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129 results for “structural variants”
Data from: Population structure of two rabies hosts relative to the known distribution of rabies virus variants in Alaska
For pathogens that infect multiple species the distinction between reservoir hosts and spillover hosts is often difficult. In Alaska, three variants of the arctic rabies virus exist with distinct spatial distributions. We test the hypothesis that rabies virus variant distribution corresponds to the population structure of the primary rabies hosts in Alaska, arctic foxes (Vulpes lagopus) and red foxes (V. vulpes) in order to possibly distinguish reservoir and spill over hosts. We used mitochondrial DNA (mtDNA) sequence and nine microsatellites to assess population structure in those two species. mtDNA structure did not correspond to rabies virus variant structure in either species. Microsatellite analyses gave varying results. Bayesian clustering found 2 groups of arctic foxes in the coastal tundra region, but for red foxes it identified tundra and boreal types. Spatial Bayesian clustering and spatial principal components analysis identified 3 and 4 groups of arctic foxes, respectively, closely matching the distribution of rabies virus variants in the state. Red foxes, conversely, showed eight clusters comprising 2 regions (boreal and tundra) with much admixture. These results run contrary to previous beliefs that arctic fox show no fine-scale spatial population structure. While we cannot rule out that the red fox is part of the maintenance host community for rabies in Alaska, the distribution of virus variants appears to be driven primarily by the artic fox Therefore we show that host population genetics can be utilized to distinguish between maintenance and spillover hosts when used in conjunction with other approaches.
Dataset for "NanoVar: a Comprehensive Workflow for Structural Variant Detection to uncover the Genome's Hidden Patterns"
<h2><strong>Output Files for Long-Read Structural Variant and Repeat Analysis in Colorectal Cancer Samples (HRR698464, HRR698460, C586, C588)</strong></h2> <h3>Description:</h3> <p>This Zenodo dataset includes comprehensive output files generated during the application of a long-read sequencing analysis protocol for structural variant (SV) detection and repeat element characterization in colorectal cancer samples. The dataset is organized into two main directories:</p> <p><strong>1. HRR698464_MSI-H_Tumor</strong><br>This directory contains all primary output files generated from the analysis pipeline applied to the MSI-H tumor sample HRR698464 (also referred to as patient C586.T). Each subdirectory corresponds to a specific stage in the protocol:</p> <ul> <li>NanoPlot_output<br>Output from Stage 1 – Quality assessment of raw reads using NanoPlot.</li> <li>SAMtools_output<br>BAM file processing outputs from Stage 2 – Alignment of long reads to the reference genome using SAMtools.</li> <li>NanoVar_output<br>Output from Stage 3 – Structural variant calling using NanoVar.</li> <li>VCF_filtering_output<br>Output from Stage 4 – Filtering of structural variants using SURVIVOR and BCFtools; includes the filtered VCF files.</li> <li>NanoINSight_output<br>Output from Stage 5 – Characterization of repeat elements using NanoINSight.</li> <li>VEP_output<br>Output from Stage 6 – Annotation of structural variants using Ensembl Variant Effect Predictor (VEP).</li> </ul> <p> </p> <p><strong>2. Additional_output_files</strong><br>This directory contains supplementary output files used for comparison and visualization in Figures 4–7 of the associated publication. These include:</p> <ul> <li>HRR698460.NanoPlot.report.html<br>NanoPlot quality summary of a lower-quality tumor sample (HRR698460), used in Figure 4 for comparison with HRR698464.</li> <li>C586.N.nanovar.pass.vcf<br>NanoVar VCF output for the matched normal sample of patient C586, used to filter somatic calls in Stage 4.</li> <li>C586.N.nanovar.pass.report.html<br>NanoVar summary report of the normal sample of C586; used in Figure 5a.</li> <li>C588.N.nanovar.pass.vcf<br>NanoVar VCF output of the MSS normal sample (C588) for comparison with the MSI-H patient (C586).</li> <li>C588.N.nanovar.pass.report.html<br>NanoVar summary report of the MSS normal sample; used in Figure 5b.</li> <li>C588.T.nanovar.pass.vcf<br>NanoVar VCF output of the MSS tumor sample (C588); used in comparative analyses with the MSI-H sample.</li> <li>C588.T.nanovar.pass.report.html<br>NanoVar summary report of the MSS tumor sample; used in Figure 5b.</li> <li>MSS.tumor.unique.vcf<br>VCF file of somatic SVs in the MSS sample, generated by comparing matched tumor and normal pairs.</li> <li>MSS.tumor.unique.RepeatMasker.tbl<br>RepeatMasker output annotating somatic insertions in the MSS tumor sample; used in Figure 6.</li> <li>MSS.tumor.unique.vep.html<br>Ensembl VEP annotation report of somatic SVs in the MSS patient; used in Figures 7a and 7b.</li> <li>This dataset supports reproducibility and transparency of the protocol and offers a valuable resource for researchers interested in long-read-based SV detection, repeat annotation, and comparative cancer genomics.</li> </ul>
Quartet DNA benchmark sets for germline small variants and structural variants
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Phased VCF outputs for "HiPhase: Jointly phasing small, structural, and tandem repeat variants from HiFi sequencing"
<p>The collection of raw and processed VCF files for the paper titled: "HiPhase: Jointly phasing small, structural, and tandem repeat variants from HiFi sequencing".</p><p>Raw VCF files from DeepVariant, pbsv, and TRGT are included in the unphased subfolder. Outputs from WhatsHap and HiPhase are in their own subfolders. Three replicates of HG002 are included in all subfolders. Layout is as follows:</p><blockquote><p>hiphase_vcfs</p><p>├── all_vcfs.md5</p><p>├── hiphase</p><p>│ ├── HG002-rep1.GRCh38.deepvariant.hiphase.vcf.gz</p><p>│ ├── HG002-rep1.GRCh38.deepvariant.hiphase.vcf.gz.tbi</p><p>│ ├── HG002-rep1.GRCh38.pbsv.hiphase.vcf.gz</p><p>│ ├── HG002-rep1.GRCh38.pbsv.hiphase.vcf.gz.tbi</p><p>│ ├── HG002-rep1.GRCh38.trgt.hiphase.vcf.gz</p><p>│ ├── HG002-rep1.GRCh38.trgt.hiphase.vcf.gz.tbi</p><p>│ ├── HG002-rep2.GRCh38.deepvariant.hiphase.vcf.gz</p><p>│ ├── HG002-rep2.GRCh38.deepvariant.hiphase.vcf.gz.tbi</p><p>│ ├── HG002-rep2.GRCh38.pbsv.hiphase.vcf.gz</p><p>│ ├── HG002-rep2.GRCh38.pbsv.hiphase.vcf.gz.tbi</p><p>│ ├── HG002-rep2.GRCh38.trgt.hiphase.vcf.gz</p><p>│ ├── HG002-rep2.GRCh38.trgt.hiphase.vcf.gz.tbi</p><p>│ ├── HG002-rep3.GRCh38.deepvariant.hiphase.vcf.gz</p><p>│ ├── HG002-rep3.GRCh38.deepvariant.hiphase.vcf.gz.tbi</p><p>│ ├── HG002-rep3.GRCh38.pbsv.hiphase.vcf.gz</p><p>│ ├── HG002-rep3.GRCh38.pbsv.hiphase.vcf.gz.tbi</p><p>│ ├── HG002-rep3.GRCh38.trgt.hiphase.vcf.gz</p><p>│ └── HG002-rep3.GRCh38.trgt.hiphase.vcf.gz.tbi</p><p>├── unphased</p><p>│ ├── HG002-rep1.GRCh38.deepvariant.vcf.gz</p><p>│ ├── HG002-rep1.GRCh38.deepvariant.vcf.gz.tbi</p><p>│ ├── HG002-rep1.GRCh38.pbsv.vcf.gz</p><p>│ ├── HG002-rep1.GRCh38.pbsv.vcf.gz.tbi</p><p>│ ├── HG002-rep1.GRCh38.trgt.vcf.gz</p><p>│ ├── HG002-rep1.GRCh38.trgt.vcf.gz.tbi</p><p>│ ├── HG002-rep2.GRCh38.deepvariant.vcf.gz</p><p>│ ├── HG002-rep2.GRCh38.deepvariant.vcf.gz.tbi</p><p>│ ├── HG002-rep2.GRCh38.pbsv.vcf.gz</p><p>│ ├── HG002-rep2.GRCh38.pbsv.vcf.gz.tbi</p><p>│ ├── HG002-rep2.GRCh38.trgt.vcf.gz</p><p>│ ├── HG002-rep2.GRCh38.trgt.vcf.gz.tbi</p><p>│ ├── HG002-rep3.GRCh38.deepvariant.vcf.gz</p><p>│ ├── HG002-rep3.GRCh38.deepvariant.vcf.gz.tbi</p><p>│ ├── HG002-rep3.GRCh38.pbsv.vcf.gz</p><p>│ ├── HG002-rep3.GRCh38.pbsv.vcf.gz.tbi</p><p>│ ├── HG002-rep3.GRCh38.trgt.vcf.gz</p><p>│ └── HG002-rep3.GRCh38.trgt.vcf.gz.tbi</p><p>└── whatshap</p><p> ├── HG002-rep1.GRCh38.deepvariant.whatshap.vcf.gz</p><p> ├── HG002-rep1.GRCh38.deepvariant.whatshap.vcf.gz.tbi</p><p> ├── HG002-rep2.GRCh38.deepvariant.whatshap.vcf.gz</p><p> ├── HG002-rep2.GRCh38.deepvariant.whatshap.vcf.gz.tbi</p><p> ├── HG002-rep3.GRCh38.deepvariant.whatshap.vcf.gz</p><p> └── HG002-rep3.GRCh38.deepvariant.whatshap.vcf.gz.tbi</p><p>3 directories, 43 files</p></blockquote>
Data from: Population genomic evidence of selection on structural variants in a natural hybrid zone
<p><span>Structural variants (SVs) can promote speciation by directly causing reproductive isolation or by suppressing recombination across large genomic regions. Whereas examples of each mechanism have been documented, systematic tests of the role of SVs in speciation are lacking. Here, we take advantage of long-read (Oxford nanopore) whole-genome sequencing and a hybrid zone between two </span><em>Lycaeides</em> butterfly taxa (<em>L. melissa</em> and Jackson Hole <em>Lycaeides</em>) to comprehensively evaluate genome-wide patterns of introgression for SVs and relate these patterns to hypotheses about speciation. We found >100,000 SVs segregating within or between the two hybridizing species. SVs and SNPs exhibited similar levels of genetic differentiation between species, with the exception of inversions, which were more differentiated. We detected credible variation in patterns of introgression among SV loci in the hybrid zone, with 562 of 1419 ancestry-informative SVs exhibiting genomic clines that deviated from null expectations based on genome-average ancestry. Overall, hybrids exhibited a directional shift towards Jackson Hole <em>Lycaeides</em> ancestry at SV loci, consistent with the hypothesis that these loci experienced more selection on average than SNP loci. Surprisingly, we found that deletions, rather than inversions, showed the highest skew towards excess ancestry from Jackson Hole <em>Lycaeides</em>. Excess Jackson Hole <em>Lycaeides</em> ancestry in hybrids was also especially pronounced for Z-linked SVs and inversions containing many genes. In conclusion, our results show that SVs are ubiquitous and suggest that SVs in general, but especially deletions, might disproportionately affect hybrid fitness and thus contribute to reproductive isolation.</p>
SNiffles structural variant vcf SHRSP genome
<p>Variant cell format file generated by Sniffles2/SURVIVOR analysis</p>
Methods for structural variant detection with long-read sequencing data
<p>SV calls from different long-read based SV callers on sequencing data. SV results evaluated in Methods for structural variant detection with long-read sequencing data.</p> <p>NA24385_Pacbio_HiFi -> HiFi_L1 in paper</p> <p>NA24385_Pacbio_MtSinai -> CLR_L1 in paper</p> <p>NA24385_Pacbio_CLR_SRX7668835 -> CLR_L2 in paper</p> <p>NA24385_Pacbio_CLR_SRX6719924 -> CLR_L3 in paper</p> <p>NA24385_ONT_Promethion -> Nano_L1 in paper</p>
De novo assemblies for the manuscrip "Candida albicans isolates contain frequent heterozygous structural variants and transposable elements within genes and centromeres"
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The population genetics of structural variants in grapevine domestication
<p><strong>The genome assembly: </strong><a href="https://zenodo.org/api/files/988c0749-aec9-42fe-865e-b09b140e2068/Chardonnay.fa.fasta?versionId=86a512b8-12a6-4f4d-ac5d-acc26d74589f">Chardonnay.fa.fasta</a> </p> <p><strong>The gene annotation: </strong><a href="https://zenodo.org/api/files/988c0749-aec9-42fe-865e-b09b140e2068/Chardonnay.annotation_sorted.gff.gz?versionId=2e5878bc-cf53-489b-ac71-2602f9ba4d2e">Chardonnay.annotation_sorted.gff.gz</a></p> <p><strong>The TE annotation: </strong><a href="https://zenodo.org/api/files/988c0749-aec9-42fe-865e-b09b140e2068/Chardonnay.annotation_te_sorted.gff3.gz?versionId=95780497-e0e7-4a7b-8860-74072d7f7bb2">Chardonnay.annotation_te_sorted.gff3.gz</a></p>
Comprehensive Structural Variant Benchmark Dataset: 1100 VCF files from long-read sequencing of 10 NCBI individuals
<p>We initially collected 10 NCBI individuals: HG002 family pedigree data (HG002 [son], HG003 [father], HG004 [mother]), the HG005 family pedigree data (HG005 [son], HG006 [father], HG007 [mother]), the NA12878 subject, the HG00096 subject, the HG00512 subject and the CHM13 subject. Then we used PacBio (CLR: Continuous Long Read, CCS: Circular Consensus Sequencing) and Nanopore (ONT) platforms, 5 aligners and 10 callers to construct the pipelines, with most parameters set to default values. After that, except for 6 invalid pipelines(pbmm2-Nanovar, lra-Picky, lra-delly, lra-NanoVar, lra-NanoSV, lra-pbsv), we obtain 1100 VCF files.</p>
FIGURE 4. Piezura pardalina ssp. pardalina Rondani. A. Male genitalia external structures, posterior. B. Male mesolobus, variant form. C. Female spermatheca. D. Female genitalia, ventral. E. Male sternite 5 in A taxonomic revision of Piezura Rondani (Diptera: Fanniidae)
FIGURE 4. Piezura pardalina ssp. pardalina Rondani. A. Male genitalia external structures, posterior. B. Male mesolobus, variant form. C. Female spermatheca. D. Female genitalia, ventral. E. Male sternite 5, ventral.
Genomic structural variants constrain and facilitate adaptation in natural populations of Theobroma cacao, the Chocolate Tree
<p>Genomic structural variants (SVs) can play important roles in adaptation and speciation. Yet, the overall fitness effects of SVs are poorly understood, partly because accurate population-level identification of SVs requires multiple high-quality genome assemblies. Here, we use 31 chromosome-scale, haplotype-resolved genome assemblies of Theobroma cacao – an outcrossing, long-lived tree species that is the source of chocolate – to investigate the fitness consequences of SVs in natural populations. Among the 31 accessions, we find over 160 thousand SVs, which together cover eight times more of the genome than SNPs and short indels (125 Mb vs. 15 Mb). Our results indicate that a vast majority of these SVs are deleterious: they segregate at low frequencies and are depleted from functional regions of the genome. We show that SVs influence gene expression, which likely impairs gene function and contributes to the detrimental effects of SVs. We also provide empirical support for a theoretical prediction that SVs, particularly inversions, increase genetic load through the accumulation of deleterious nucleotide variants as a result of suppressed recombination.<br> Despite the overall detrimental effects, we identify individual SVs bearing signatures of local adaptation, several of which are associated with genes differentially expressed between populations. Genes involved in pathogen resistance are strongly enriched among these candidates, highlighting the contribution of SVs on this important local adaptation trait. Beyond revealing new empirical evidence for the evolutionary importance of SVs, these 31 de novo assemblies provide a valuable resource for genetic and breeding studies in T. cacao. </p>
Gaps and complex structurally variant loci in phased genome assemblies
<p>Supplementary data including code for an science article 'Gaps and complex structurally variant loci in phased genome assemblies'.</p>
Tradeoffs in alignment and assembly-based methods for structural variant detection with long-read sequencing data
<p>Source data for the paper "Tradeoffs in alignment and assembly-based methods for structural variant detection with long-read sequencing data"</p>
A pangenome graph reference of 30 chicken genomes allows genotyping of large and complex structural variants
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Data from: Population genomic evidence of selection on structural variants in a natural hybrid zone
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Raw genotyped total called structural variant (SV)
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Data from: Population structure of two rabies hosts relative to the known distribution of rabies virus variants in Alaska
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Data from: Geographic distribution and adaptive significance of genomic structural variants: an anthropological genetics perspective
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Data from: The role of structural genomic variants in population differentiation and ecotype formation in Timema cristinae walking sticks
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