS_Kinh_LP6005442-DNA_C11_wssd EAS_Kinh_LP6005442-DNA_C11 wssd CN EAS_Kinh_LP6005442-DNA_C11 Copy Number varRep 
EAS_Japanese_LP6005592-DNA_C02_wssd EAS_Japanese_LP6005592-DNA_C02 wssd CN EAS_Japanese_LP6005592-DNA_C02 Copy Number varRep 
EAS_Japanese_LP6005441-DNA_D06_wssd EAS_Japanese_LP6005441-DNA_D06 wssd CN EAS_Japanese_LP6005441-DNA_D06 Copy Number varRep 
EAS_Japanese_LP6005441-DNA_C06_wssd EAS_Japanese_LP6005441-DNA_C06 wssd CN EAS_Japanese_LP6005441-DNA_C06 Copy Number varRep 
EAS_Hezhen_LP6005441-DNA_H05_wssd EAS_Hezhen_LP6005441-DNA_H05 wssd CN EAS_Hezhen_LP6005441-DNA_H05 Copy Number varRep 
EAS_Hezhen_LP6005441-DNA_G05_wssd EAS_Hezhen_LP6005441-DNA_G05 wssd CN EAS_Hezhen_LP6005441-DNA_G05 Copy Number varRep 
EAS_Han_LP6005441-DNA_D05_wssd EAS_Han_LP6005441-DNA_D05 wssd CN EAS_Han_LP6005441-DNA_D05 Copy Number varRep 
EAS_Han_LP6005441-DNA_C05_wssd EAS_Han_LP6005441-DNA_C05 wssd CN EAS_Han_LP6005441-DNA_C05 Copy Number varRep 
EAS_Daur_LP6005443-DNA_C01_wssd EAS_Daur_LP6005443-DNA_C01 wssd CN EAS_Daur_LP6005443-DNA_C01 Copy Number varRep 
EAS_Daur_LP6005441-DNA_F04_wssd EAS_Daur_LP6005441-DNA_F04 wssd CN EAS_Daur_LP6005441-DNA_F04 Copy Number varRep 
EAS_Dai_LP6005592-DNA_D03_wssd EAS_Dai_LP6005592-DNA_D03 wssd CN EAS_Dai_LP6005592-DNA_D03 Copy Number varRep 
EAS_Dai_LP6005443-DNA_B01_wssd EAS_Dai_LP6005443-DNA_B01 wssd CN EAS_Dai_LP6005443-DNA_B01 Copy Number varRep 
EAS_Dai_LP6005441-DNA_D04_wssd EAS_Dai_LP6005441-DNA_D04 wssd CN EAS_Dai_LP6005441-DNA_D04 Copy Number varRep 
EAS_Cambodian_LP6005441-DNA_H03_wssd EAS_Cambodian_LP6005441-DNA_H03 wssd CN EAS_Cambodian_LP6005441-DNA_H03 Copy Number varRep 
EAS_Cambodian_LP6005441-DNA_G03_wssd EAS_Cambodian_LP6005441-DNA_G03 wssd CN EAS_Cambodian_LP6005441-DNA_G03 Copy Number varRep 
EAS_Burmese_LP6005519-DNA_B06_wssd EAS_Burmese_LP6005519-DNA_B06 wssd CN EAS_Burmese_LP6005519-DNA_B06 Copy Number varRep 
EAS_Burmese_LP6005519-DNA_A06_wssd EAS_Burmese_LP6005519-DNA_A06 wssd CN EAS_Burmese_LP6005519-DNA_A06 Copy Number varRep 
EAS_Atayal_LP6005442-DNA_E07_wssd EAS_Atayal_LP6005442-DNA_E07 wssd CN EAS_Atayal_LP6005442-DNA_E07 Copy Number varRep 
EAS_Ami_LP6005443-DNA_G05_wssd EAS_Ami_LP6005443-DNA_G05 wssd CN EAS_Ami_LP6005443-DNA_G05 Copy Number varRep 
EAS_Ami_LP6005442-DNA_C07_wssd EAS_Ami_LP6005442-DNA_C07 wssd CN EAS_Ami_LP6005442-DNA_C07 Copy Number varRep 
AMR_Zapotec_LP6005677-DNA_D01_wssd AMR_Zapotec_LP6005677-DNA_D01 wssd CN AMR_Zapotec_LP6005677-DNA_D01 Copy Number varRep 
AMR_Zapotec_LP6005443-DNA_A12_wssd AMR_Zapotec_LP6005443-DNA_A12 wssd CN AMR_Zapotec_LP6005443-DNA_A12 Copy Number varRep 
AMR_Surui_LP6005441-DNA_B12_wssd AMR_Surui_LP6005441-DNA_B12 wssd CN AMR_Surui_LP6005441-DNA_B12 Copy Number varRep 
AMR_Surui_LP6005441-DNA_A12_wssd AMR_Surui_LP6005441-DNA_A12 wssd CN AMR_Surui_LP6005441-DNA_A12 Copy Number varRep 
AMR_Quechua_LP6005677-DNA_F01_wssd AMR_Quechua_LP6005677-DNA_F01 wssd CN AMR_Quechua_LP6005677-DNA_F01 Copy Number varRep 
AMR_Quechua_LP6005677-DNA_E01_wssd AMR_Quechua_LP6005677-DNA_E01 wssd CN AMR_Quechua_LP6005677-DNA_E01 Copy Number varRep 
AMR_Quechua_LP6005519-DNA_G02_wssd AMR_Quechua_LP6005519-DNA_G02 wssd CN AMR_Quechua_LP6005519-DNA_G02 Copy Number varRep 
AMR_Pima_LP6005441-DNA_F10_wssd AMR_Pima_LP6005441-DNA_F10 wssd CN AMR_Pima_LP6005441-DNA_F10 Copy Number varRep 
AMR_Pima_LP6005441-DNA_E10_wssd AMR_Pima_LP6005441-DNA_E10 wssd CN AMR_Pima_LP6005441-DNA_E10 Copy Number varRep 
AMR_Piapoco_LP6005441-DNA_B04_wssd AMR_Piapoco_LP6005441-DNA_B04 wssd CN AMR_Piapoco_LP6005441-DNA_B04 Copy Number varRep 
AMR_Piapoco_LP6005441-DNA_A04_wssd AMR_Piapoco_LP6005441-DNA_A04 wssd CN AMR_Piapoco_LP6005441-DNA_A04 Copy Number varRep 
AMR_Nahua_LP6005519-DNA_B03_wssd AMR_Nahua_LP6005519-DNA_B03 wssd CN AMR_Nahua_LP6005519-DNA_B03 Copy Number varRep 
AMR_Nahua_LP6005519-DNA_A03_wssd AMR_Nahua_LP6005519-DNA_A03 wssd CN AMR_Nahua_LP6005519-DNA_A03 Copy Number varRep 
AMR_Mixtec_LP6005443-DNA_H11_wssd AMR_Mixtec_LP6005443-DNA_H11 wssd CN AMR_Mixtec_LP6005443-DNA_H11 Copy Number varRep 
AMR_Mixtec_LP6005443-DNA_G11_wssd AMR_Mixtec_LP6005443-DNA_G11 wssd CN AMR_Mixtec_LP6005443-DNA_G11 Copy Number varRep 
AMR_Mixe_LP6005443-DNA_F11_wssd AMR_Mixe_LP6005443-DNA_F11 wssd CN AMR_Mixe_LP6005443-DNA_F11 Copy Number varRep 
AMR_Mixe_LP6005443-DNA_E11_wssd AMR_Mixe_LP6005443-DNA_E11 wssd CN AMR_Mixe_LP6005443-DNA_E11 Copy Number varRep 
AMR_Mayan_LP6005441-DNA_H07_wssd AMR_Mayan_LP6005441-DNA_H07 wssd CN AMR_Mayan_LP6005441-DNA_H07 Copy Number varRep 
AMR_Mayan_LP6005441-DNA_G07_wssd AMR_Mayan_LP6005441-DNA_G07 wssd CN AMR_Mayan_LP6005441-DNA_G07 Copy Number varRep 
AMR_Karitiana_LP6005441-DNA_H06_wssd AMR_Karitiana_LP6005441-DNA_H06 wssd CN AMR_Karitiana_LP6005441-DNA_H06 Copy Number varRep 
AMR_Karitiana_LP6005441-DNA_G06_wssd AMR_Karitiana_LP6005441-DNA_G06 wssd CN AMR_Karitiana_LP6005441-DNA_G06 Copy Number varRep 
AMR_Chane_LP6005519-DNA_D01_wssd AMR_Chane_LP6005519-DNA_D01 wssd CN AMR_Chane_LP6005519-DNA_D01 Copy Number varRep 
AFR_Yoruba_LP6005442-DNA_B02_wssd AFR_Yoruba_LP6005442-DNA_B02 wssd CN AFR_Yoruba_LP6005442-DNA_B02 Copy Number varRep 
AFR_Yoruba_LP6005442-DNA_A02_wssd AFR_Yoruba_LP6005442-DNA_A02 wssd CN AFR_Yoruba_LP6005442-DNA_A02 Copy Number varRep 
AFR_Tikar_South_LP6005519-DNA_D10_wssd AFR_Tikar_South_LP6005519-DNA_D10 wssd CN AFR_Tikar_South_LP6005519-DNA_D10 Copy Number varRep 
AFR_Tikar_South_LP6005519-DNA_C10_wssd AFR_Tikar_South_LP6005519-DNA_C10 wssd CN AFR_Tikar_South_LP6005519-DNA_C10 Copy Number varRep 
AFR_Somali_LP6005442-DNA_D09_wssd AFR_Somali_LP6005442-DNA_D09 wssd CN AFR_Somali_LP6005442-DNA_D09 Copy Number varRep 
AFR_Sengwer_LP6005519-DNA_G11_wssd AFR_Sengwer_LP6005519-DNA_G11 wssd CN AFR_Sengwer_LP6005519-DNA_G11 Copy Number varRep 
AFR_Sandawe_LP6005519-DNA_H12_wssd AFR_Sandawe_LP6005519-DNA_H12 wssd CN AFR_Sandawe_LP6005519-DNA_H12 Copy Number varRep 
AFR_Saharawi_LP6005619-DNA_C01_wssd AFR_Saharawi_LP6005619-DNA_C01 wssd CN AFR_Saharawi_LP6005619-DNA_C01 Copy Number varRep 
AFR_Saharawi_LP6005619-DNA_B01_wssd AFR_Saharawi_LP6005619-DNA_B01 wssd CN AFR_Saharawi_LP6005619-DNA_B01 Copy Number varRep 
AFR_Rendille_LP6005519-DNA_F11_wssd AFR_Rendille_LP6005519-DNA_F11 wssd CN AFR_Rendille_LP6005519-DNA_F11 Copy Number varRep 
AFR_Rendille_LP6005519-DNA_E11_wssd AFR_Rendille_LP6005519-DNA_E11 wssd CN AFR_Rendille_LP6005519-DNA_E11 Copy Number varRep 
AFR_Ogiek_LP6005519-DNA_D11_wssd AFR_Ogiek_LP6005519-DNA_D11 wssd CN AFR_Ogiek_LP6005519-DNA_D11 Copy Number varRep 
AFR_Ogiek_LP6005519-DNA_C11_wssd AFR_Ogiek_LP6005519-DNA_C11 wssd CN AFR_Ogiek_LP6005519-DNA_C11 Copy Number varRep 
AFR_Ngumba_LP6005519-DNA_F10_wssd AFR_Ngumba_LP6005519-DNA_F10 wssd CN AFR_Ngumba_LP6005519-DNA_F10 Copy Number varRep 
AFR_Ngumba_LP6005519-DNA_E10_wssd AFR_Ngumba_LP6005519-DNA_E10 wssd CN AFR_Ngumba_LP6005519-DNA_E10 Copy Number varRep 
AFR_Mursi_LP6005519-DNA_B10_wssd AFR_Mursi_LP6005519-DNA_B10 wssd CN AFR_Mursi_LP6005519-DNA_B10 Copy Number varRep 
AFR_Mursi_LP6005519-DNA_A10_wssd AFR_Mursi_LP6005519-DNA_A10 wssd CN AFR_Mursi_LP6005519-DNA_A10 Copy Number varRep 
AFR_Mozabite_LP6005441-DNA_H08_wssd AFR_Mozabite_LP6005441-DNA_H08 wssd CN AFR_Mozabite_LP6005441-DNA_H08 Copy Number varRep 
AFR_Mozabite_LP6005441-DNA_G08_wssd AFR_Mozabite_LP6005441-DNA_G08 wssd CN AFR_Mozabite_LP6005441-DNA_G08 Copy Number varRep 
AFR_Mende_LP6005442-DNA_H11_wssd AFR_Mende_LP6005442-DNA_H11 wssd CN AFR_Mende_LP6005442-DNA_H11 Copy Number varRep 
AFR_Mende_LP6005442-DNA_G11_wssd AFR_Mende_LP6005442-DNA_G11 wssd CN AFR_Mende_LP6005442-DNA_G11 Copy Number varRep 
AFR_Mbuti_SS6004471_wssd AFR_Mbuti_SS6004471 wssd CN AFR_Mbuti_SS6004471 Copy Number varRep 
AFR_Mbuti_LP6005592-DNA_C03_wssd AFR_Mbuti_LP6005592-DNA_C03 wssd CN AFR_Mbuti_LP6005592-DNA_C03 Copy Number varRep 
AFR_Mbuti_LP6005441-DNA_B08_wssd AFR_Mbuti_LP6005441-DNA_B08 wssd CN AFR_Mbuti_LP6005441-DNA_B08 Copy Number varRep 
AFR_Mbuti_LP6005441-DNA_A08_wssd AFR_Mbuti_LP6005441-DNA_A08 wssd CN AFR_Mbuti_LP6005441-DNA_A08 Copy Number varRep 
AFR_Masai_LP6005443-DNA_F06_wssd AFR_Masai_LP6005443-DNA_F06 wssd CN AFR_Masai_LP6005443-DNA_F06 Copy Number varRep 
AFR_Masai_LP6005443-DNA_E06_wssd AFR_Masai_LP6005443-DNA_E06 wssd CN AFR_Masai_LP6005443-DNA_E06 Copy Number varRep 
AFR_Mandenka_LP6005441-DNA_F07_wssd AFR_Mandenka_LP6005441-DNA_F07 wssd CN AFR_Mandenka_LP6005441-DNA_F07 Copy Number varRep 
AFR_Mandenka_LP6005441-DNA_E07_wssd AFR_Mandenka_LP6005441-DNA_E07 wssd CN AFR_Mandenka_LP6005441-DNA_E07 Copy Number varRep 
AFR_Mada_LP6005519-DNA_B08_wssd AFR_Mada_LP6005519-DNA_B08 wssd CN AFR_Mada_LP6005519-DNA_B08 Copy Number varRep 
AFR_Luo_LP6005677-DNA_G01_wssd AFR_Luo_LP6005677-DNA_G01 wssd CN AFR_Luo_LP6005677-DNA_G01 Copy Number varRep 
AFR_Luo_LP6005442-DNA_F09_wssd AFR_Luo_LP6005442-DNA_F09 wssd CN AFR_Luo_LP6005442-DNA_F09 Copy Number varRep 
AFR_Luhya_LP6005442-DNA_F11_wssd AFR_Luhya_LP6005442-DNA_F11 wssd CN AFR_Luhya_LP6005442-DNA_F11 Copy Number varRep 
AFR_Luhya_LP6005442-DNA_E11_wssd AFR_Luhya_LP6005442-DNA_E11 wssd CN AFR_Luhya_LP6005442-DNA_E11 Copy Number varRep 
AFR_Lemande_LP6005677-DNA_D04_wssd AFR_Lemande_LP6005677-DNA_D04 wssd CN AFR_Lemande_LP6005677-DNA_D04 Copy Number varRep 
AFR_Lemande_LP6005677-DNA_C04_wssd AFR_Lemande_LP6005677-DNA_C04 wssd CN AFR_Lemande_LP6005677-DNA_C04 Copy Number varRep 
AFR_Laka_LP6005677-DNA_E04_wssd AFR_Laka_LP6005677-DNA_E04 wssd CN AFR_Laka_LP6005677-DNA_E04 Copy Number varRep 
AFR_Laka_LP6005519-DNA_G08_wssd AFR_Laka_LP6005519-DNA_G08 wssd CN AFR_Laka_LP6005519-DNA_G08 Copy Number varRep 
AFR_Kongo_LP6005519-DNA_A09_wssd AFR_Kongo_LP6005519-DNA_A09 wssd CN AFR_Kongo_LP6005519-DNA_A09 Copy Number varRep 
AFR_Kikuyu_LP6005519-DNA_B11_wssd AFR_Kikuyu_LP6005519-DNA_B11 wssd CN AFR_Kikuyu_LP6005519-DNA_B11 Copy Number varRep 
AFR_Kikuyu_LP6005519-DNA_A11_wssd AFR_Kikuyu_LP6005519-DNA_A11 wssd CN AFR_Kikuyu_LP6005519-DNA_A11 Copy Number varRep 
AFR_Khomani_San_LP6005677-DNA_D03_wssd AFR_Khomani_San_LP6005677-DNA_D03 wssd CN AFR_Khomani_San_LP6005677-DNA_D03 Copy Number varRep 
AFR_Khomani_San_LP6005592-DNA_C05_wssd AFR_Khomani_San_LP6005592-DNA_C05 wssd CN AFR_Khomani_San_LP6005592-DNA_C05 Copy Number varRep 
AFR_Kaba_LP6005519-DNA_D08_wssd AFR_Kaba_LP6005519-DNA_D08 wssd CN AFR_Kaba_LP6005519-DNA_D08 Copy Number varRep 
AFR_Ju_hoan_North_LP6005443-DNA_G08_wssd AFR_Ju_hoan_North_LP6005443-DNA_G08 wssd CN AFR_Ju_hoan_North_LP6005443-DNA_G08 Copy Number varRep 
AFR_Ju_hoan_North_LP6005441-DNA_B11_wssd AFR_Ju_hoan_North_LP6005441-DNA_B11 wssd CN AFR_Ju_hoan_North_LP6005441-DNA_B11 Copy Number varRep 
AFR_Ju_hoan_North_LP6005441-DNA_A11_wssd AFR_Ju_hoan_North_LP6005441-DNA_A11 wssd CN AFR_Ju_hoan_North_LP6005441-DNA_A11 Copy Number varRep 
AFR_Igbo_LP6005519-DNA_B12_wssd AFR_Igbo_LP6005519-DNA_B12 wssd CN AFR_Igbo_LP6005519-DNA_B12 Copy Number varRep 
AFR_Igbo_LP6005519-DNA_A12_wssd AFR_Igbo_LP6005519-DNA_A12 wssd CN AFR_Igbo_LP6005519-DNA_A12 Copy Number varRep 
AFR_Hadza_LP6005519-DNA_C12_wssd AFR_Hadza_LP6005519-DNA_C12 wssd CN AFR_Hadza_LP6005519-DNA_C12 Copy Number varRep 
AFR_Gambian_LP6005442-DNA_H10_wssd AFR_Gambian_LP6005442-DNA_H10 wssd CN AFR_Gambian_LP6005442-DNA_H10 Copy Number varRep 
AFR_Gambian_LP6005442-DNA_G10_wssd AFR_Gambian_LP6005442-DNA_G10 wssd CN AFR_Gambian_LP6005442-DNA_G10 Copy Number varRep 
AFR_Fulani_LP6005677-DNA_B04_wssd AFR_Fulani_LP6005677-DNA_B04 wssd CN AFR_Fulani_LP6005677-DNA_B04 Copy Number varRep 
AFR_Fulani_LP6005677-DNA_A04_wssd AFR_Fulani_LP6005677-DNA_A04 wssd CN AFR_Fulani_LP6005677-DNA_A04 Copy Number varRep 
AFR_Esan_LP6005442-DNA_B10_wssd AFR_Esan_LP6005442-DNA_B10 wssd CN AFR_Esan_LP6005442-DNA_B10 Copy Number varRep 
AFR_Esan_LP6005442-DNA_A10_wssd AFR_Esan_LP6005442-DNA_A10 wssd CN AFR_Esan_LP6005442-DNA_A10 Copy Number varRep 
AFR_Elmolo_LP6005519-DNA_H10_wssd AFR_Elmolo_LP6005519-DNA_H10 wssd CN AFR_Elmolo_LP6005519-DNA_H10 Copy Number varRep 
AFR_Elmolo_LP6005519-DNA_G10_wssd AFR_Elmolo_LP6005519-DNA_G10 wssd CN AFR_Elmolo_LP6005519-DNA_G10 Copy Number varRep 
AFR_Dinka_SS6004480_wssd AFR_Dinka_SS6004480 wssd CN AFR_Dinka_SS6004480 Copy Number varRep 
AFR_Dinka_LP6005443-DNA_H08_wssd AFR_Dinka_LP6005443-DNA_H08 wssd CN AFR_Dinka_LP6005443-DNA_H08 Copy Number varRep 
AFR_Dinka_LP6005443-DNA_B09_wssd AFR_Dinka_LP6005443-DNA_B09 wssd CN AFR_Dinka_LP6005443-DNA_B09 Copy Number varRep 
AFR_Bulala_LP6005519-DNA_F08_wssd AFR_Bulala_LP6005519-DNA_F08 wssd CN AFR_Bulala_LP6005519-DNA_F08 Copy Number varRep 
AFR_Bulala_LP6005519-DNA_E08_wssd AFR_Bulala_LP6005519-DNA_E08 wssd CN AFR_Bulala_LP6005519-DNA_E08 Copy Number varRep 
AFR_Biaka_LP6005441-DNA_H02_wssd AFR_Biaka_LP6005441-DNA_H02 wssd CN AFR_Biaka_LP6005441-DNA_H02 Copy Number varRep 
AFR_Biaka_LP6005441-DNA_G02_wssd AFR_Biaka_LP6005441-DNA_G02 wssd CN AFR_Biaka_LP6005441-DNA_G02 Copy Number varRep 
AFR_Bedzan_LP6005677-DNA_H03_wssd AFR_Bedzan_LP6005677-DNA_H03 wssd CN AFR_Bedzan_LP6005677-DNA_H03 Copy Number varRep 
AFR_BantuTswana_LP6005443-DNA_G02_wssd AFR_BantuTswana_LP6005443-DNA_G02 wssd CN AFR_BantuTswana_LP6005443-DNA_G02 Copy Number varRep 
AFR_BantuTswana_LP6005443-DNA_F02_wssd AFR_BantuTswana_LP6005443-DNA_F02 wssd CN AFR_BantuTswana_LP6005443-DNA_F02 Copy Number varRep 
AFR_BantuKenya_LP6005443-DNA_A01_wssd AFR_BantuKenya_LP6005443-DNA_A01 wssd CN AFR_BantuKenya_LP6005443-DNA_A01 Copy Number varRep 
AFR_BantuKenya_LP6005441-DNA_B02_wssd AFR_BantuKenya_LP6005441-DNA_B02 wssd CN AFR_BantuKenya_LP6005441-DNA_B02 Copy Number varRep 
AFR_BantuHerero_LP6005443-DNA_E02_wssd AFR_BantuHerero_LP6005443-DNA_E02 wssd CN AFR_BantuHerero_LP6005443-DNA_E02 Copy Number varRep 
AFR_BantuHerero_LP6005441-DNA_F01_wssd AFR_BantuHerero_LP6005441-DNA_F01 wssd CN AFR_BantuHerero_LP6005441-DNA_F01 Copy Number varRep 
AFR_Bakola_LP6005677-DNA_G03_wssd AFR_Bakola_LP6005677-DNA_G03 wssd CN AFR_Bakola_LP6005677-DNA_G03 Copy Number varRep 
AFR_Bakola_LP6005677-DNA_F03_wssd AFR_Bakola_LP6005677-DNA_F03 wssd CN AFR_Bakola_LP6005677-DNA_F03 Copy Number varRep 
AFR_Baka_LP6005677-DNA_E03_wssd AFR_Baka_LP6005677-DNA_E03 wssd CN AFR_Baka_LP6005677-DNA_E03 Copy Number varRep 
AFR_Baka_LP6005519-DNA_G06_wssd AFR_Baka_LP6005519-DNA_G06 wssd CN AFR_Baka_LP6005519-DNA_G06 Copy Number varRep 
AFR_Amhara_LP6005519-DNA_H09_wssd AFR_Amhara_LP6005519-DNA_H09 wssd CN AFR_Amhara_LP6005519-DNA_H09 Copy Number varRep 
AFR_Amhara_LP6005519-DNA_G09_wssd AFR_Amhara_LP6005519-DNA_G09 wssd CN AFR_Amhara_LP6005519-DNA_G09 Copy Number varRep 
AFR_Agaw_LP6005519-DNA_F09_wssd AFR_Agaw_LP6005519-DNA_F09 wssd CN AFR_Agaw_LP6005519-DNA_F09 Copy Number varRep 
AFR_Agaw_LP6005519-DNA_E09_wssd AFR_Agaw_LP6005519-DNA_E09 wssd CN AFR_Agaw_LP6005519-DNA_E09 Copy Number varRep 
AFR_Aari_LP6005519-DNA_D09_wssd AFR_Aari_LP6005519-DNA_D09 wssd CN AFR_Aari_LP6005519-DNA_D09 Copy Number varRep 
sgdpCopyNumber_subset SGDP copy number subset SGDP copy number estimates subset varRep Description This track represents copy number estimates form the Simons Genome Diversity Project. Copy number is estimated over 500 bp windows of uniquely mappable sequence. Sequences are colored from cold to hot (0 - 120+) and exact copy can be found by clicking on the region of interest. Code Availability GitHub Copy Number Key Copy numberColor0&#9632;1&#9632;2&#9632;3&#9632;4&#9632;5&#9632;6&#9632;7&#9632;8&#9632;9&#9632;10&#9632;20&#9632;30&#9632;40&#9632;50&#9632;60&#9632;70&#9632;80&#9632;90&#9632;100&#9632;110&#9632;120&#9632; Credits Please feel free to contact William Harvey or Mitchell Vollger with any questions and/or concerns regarding this track. References Bailey JA, Gu Z, Clark RA, Reinert K, Samonte RV, Schwartz S, Adams MD, Myers EW, Li PW, Eichler EE. Recent segmental duplications in the human genome. Science 2002 Pendleton AL, Shen F, Taravella AM, Emery S, Veeramah KR, Boyko AR, Kidd JM. Comparison of village dog and wolf genomes highlights the role of the neural crest in dog domestication. BMC Biol. 2018 Sudmant PH, Mallick S, Nelson BJ, Hormozdiari F, Krumm N, Huddleston J, et al. Global diversity, population stratification, and selection of human copy-number variation. Science. 2015 Sudmant PH, Kitzman JO, Antonacci F, Alkan C, Malig M, Tsalenko A, et al. Diversity of human copy number. Science. 2010 Sample table SampleRead file LP6005441-DNA_A01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_A01.srt.aln.bam LP6005441-DNA_A03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_A03.srt.aln.bam LP6005441-DNA_A04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_A04.srt.aln.bam LP6005441-DNA_A05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_A05.srt.aln.bam LP6005441-DNA_A06/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_A06.srt.aln.bam LP6005441-DNA_A08/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_A08.srt.aln.bam LP6005441-DNA_A09/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_A09.srt.aln.bam LP6005441-DNA_A10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_A10.srt.aln.bam LP6005441-DNA_A11/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_A11.srt.aln.bam LP6005441-DNA_A12/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_A12.srt.aln.bam LP6005441-DNA_B01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_B01.srt.aln.bam LP6005441-DNA_B02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_B02.srt.aln.bam LP6005441-DNA_B03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_B03.srt.aln.bam LP6005441-DNA_B04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_B04.srt.aln.bam LP6005441-DNA_B05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_B05.srt.aln.bam LP6005441-DNA_B06/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_B06.srt.aln.bam LP6005441-DNA_B07/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_B07.srt.aln.bam LP6005441-DNA_B08/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_B08.srt.aln.bam LP6005441-DNA_B09/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_B09.srt.aln.bam LP6005441-DNA_B10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_B10.srt.aln.bam LP6005441-DNA_B11/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_B11.srt.aln.bam LP6005441-DNA_B12/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_B12.srt.aln.bam LP6005441-DNA_C02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_C02.srt.aln.bam LP6005441-DNA_C03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_C03.srt.aln.bam LP6005441-DNA_C05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_C05.srt.aln.bam LP6005441-DNA_C06/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_C06.srt.aln.bam LP6005441-DNA_C07/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_C07.srt.aln.bam LP6005441-DNA_C08/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_C08.srt.aln.bam LP6005441-DNA_C09/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_C09.srt.aln.bam LP6005441-DNA_C10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_C10.srt.aln.bam LP6005441-DNA_C11/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_C11.srt.aln.bam LP6005441-DNA_D01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_D01.srt.aln.bam LP6005441-DNA_D02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_D02.srt.aln.bam LP6005441-DNA_D03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_D03.srt.aln.bam LP6005441-DNA_D04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_D04.srt.aln.bam LP6005441-DNA_D05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_D05.srt.aln.bam LP6005441-DNA_D06/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_D06.srt.aln.bam LP6005441-DNA_D07/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_D07.srt.aln.bam LP6005441-DNA_D08/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_D08.srt.aln.bam LP6005441-DNA_D09/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_D09.srt.aln.bam LP6005441-DNA_D10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_D10.srt.aln.bam LP6005441-DNA_D11/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_D11.srt.aln.bam LP6005441-DNA_D12/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_D12.srt.aln.bam LP6005441-DNA_E02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_E02.srt.aln.bam LP6005441-DNA_E03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_E03.srt.aln.bam LP6005441-DNA_E05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_E05.srt.aln.bam LP6005441-DNA_E06/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_E06.srt.aln.bam LP6005441-DNA_E07/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_E07.srt.aln.bam LP6005441-DNA_E08/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_E08.srt.aln.bam LP6005441-DNA_E09/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_E09.srt.aln.bam LP6005441-DNA_E10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_E10.srt.aln.bam LP6005441-DNA_F01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_F01.srt.aln.bam LP6005441-DNA_F02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_F02.srt.aln.bam LP6005441-DNA_F03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_F03.srt.aln.bam LP6005441-DNA_F04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_F04.srt.aln.bam LP6005441-DNA_F05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_F05.srt.aln.bam LP6005441-DNA_F06/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_F06.srt.aln.bam LP6005441-DNA_F07/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_F07.srt.aln.bam LP6005441-DNA_F08/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_F08.srt.aln.bam LP6005441-DNA_F09/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_F09.srt.aln.bam LP6005441-DNA_F10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_F10.srt.aln.bam LP6005441-DNA_F12/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_F12.srt.aln.bam LP6005441-DNA_G02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_G02.srt.aln.bam LP6005441-DNA_G03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_G03.srt.aln.bam LP6005441-DNA_G04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_G04.srt.aln.bam LP6005441-DNA_G05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_G05.srt.aln.bam LP6005441-DNA_G06/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_G06.srt.aln.bam LP6005441-DNA_G07/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_G07.srt.aln.bam LP6005441-DNA_G08/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_G08.srt.aln.bam LP6005441-DNA_G09/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_G09.srt.aln.bam LP6005441-DNA_G10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_G10.srt.aln.bam LP6005441-DNA_G11/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_G11.srt.aln.bam LP6005441-DNA_G12/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_G12.srt.aln.bam LP6005441-DNA_H02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_H02.srt.aln.bam LP6005441-DNA_H03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_H03.srt.aln.bam LP6005441-DNA_H05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_H05.srt.aln.bam LP6005441-DNA_H06/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_H06.srt.aln.bam LP6005441-DNA_H07/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_H07.srt.aln.bam LP6005441-DNA_H08/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_H08.srt.aln.bam LP6005441-DNA_H09/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_H09.srt.aln.bam LP6005441-DNA_H10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_H10.srt.aln.bam LP6005441-DNA_H11/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_H11.srt.aln.bam LP6005441-DNA_H12/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005441-DNA_H12.srt.aln.bam LP6005442-DNA_A02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005442-DNA_A02.srt.aln.bam LP6005442-DNA_A03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005442-DNA_A03.srt.aln.bam LP6005442-DNA_A04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005442-DNA_A04.srt.aln.bam LP6005442-DNA_A08/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005442-DNA_A08.srt.aln.bam LP6005442-DNA_A09/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005442-DNA_A09.srt.aln.bam LP6005442-DNA_A10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005442-DNA_A10.srt.aln.bam LP6005442-DNA_A11/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005442-DNA_A11.srt.aln.bam LP6005442-DNA_A12/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005442-DNA_A12.srt.aln.bam LP6005442-DNA_B01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005442-DNA_B01.srt.aln.bam LP6005442-DNA_B02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005442-DNA_B02.srt.aln.bam LP6005442-DNA_B03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005442-DNA_B03.srt.aln.bam LP6005442-DNA_B04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005442-DNA_B04.srt.aln.bam LP6005442-DNA_B08/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005442-DNA_B08.srt.aln.bam LP6005442-DNA_B10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005442-DNA_B10.srt.aln.bam LP6005442-DNA_B11/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005442-DNA_B11.srt.aln.bam LP6005442-DNA_B12/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005442-DNA_B12.srt.aln.bam LP6005442-DNA_C02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005442-DNA_C02.srt.aln.bam LP6005442-DNA_C04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005442-DNA_C04.srt.aln.bam LP6005442-DNA_C07/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005442-DNA_C07.srt.aln.bam LP6005442-DNA_C10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005442-DNA_C10.srt.aln.bam LP6005442-DNA_C11/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005442-DNA_C11.srt.aln.bam LP6005442-DNA_D01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005442-DNA_D01.srt.aln.bam LP6005442-DNA_D02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005442-DNA_D02.srt.aln.bam LP6005442-DNA_D03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005442-DNA_D03.srt.aln.bam 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LP6005519-DNA_C06/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_C06.srt.aln.bam LP6005519-DNA_C10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_C10.srt.aln.bam LP6005519-DNA_C11/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_C11.srt.aln.bam LP6005519-DNA_C12/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_C12.srt.aln.bam LP6005519-DNA_D01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_D01.srt.aln.bam LP6005519-DNA_D03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_D03.srt.aln.bam LP6005519-DNA_D04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_D04.srt.aln.bam LP6005519-DNA_D05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_D05.srt.aln.bam LP6005519-DNA_D06/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_D06.srt.aln.bam LP6005519-DNA_D08/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_D08.srt.aln.bam LP6005519-DNA_D09/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_D09.srt.aln.bam LP6005519-DNA_D10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_D10.srt.aln.bam LP6005519-DNA_D11/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_D11.srt.aln.bam LP6005519-DNA_E04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_E04.srt.aln.bam LP6005519-DNA_E05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_E05.srt.aln.bam LP6005519-DNA_E06/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_E06.srt.aln.bam LP6005519-DNA_E08/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_E08.srt.aln.bam LP6005519-DNA_E09/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_E09.srt.aln.bam LP6005519-DNA_E10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_E10.srt.aln.bam LP6005519-DNA_E11/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_E11.srt.aln.bam LP6005519-DNA_F03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_F03.srt.aln.bam LP6005519-DNA_F04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_F04.srt.aln.bam LP6005519-DNA_F06/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_F06.srt.aln.bam LP6005519-DNA_F08/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_F08.srt.aln.bam LP6005519-DNA_F09/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_F09.srt.aln.bam LP6005519-DNA_F10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_F10.srt.aln.bam LP6005519-DNA_F11/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_F11.srt.aln.bam LP6005519-DNA_G02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_G02.srt.aln.bam LP6005519-DNA_G03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_G03.srt.aln.bam LP6005519-DNA_G04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_G04.srt.aln.bam LP6005519-DNA_G06/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_G06.srt.aln.bam LP6005519-DNA_G08/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_G08.srt.aln.bam LP6005519-DNA_G09/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_G09.srt.aln.bam LP6005519-DNA_G10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_G10.srt.aln.bam LP6005519-DNA_G11/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_G11.srt.aln.bam LP6005519-DNA_H03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_H03.srt.aln.bam LP6005519-DNA_H04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_H04.srt.aln.bam LP6005519-DNA_H09/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_H09.srt.aln.bam LP6005519-DNA_H10/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_H10.srt.aln.bam LP6005519-DNA_H12/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005519-DNA_H12.srt.aln.bam LP6005592-DNA_A02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_A02.srt.aln.bam LP6005592-DNA_A04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_A04.srt.aln.bam LP6005592-DNA_B01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_B01.srt.aln.bam LP6005592-DNA_B02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_B02.srt.aln.bam LP6005592-DNA_B03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_B03.srt.aln.bam LP6005592-DNA_B04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_B04.srt.aln.bam LP6005592-DNA_C01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_C01.srt.aln.bam LP6005592-DNA_C02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_C02.srt.aln.bam LP6005592-DNA_C03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_C03.srt.aln.bam LP6005592-DNA_C05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_C05.srt.aln.bam LP6005592-DNA_D01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_D01.srt.aln.bam LP6005592-DNA_D03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_D03.srt.aln.bam LP6005592-DNA_D04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_D04.srt.aln.bam LP6005592-DNA_E01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_E01.srt.aln.bam LP6005592-DNA_E02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_E02.srt.aln.bam LP6005592-DNA_E05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_E05.srt.aln.bam LP6005592-DNA_F01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_F01.srt.aln.bam LP6005592-DNA_F03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_F03.srt.aln.bam LP6005592-DNA_F05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_F05.srt.aln.bam LP6005592-DNA_G01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_G01.srt.aln.bam LP6005592-DNA_G03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_G03.srt.aln.bam LP6005592-DNA_G05/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_G05.srt.aln.bam LP6005592-DNA_H01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_H01.srt.aln.bam LP6005592-DNA_H03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005592-DNA_H03.srt.aln.bam LP6005619-DNA_A01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005619-DNA_A01.srt.aln.bam LP6005619-DNA_B01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005619-DNA_B01.srt.aln.bam LP6005619-DNA_C01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005619-DNA_C01.srt.aln.bam LP6005619-DNA_D01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005619-DNA_D01.srt.aln.bam LP6005677-DNA_A02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_A02.srt.aln.bam LP6005677-DNA_A03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_A03.srt.aln.bam LP6005677-DNA_A04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_A04.srt.aln.bam LP6005677-DNA_B01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_B01.srt.aln.bam LP6005677-DNA_B02/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_B02.srt.aln.bam LP6005677-DNA_B04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_B04.srt.aln.bam LP6005677-DNA_C03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_C03.srt.aln.bam LP6005677-DNA_C04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_C04.srt.aln.bam LP6005677-DNA_D01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_D01.srt.aln.bam LP6005677-DNA_D03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_D03.srt.aln.bam LP6005677-DNA_D04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_D04.srt.aln.bam LP6005677-DNA_E01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_E01.srt.aln.bam LP6005677-DNA_E03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_E03.srt.aln.bam LP6005677-DNA_E04/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_E04.srt.aln.bam LP6005677-DNA_F01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_F01.srt.aln.bam LP6005677-DNA_F03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_F03.srt.aln.bam LP6005677-DNA_G01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_G01.srt.aln.bam LP6005677-DNA_G03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_G03.srt.aln.bam LP6005677-DNA_H03/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6005677-DNA_H03.srt.aln.bam LP6007068-DNA_A01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6007068-DNA_A01.srt.aln.bam LP6007069-DNA_A01/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/LP6007069-DNA_A01.srt.aln.bam SS6004471/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/SS6004471.srt.aln.bam SS6004477/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/SS6004477.srt.aln.bam SS6004478/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/SS6004478.srt.aln.bam SS6004480/net/eichler/vol28/projects/short_read_cohorts/nobackups/SGDP/SS6004480.srt.aln.bam CHM13_kmer/net/eichler/vol27/projects/hprc/nobackups/analysis/kmer_fastcn/input/kmer/CHM13_v2.0/CHM13_v2.0.fa.gz GRCh38_kmer/net/eichler/vol27/projects/hprc/nobackups/analysis/kmer_fastcn/input/kmer/GRCh38/GRCh38.fa.gz 
subset_GRCh38_kmer_wssd GRCh38_kmer wssd CN GRCh38_kmer Copy Number varRep 
subset_CHM13_kmer_wssd CHM13_kmer wssd CN CHM13_kmer Copy Number varRep 
subset_SAS_Makrani_LP6005441-DNA_C07_wssd SAS_Makrani_LP6005441-DNA_C07 wssd CN SAS_Makrani_LP6005441-DNA_C07 Copy Number varRep 
subset_SAS_Brahui_LP6005441-DNA_C03_wssd SAS_Brahui_LP6005441-DNA_C03 wssd CN SAS_Brahui_LP6005441-DNA_C03 Copy Number varRep 
subset_OCN_Papuan_LP6005441-DNA_B10_wssd OCN_Papuan_LP6005441-DNA_B10 wssd CN OCN_Papuan_LP6005441-DNA_B10 Copy Number varRep 
subset_OCN_Papuan_LP6005441-DNA_A10_wssd OCN_Papuan_LP6005441-DNA_A10 wssd CN OCN_Papuan_LP6005441-DNA_A10 Copy Number varRep 
subset_OCN_Bougainville_LP6005441-DNA_B03_wssd OCN_Bougainville_LP6005441-DNA_B03 wssd CN OCN_Bougainville_LP6005441-DNA_B03 Copy Number varRep 
subset_OCN_Bougainville_LP6005441-DNA_A03_wssd OCN_Bougainville_LP6005441-DNA_A03 wssd CN OCN_Bougainville_LP6005441-DNA_A03 Copy Number varRep 
subset_EUR_French_LP6005441-DNA_B05_wssd EUR_French_LP6005441-DNA_B05 wssd CN EUR_French_LP6005441-DNA_B05 Copy Number varRep 
subset_EUR_French_LP6005441-DNA_A05_wssd EUR_French_LP6005441-DNA_A05 wssd CN EUR_French_LP6005441-DNA_A05 Copy Number varRep 
subset_EUR_Bergamo_LP6005441-DNA_B06_wssd EUR_Bergamo_LP6005441-DNA_B06 wssd CN EUR_Bergamo_LP6005441-DNA_B06 Copy Number varRep 
subset_EUR_Bergamo_LP6005441-DNA_A06_wssd EUR_Bergamo_LP6005441-DNA_A06 wssd CN EUR_Bergamo_LP6005441-DNA_A06 Copy Number varRep 
subset_EUR_Basque_LP6005441-DNA_C02_wssd EUR_Basque_LP6005441-DNA_C02 wssd CN EUR_Basque_LP6005441-DNA_C02 Copy Number varRep 
subset_EUR_Adygei_LP6005441-DNA_B01_wssd EUR_Adygei_LP6005441-DNA_B01 wssd CN EUR_Adygei_LP6005441-DNA_B01 Copy Number varRep 
subset_EUR_Adygei_LP6005441-DNA_A01_wssd EUR_Adygei_LP6005441-DNA_A01 wssd CN EUR_Adygei_LP6005441-DNA_A01 Copy Number varRep 
subset_EAS_Naxi_LP6005441-DNA_B09_wssd EAS_Naxi_LP6005441-DNA_B09 wssd CN EAS_Naxi_LP6005441-DNA_B09 Copy Number varRep 
subset_EAS_Naxi_LP6005441-DNA_A09_wssd EAS_Naxi_LP6005441-DNA_A09 wssd CN EAS_Naxi_LP6005441-DNA_A09 Copy Number varRep 
subset_EAS_Lahu_LP6005441-DNA_B07_wssd EAS_Lahu_LP6005441-DNA_B07 wssd CN EAS_Lahu_LP6005441-DNA_B07 Copy Number varRep 
subset_EAS_Japanese_LP6005441-DNA_C06_wssd EAS_Japanese_LP6005441-DNA_C06 wssd CN EAS_Japanese_LP6005441-DNA_C06 Copy Number varRep 
subset_EAS_Han_LP6005441-DNA_C05_wssd EAS_Han_LP6005441-DNA_C05 wssd CN EAS_Han_LP6005441-DNA_C05 Copy Number varRep 
subset_AMR_Surui_LP6005441-DNA_B12_wssd AMR_Surui_LP6005441-DNA_B12 wssd CN AMR_Surui_LP6005441-DNA_B12 Copy Number varRep 
subset_AMR_Surui_LP6005441-DNA_A12_wssd AMR_Surui_LP6005441-DNA_A12 wssd CN AMR_Surui_LP6005441-DNA_A12 Copy Number varRep 
subset_AMR_Piapoco_LP6005441-DNA_B04_wssd AMR_Piapoco_LP6005441-DNA_B04 wssd CN AMR_Piapoco_LP6005441-DNA_B04 Copy Number varRep 
subset_AMR_Piapoco_LP6005441-DNA_A04_wssd AMR_Piapoco_LP6005441-DNA_A04 wssd CN AMR_Piapoco_LP6005441-DNA_A04 Copy Number varRep 
subset_AFR_Mbuti_LP6005441-DNA_B08_wssd AFR_Mbuti_LP6005441-DNA_B08 wssd CN AFR_Mbuti_LP6005441-DNA_B08 Copy Number varRep 
subset_AFR_Mbuti_LP6005441-DNA_A08_wssd AFR_Mbuti_LP6005441-DNA_A08 wssd CN AFR_Mbuti_LP6005441-DNA_A08 Copy Number varRep 
subset_AFR_Ju_hoan_North_LP6005441-DNA_B11_wssd AFR_Ju_hoan_North_LP6005441-DNA_B11 wssd CN AFR_Ju_hoan_North_LP6005441-DNA_B11 Copy Number varRep 
subset_AFR_Ju_hoan_North_LP6005441-DNA_A11_wssd AFR_Ju_hoan_North_LP6005441-DNA_A11 wssd CN AFR_Ju_hoan_North_LP6005441-DNA_A11 Copy Number varRep 
subset_AFR_BantuKenya_LP6005441-DNA_B02_wssd AFR_BantuKenya_LP6005441-DNA_B02 wssd CN AFR_BantuKenya_LP6005441-DNA_B02 Copy Number varRep