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Asset Tag: structural variation

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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July 7, 2019  |  

The state of whole-genome sequencing

Over the last decade, a technological paradigm shift has slashed the cost of DNA sequencing by over five orders of magnitude. Today, the cost of sequencing a human genome is a few thousand dollars, and it continues to fall. Here, we review the most cost-effective platforms for whole-genome sequencing (WGS) as well as emerging technologies that may displace or complement these. We also discuss the practical challenges of generating and analyzing WGS data, and how WGS has unlocked new strategies for discovering genes and variants underlying both rare and common human diseases.

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July 7, 2019  |  

Genomic resources and their influence on the detection of the signal of positive selection in genome scans.

Genome scans represent powerful approaches to investigate the action of natural selection on the genetic variation of natural populations and to better understand local adaptation. This is very useful, for example, in the field of conservation biology and evolutionary biology. Thanks to Next Generation Sequencing, genomic resources are growing exponentially, improving genome scan analyses in non-model species. Thousands of SNPs called using Reduced Representation Sequencing are increasingly used in genome scans. Besides, genome sequences are also becoming increasingly available, allowing better processing of short-read data, offering physical localization of variants, and improving haplotype reconstruction and data imputation. Ultimately, genome sequences are also becoming the raw material for selection inferences. Here, we discuss how the increasing availability of such genomic resources, notably genome sequences, influences the detection of signals of selection. Mainly, increasing data density and having the information of physical linkage data expand genome scans by (i) improving the overall quality of the data, (ii) helping the reconstruction of demographic history for the population studied to decrease false-positive rates and (iii) improving the statistical power of methods to detect the signal of selection. Of particular importance, the availability of a high-quality reference genome can improve the detection of the signal of selection by (i) allowing matching the potential candidate loci to linked coding regions under selection, (ii) rapidly moving the investigation to the gene and function and (iii) ensuring that the highly variable regions of the genomes that include functional genes are also investigated. For all those reasons, using reference genomes in genome scan analyses is highly recommended. © 2015 John Wiley & Sons Ltd.

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July 7, 2019  |  

Resolving complex structural genomic rearrangements using a randomized approach.

Complex chromosomal rearrangements are structural genomic alterations involving multiple instances of deletions, duplications, inversions, or translocations that co-occur either on the same chromosome or represent different overlapping events on homologous chromosomes. We present SVelter, an algorithm that identifies regions of the genome suspected to harbor a complex event and then resolves the structure by iteratively rearranging the local genome structure, in a randomized fashion, with each structure scored against characteristics of the observed sequencing data. SVelter is able to accurately reconstruct complex chromosomal rearrangements when compared to well-characterized genomes that have been deeply sequenced with both short and long reads.

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July 7, 2019  |  

Rapid evolution of citrate utilization by Escherichia coli by direct selection requires citT and dctA.

The isolation of aerobic citrate-utilizing Escherichia coli (Cit(+)) in long-term evolution experiments (LTEE) has been termed a rare, innovative, presumptive speciation event. We hypothesized that direct selection would rapidly yield the same class of E. coli Cit(+) mutants and follow the same genetic trajectory: potentiation, actualization, and refinement. This hypothesis was tested with wild-type E. coli strain B and with K-12 and three K-12 derivatives: an E. coli ?rpoS::kan mutant (impaired for stationary-phase survival), an E. coli ?citT::kan mutant (deleted for the anaerobic citrate/succinate antiporter), and an E. coli ?dctA::kan mutant (deleted for the aerobic succinate transporter). E. coli underwent adaptation to aerobic citrate metabolism that was readily and repeatedly achieved using minimal medium supplemented with citrate (M9C), M9C with 0.005% glycerol, or M9C with 0.0025% glucose. Forty-six independent E. coli Cit(+) mutants were isolated from all E. coli derivatives except the E. coli ?citT::kan mutant. Potentiation/actualization mutations occurred within as few as 12 generations, and refinement mutations occurred within 100 generations. Citrate utilization was confirmed using Simmons, Christensen, and LeMaster Richards citrate media and quantified by mass spectrometry. E. coli Cit(+) mutants grew in clumps and in long incompletely divided chains, a phenotype that was reversible in rich media. Genomic DNA sequencing of four E. coli Cit(+) mutants revealed the required sequence of mutational events leading to a refined Cit(+) mutant. These events showed amplified citT and dctA loci followed by DNA rearrangements consistent with promoter capture events for citT. These mutations were equivalent to the amplification and promoter capture CitT-activating mutations identified in the LTEE.IMPORTANCE E. coli cannot use citrate aerobically. Long-term evolution experiments (LTEE) performed by Blount et al. (Z. D. Blount, J. E. Barrick, C. J. Davidson, and R. E. Lenski, Nature 489:513-518, 2012, http://dx.doi.org/10.1038/nature11514 ) found a single aerobic, citrate-utilizing E. coli strain after 33,000 generations (15 years). This was interpreted as a speciation event. Here we show why it probably was not a speciation event. Using similar media, 46 independent citrate-utilizing mutants were isolated in as few as 12 to 100 generations. Genomic DNA sequencing revealed an amplification of the citT and dctA loci and DNA rearrangements to capture a promoter to express CitT, aerobically. These are members of the same class of mutations identified by the LTEE. We conclude that the rarity of the LTEE mutant was an artifact of the experimental conditions and not a unique evolutionary event. No new genetic information (novel gene function) evolved. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

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