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Asset Tag: structural variation

July 7, 2019

XCAVATOR: accurate detection and genotyping of copy number variants from second and third generation whole-genome sequencing experiments.

We developed a novel software package, XCAVATOR, for the identification of genomic regions involved in copy number variants/alterations (CNVs/CNAs) from short and long reads whole-genome sequencing experiments.By using simulated and real datasets we showed that our tool, based on read count approach, is capable to predict the boundaries and the absolute number of DNA copies CNVs/CNAs with high resolutions. To demonstrate the power of our software we applied it to the analysis Illumina and Pacific Bioscencies data and we compared its performance to other ten state of the art tools.All the analyses we performed demonstrate that XCAVATOR is capable to detect germline and somatic CNVs/CNAs outperforming all the other tools we compared. XCAVATOR is freely available at http://sourceforge.net/projects/xcavator/ .

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July 7, 2019

SVachra: a tool to identify genomic structural variation in mate pair sequencing data containing inward and outward facing reads.

Characterization of genomic structural variation (SV) is essential to expanding the research and clinical applications of genome sequencing. Reliance upon short DNA fragment paired end sequencing has yielded a wealth of single nucleotide variants and internal sequencing read insertions-deletions, at the cost of limited SV detection. Multi-kilobase DNA fragment mate pair sequencing has supplemented the void in SV detection, but introduced new analytic challenges requiring SV detection tools specifically designed for mate pair sequencing data. Here, we introduce SVachra – Structural Variation Assessment of CHRomosomal Aberrations, a breakpoint calling program that identifies large insertions-deletions, inversions, inter- and intra-chromosomal translocations utilizing both inward and outward facing read types generated by mate pair sequencing.We demonstrate SVachra’s utility by executing the program on large-insert (Illumina Nextera) mate pair sequencing data from the personal genome of a single subject (HS1011). An additional data set of long-read (Pacific BioSciences RSII) was also generated to validate SV calls from SVachra and other comparison SV calling programs. SVachra exhibited the highest validation rate and reported the widest distribution of SV types and size ranges when compared to other SV callers.SVachra is a highly specific breakpoint calling program that exhibits a more unbiased SV detection methodology than other callers.

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July 7, 2019

The Mobile Element Locator Tool (MELT): population-scale mobile element discovery and biology.

Mobile element insertions (MEIs) represent ~25% of all structural variants in human genomes. Moreover, when they disrupt genes, MEIs can influence human traits and diseases. Therefore, MEIs should be fully discovered along with other forms of genetic variation in whole genome sequencing (WGS) projects involving population genetics, human diseases, and clinical genomics. Here, we describe the Mobile Element Locator Tool (MELT), which was developed as part of the 1000 Genomes Project to perform MEI discovery on a population scale. Using both Illumina WGS data and simulations, we demonstrate that MELT outperforms existing MEI discovery tools in terms of speed, scalability, specificity, and sensitivity, while also detecting a broader spectrum of MEI-associated features. Several run modes were developed to perform MEI discovery on local and cloud systems. In addition to using MELT to discover MEIs in modern humans as part of the 1000 Genomes Project, we also used it to discover MEIs in chimpanzees and ancient (Neanderthal and Denisovan) hominids. We detected diverse patterns of MEI stratification across these populations that likely were caused by (1) diverse rates of MEI production from source elements, (2) diverse patterns of MEI inheritance, and (3) the introgression of ancient MEIs into modern human genomes. Overall, our study provides the most comprehensive map of MEIs to date spanning chimpanzees, ancient hominids, and modern humans and reveals new aspects of MEI biology in these lineages. We also demonstrate that MELT is a robust platform for MEI discovery and analysis in a variety of experimental settings.© 2017 Gardner et al.; Published by Cold Spring Harbor Laboratory Press.

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July 7, 2019

Structural variation offers new home for disease associations and gene discovery

Following completion of the Human Genome Project, most studies of human genetic variation have centered on single nucleotide polymorphisms (SNPs). SNPs are numerous in individual genomes and serve as useful genetic markers in association studies across a population. These markers have been leveraged to identify genetic loci for disease risk and draw associations with numerous traits of interest. Despite their usefulness, SNPs do not tell the whole story. For example, most SNPs are associated with only a small increased risk of disease, and they usually cannot identify on their own which genes are causal. This has resulted in what many researchers have referred to as missing or hidden heritability.

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July 7, 2019

Lightning-fast genome variant detection with GROM.

Current human whole genome sequencing projects produce massive amounts of data, often creating significant computational challenges. Different approaches have been developed for each type of genome variant and method of its detection, necessitating users to run multiple algorithms to find variants.We present GROM (Genome Rearrangement OmniMapper), a novel comprehensive variant detection algorithm accepting aligned read files as input and finding SNVs, indels, structural variants (SVs), and copy number variants (CNVs). We show that GROM outperforms state-of-the-art methods on seven validated benchmarks using two whole genome sequencing (WGS) datasets. Additionally, GROM boasts lightning fast run times, analyzing a 50x WGS human dataset (NA12878) on commonly available computer hardware in 11 minutes, more than an order of magnitude (up to 72 times) faster than tools detecting a similar range of variants.Addressing the needs of big data analysis, GROM combines in one algorithm SNV, indel, SV, and CNV detection providing superior speed, sensitivity, and precision. GROM is also able to detect CNVs, SNVs and indels in non-paired read WGS libraries, as well as SNVs and indels in whole exome or RNA sequencing datasets.

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July 7, 2019

GRIDSS: sensitive and specific genomic rearrangement detection using positional de Bruijn graph assembly.

The identification of genomic rearrangements with high sensitivity and specificity using massively parallel sequencing remains a major challenge, particularly in precision medicine and cancer research. Here, we describe a new method for detecting rearrangements, GRIDSS (Genome Rearrangement IDentification Software Suite). GRIDSS is a multithreaded structural variant (SV) caller that performs efficient genome-wide break-end assembly prior to variant calling using a novel positional de Bruijn graph-based assembler. By combining assembly, split read, and read pair evidence using a probabilistic scoring, GRIDSS achieves high sensitivity and specificity on simulated, cell line, and patient tumor data, recently winning SV subchallenge #5 of the ICGC-TCGA DREAM8.5 Somatic Mutation Calling Challenge. On human cell line data, GRIDSS halves the false discovery rate compared to other recent methods while matching or exceeding their sensitivity. GRIDSS identifies nontemplate sequence insertions, microhomologies, and large imperfect homologies, estimates a quality score for each breakpoint, stratifies calls into high or low confidence, and supports multisample analysis.© 2017 Cameron et al.; Published by Cold Spring Harbor Laboratory Press.

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July 7, 2019

Variant review with the Integrative Genomics Viewer.

Manual review of aligned reads for confirmation and interpretation of variant calls is an important step in many variant calling pipelines for next-generation sequencing (NGS) data. Visual inspection can greatly increase the confidence in calls, reduce the risk of false positives, and help characterize complex events. The Integrative Genomics Viewer (IGV) was one of the first tools to provide NGS data visualization, and it currently provides a rich set of tools for inspection, validation, and interpretation of NGS datasets, as well as other types of genomic data. Here, we present a short overview of IGV’s variant review features for both single-nucleotide variants and structural variants, with examples from both cancer and germline datasets. IGV is freely available at https://www.igv.org Cancer Res; 77(21); e31-34. ©2017 AACR.©2017 American Association for Cancer Research.

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July 7, 2019

Tools for annotation and comparison of structural variation.

The impact of structural variants (SVs) on a variety of organisms and diseases like cancer has become increasingly evident. Methods for SV detection when studying genomic differences across cells, individuals or populations are being actively developed. Currently, just a few methods are available to compare different SVs callsets, and no specialized methods are available to annotate SVs that account for the unique characteristics of these variant types. Here, we introduce SURVIVOR_ant, a tool that compares types and breakpoints for candidate SVs from different callsets and enables fast comparison of SVs to genomic features such as genes and repetitive regions, as well as to previously established SV datasets such as from the 1000 Genomes Project. As proof of concept we compared 16 SV callsets generated by different SV calling methods on a single genome, the Genome in a Bottle sample HG002 (Ashkenazi son), and annotated the SVs with gene annotations, 1000 Genomes Project SV calls, and four different types of repetitive regions. Computation time to annotate 134,528 SVs with 33,954 of annotations was 22 seconds on a laptop.

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July 7, 2019

Hunting structural variants: Population by population

Until recently, most population-scale genome sequencing studies have focused on identifying single nucleotide variants (SNVs) to explore genetic differences between individuals. Like so many SNV-based genome-wide association studies, however, these efforts have had difficulty identifying causative genetic mechanisms underlying most complex functions. More and more, the genomics community has realised that structural variation is likely responsible for many of the traits and phenotypes that scientists have not been able to attribute to SNVs. This class of variants, defined as genetic differences of 50 bp or larger, accounts for most of the DNA sequence differences between any two people. Structural variants (SVs) are also already known to cause many common and rare diseases including ALS, schizophrenia, leukemia, Carney complex, and Huntington’s disease. Despite the importance of SVs, these larger variants have been understudied and underreported compared to their single-nucleotide counterparts. One reason is that they remain difficult to detect. Their length often means they cannot be fully spanned using short sequencing reads. They also often occur in highly repetitive or GC-rich regions of the genome, making them challenging targets. As such, this class of human genetic variation has remained vastly under-explored in global populations and is now ripe for discovery.

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July 7, 2019

Detection of complex structural variation from paired-end sequencing data

Detecting structural variants (SVs) from sequencing data is a key problem in genome analysis, but the full diversity of SVs is not captured by most methods. We introduce the Automated Reconstruction of Complex Structural Variants (ARC-SV) method, which detects a broad class of structural variants from paired-end whole genome sequencing (WGS) data. Analysis of samples from NA12878 and HuRef suggests that complex SVs are often misclassified by traditional methods. We validated our results both experimentally and by comparison to whole genome assembly and PacBio data; ARC-SV compares favorably to existing algorithms in general and gives state-of-the-art results on complex SV detection. By expanding the range of detectable SVs compared to commonly-used algorithms, ARC-SV allows additional information to be extracted from existing WGS data.

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July 7, 2019

Contributions of Zea mays subspecies mexicana haplotypes to modern maize.

Maize was domesticated from lowland teosinte (Zea mays ssp. parviglumis), but the contribution of highland teosinte (Zea mays ssp. mexicana, hereafter mexicana) to modern maize is not clear. Here, two genomes for Mo17 (a modern maize inbred) and mexicana are assembled using a meta-assembly strategy after sequencing of 10 lines derived from a maize-teosinte cross. Comparative analyses reveal a high level of diversity between Mo17, B73, and mexicana, including three Mb-size structural rearrangements. The maize spontaneous mutation rate is estimated to be 2.17?×?10-8 ~3.87?×?10-8 per site per generation with a nonrandom distribution across the genome. A higher deleterious mutation rate is observed in the pericentromeric regions, and might be caused by differences in recombination frequency. Over 10% of the maize genome shows evidence of introgression from the mexicana genome, suggesting that mexicana contributed to maize adaptation and improvement. Our data offer a rich resource for constructing the pan-genome of Zea mays and genetic improvement of modern maize varieties.

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July 7, 2019

Hidden genetic variation shapes the structure of functional elements in Drosophila.

Mutations that add, subtract, rearrange, or otherwise refashion genome structure often affect phenotypes, although the fragmented nature of most contemporary assemblies obscures them. To discover such mutations, we assembled the first new reference-quality genome of Drosophila melanogaster since its initial sequencing. By comparing this new genome to the existing D. melanogaster assembly, we created a structural variant map of unprecedented resolution and identified extensive genetic variation that has remained hidden until now. Many of these variants constitute candidates underlying phenotypic variation, including tandem duplications and a transposable element insertion that amplifies the expression of detoxification-related genes associated with nicotine resistance. The abundance of important genetic variation that still evades discovery highlights how crucial high-quality reference genomes are to deciphering phenotypes.

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July 7, 2019

SV2: Accurate structural variation genotyping and de novo mutation detection from whole genomes.

Structural Variation (SV) detection from short-read whole genome sequencing is error prone, presenting significant challenges for population or family-based studies of disease.Here we describe SV2, a machine-learning algorithm for genotyping deletions and duplications from paired-end sequencing data. SV2 can rapidly integrate variant calls from multiple structural variant discovery algorithms into a unified call set with high genotyping accuracy and capability to detect de novo mutations. SV2 is freely available on GitHub (https://github.com/dantaki/SV2).Supplementary data are available at Bioinformatics online.© The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

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July 7, 2019

Copy number variation and expression analysis reveals a nonorthologous pinta gene family member involved in butterfly vision.

Vertebrate (cellular retinaldehyde-binding protein) and Drosophila (prolonged depolarization afterpotential is not apparent [PINTA]) proteins with a CRAL-TRIO domain transport retinal-based chromophores that bind to opsin proteins and are necessary for phototransduction. The CRAL-TRIO domain gene family is composed of genes that encode proteins with a common N-terminal structural domain. Although there is an expansion of this gene family in Lepidoptera, there is no lepidopteran ortholog of pinta. Further, the function of these genes in lepidopterans has not yet been established. Here, we explored the molecular evolution and expression of CRAL-TRIO domain genes in the butterfly Heliconius melpomene in order to identify a member of this gene family as a candidate chromophore transporter. We generated and searched a four tissue transcriptome and searched a reference genome for CRAL-TRIO domain genes. We expanded an insect CRAL-TRIO domain gene phylogeny to include H. melpomene and used 18 genomes from 4 subspecies to assess copy number variation. A transcriptome-wide differential expression analysis comparing four tissue types identified a CRAL-TRIO domain gene, Hme CTD31, upregulated in heads suggesting a potential role in vision for this CRAL-TRIO domain gene. RT-PCR and immunohistochemistry confirmed that Hme CTD31 and its protein product are expressed in the retina, specifically in primary and secondary pigment cells and in tracheal cells. Sequencing of eye protein extracts that fluoresce in the ultraviolet identified Hme CTD31 as a possible chromophore binding protein. Although we found several recent duplications and numerous copy number variants in CRAL-TRIO domain genes, we identified a single copy pinta paralog that likely binds the chromophore in butterflies.© The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

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July 7, 2019

A recurrence-based approach for validating structural variation using long-read sequencing technology.

Although numerous algorithms have been developed to identify structural variations (SVs) in genomic sequences, there is a dearth of approaches that can be used to evaluate their results. This is significant as the accurate identification of structural variation is still an outstanding but important problem in genomics. The emergence of new sequencing technologies that generate longer sequence reads can, in theory, provide direct evidence for all types of SVs regardless of the length of the region through which it spans. However, current efforts to use these data in this manner require the use of large computational resources to assemble these sequences as well as visual inspection of each region. Here we present VaPoR, a highly efficient algorithm that autonomously validates large SV sets using long-read sequencing data. We assessed the performance of VaPoR on SVs in both simulated and real genomes and report a high-fidelity rate for overall accuracy across different levels of sequence depths. We show that VaPoR can interrogate a much larger range of SVs while still matching existing methods in terms of false positive validations and providing additional features considering breakpoint precision and predicted genotype. We further show that VaPoR can run quickly and efficiency without requiring a large processing or assembly pipeline. VaPoR provides a long read-based validation approach for genomic SVs that requires relatively low read depth and computing resources and thus will provide utility with targeted or low-pass sequencing coverage for accurate SV assessment. The VaPoR Software is available at: https://github.com/mills-lab/vapor.© The Authors 2017. Published by Oxford University Press.

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