Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Detection of structural variants and indels within exome data.

ABSTRACT: We report an algorithm to detect structural variation and indels from 1 base pair (bp) to 1 Mbp within exome sequence data sets. Splitread uses one end-anchored placements to cluster the mappings of subsequences of unanchored ends to identify the size, content and location of variants with high specificity and sensitivity. The algorithm discovers indels, structural variants, de novo events and copy number-polymorphic processed pseudogenes missed by other methods.

SUBMITTER: Karakoc E

PROVIDER: S-EPMC3269549 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Detection of structural variants and indels within exome data.

Karakoc Emre E Alkan Can C O'Roak Brian J BJ Dennis Megan Y MY Vives Laura L Mark Kenneth K Rieder Mark J MJ Nickerson Debbie A DA Eichler Evan E EE

Nature methods 20111218 2

We report an algorithm to detect structural variation and indels from 1 base pair (bp) to 1 Mbp within exome sequence data sets. Splitread uses one end-anchored placements to cluster the mappings of subsequences of unanchored ends to identify the size, content and location of variants with high specificity and sensitivity. The algorithm discovers indels, structural variants, de novo events and copy number-polymorphic processed pseudogenes missed by other methods. ...[more]

PMID: 22179552

Similar Datasets

Optimized detection of insertions/deletions (INDELs) in whole-exome sequencing data.

Project description:Insertion and deletion (INDEL) mutations, the most common type of structural variance, are associated with several human diseases. The detection of INDELs through next-generation sequencing (NGS) is becoming more common due to the decrease in costs, the increase in efficiency, and sensitivity improvements demonstrated by the various sequencing platforms and analytical tools. However, there are still many errors associated with INDEL variant calling, and distinguishing INDELs from errors in NGS remains challenging. To evaluate INDEL calling from whole-exome sequencing (WES) data, we performed Sanger sequencing for all INDELs called from the several calling algorithm. We compared the performance of the four algorithms (i.e. GATK, SAMtools, Dindel, and Freebayes) for INDEL detection from the same sample. We examined the sensitivity and PPV of GATK (90.2 and 89.5%, respectively), SAMtools (75.3 and 94.4%, respectively), Dindel (90.1 and 88.6%, respectively), and Freebayes (80.1 and 94.4%, respectively). GATK had the highest sensitivity. Furthermore, we identified INDELs with high PPV (4 algorithms intersection: 98.7%, 3 algorithms intersection: 97.6%, and GATK and SAMtools intersection INDELs: 97.6%). We presented two key sources of difficulties in accurate INDEL detection: 1) the presence of repeat, and 2) heterozygous INDELs. Herein we could suggest the accessible algorithms that selectively reduce error rates and thereby facilitate INDEL detection. Our study may also serve as a basis for understanding the accuracy and completeness of INDEL detection.

| S-EPMC5549930 | biostudies-other

SvABA: genome-wide detection of structural variants and indels by local assembly.

Project description:Structural variants (SVs), including small insertion and deletion variants (indels), are challenging to detect through standard alignment-based variant calling methods. Sequence assembly offers a powerful approach to identifying SVs, but is difficult to apply at scale genome-wide for SV detection due to its computational complexity and the difficulty of extracting SVs from assembly contigs. We describe SvABA, an efficient and accurate method for detecting SVs from short-read sequencing data using genome-wide local assembly with low memory and computing requirements. We evaluated SvABA's performance on the NA12878 human genome and in simulated and real cancer genomes. SvABA demonstrates superior sensitivity and specificity across a large spectrum of SVs and substantially improves detection performance for variants in the 20-300 bp range, compared with existing methods. SvABA also identifies complex somatic rearrangements with chains of short (<1000 bp) templated-sequence insertions copied from distant genomic regions. We applied SvABA to 344 cancer genomes from 11 cancer types and found that short templated-sequence insertions occur in ∼4% of all somatic rearrangements. Finally, we demonstrate that SvABA can identify sites of viral integration and cancer driver alterations containing medium-sized (50-300 bp) SVs.

| S-EPMC5880247 | biostudies-literature

LinkedSV for detection of mosaic structural variants from linked-read exome and genome sequencing data.

Project description:Linked-read sequencing provides long-range information on short-read sequencing data by barcoding reads originating from the same DNA molecule, and can improve detection and breakpoint identification for structural variants (SVs). Here we present LinkedSV for SV detection on linked-read sequencing data. LinkedSV considers barcode overlapping and enriched fragment endpoints as signals to detect large SVs, while it leverages read depth, paired-end signals and local assembly to detect small SVs. Benchmarking studies demonstrate that LinkedSV outperforms existing tools, especially on exome data and on somatic SVs with low variant allele frequencies. We demonstrate clinical cases where LinkedSV identifies disease-causal SVs from linked-read exome sequencing data missed by conventional exome sequencing, and show examples where LinkedSV identifies SVs missed by high-coverage long-read sequencing. In summary, LinkedSV can detect SVs missed by conventional short-read and long-read sequencing approaches, and may resolve negative cases from clinical genome/exome sequencing studies.

| S-EPMC6898185 | biostudies-literature

Leveraging multiple genomic data to prioritize disease-causing indels from exome sequencing data.

Project description:The emergence of exome sequencing in recent years has enabled rapid and cost-effective detection of genetic variants in coding regions and offers a great opportunity to combine sequencing experiments with subsequent computational analysis for dissecting genetic basis of human inherited diseases. However, this strategy, though successful in practice, still faces such challenges as limited sample size and substantial number or diversity of candidate variants. To overcome these obstacles, researchers have been concentrated in the development of advanced computational methods and have recently achieved great progress for analysing single nucleotide variant. Nevertheless, it still remains unclear on how to analyse indels, another type of genetic variant that accounts for substantial proportion of known disease-causing variants. In this paper, we proposed an integrative method to effectively identify disease-causing indels from exome sequencing data. Specifically, we put forward a statistical method to combine five functional prediction scores, four genic association scores and a genic intolerance score to produce an integrated p-value, which could then be used for prioritizing candidate indels. We performed extensive simulation studies and demonstrated that our method achieved high accuracy in uncovering disease-causing indels. Our software is available at http://bioinfo.au.tsinghua.edu.cn/jianglab/IndelPrioritizer/.

| S-EPMC5431795 | biostudies-literature

Enhanced copy number variants detection from whole-exome sequencing data using EXCAVATOR2.

Project description:Copy Number Variants (CNVs) are structural rearrangements contributing to phenotypic variation that have been proved to be associated with many disease states. Over the last years, the identification of CNVs from whole-exome sequencing (WES) data has become a common practice for research and clinical purpose and, consequently, the demand for more and more efficient and accurate methods has increased. In this paper, we demonstrate that more than 30% of WES data map outside the targeted regions and that these reads, usually discarded, can be exploited to enhance the identification of CNVs from WES experiments. Here, we present EXCAVATOR2, the first read count based tool that exploits all the reads produced by WES experiments to detect CNVs with a genome-wide resolution. To evaluate the performance of our novel tool we use it for analysing two WES data sets, a population data set sequenced by the 1000 Genomes Project and a tumor data set made of bladder cancer samples. The results obtained from these analyses demonstrate that EXCAVATOR2 outperforms other four state-of-the-art methods and that our combined approach enlarge the spectrum of detectable CNVs from WES data with an unprecedented resolution. EXCAVATOR2 is freely available at http://sourceforge.net/projects/excavator2tool/.

| S-EPMC5175347 | biostudies-literature

A practical method to detect SNVs and indels from whole genome and exome sequencing data.

Project description:The recent development of massively parallel sequencing technology has allowed the creation of comprehensive catalogs of genetic variation. However, due to the relatively high sequencing error rate for short read sequence data, sophisticated analysis methods are required to obtain high-quality variant calls. Here, we developed a probabilistic multinomial method for the detection of single nucleotide variants (SNVs) as well as short insertions and deletions (indels) in whole genome sequencing (WGS) and whole exome sequencing (WES) data for single sample calling. Evaluation with DNA genotyping arrays revealed a concordance rate of 99.98% for WGS calls and 99.99% for WES calls. Sanger sequencing of the discordant calls determined the false positive and false negative rates for the WGS (0.0068% and 0.17%) and WES (0.0036% and 0.0084%) datasets. Furthermore, short indels were identified with high accuracy (WGS: 94.7%, WES: 97.3%). We believe our method can contribute to the greater understanding of human diseases.

| S-EPMC3703611 | biostudies-literature

Whole-exome sequencing for the detection of SNPs and indels

Project description:Whole-exome sequencing for the detection of SNPs and indels

| PRJEB44814 | ENA

A novel approach for simultaneous detection of structural and single-nucleotide variants based on a combination of chromosome conformation capture and exome sequencing

Project description:Effective molecular diagnosis of congenital diseases hinges on comprehensive genomic analysis, traditionally reliant on various methodologies specific to each variant type — whole exome or genome sequencing for single nucleotide variants (SNVs), array CGH for copy-number variants (CNVs), and microscopy for structural variants (SVs). We introduce a novel, integrative approach combining exome sequencing with chromosome conformation capture, termed Exo-C. This method enables the concurrent identification of SNVs in clinically relevant genes and SVs across the genome and allows analysis of heterozygous and mosaic carriers. Enhanced with targeted long-read sequencing, Exo-C evolves into a cost-efficient solution capable of resolving complex SVs at base-pair accuracy. Through several case studies, we demonstrate how Exo-C's multifaceted application can effectively uncover diverse causative variants and elucidate disease mechanisms in patients with rare disorders.

2024-01-24 | GSE253950 | GEO

Detection of somatic structural variants from short-read next-generation sequencing data.

Project description:Somatic structural variants (SVs), which are variants that typically impact >50 nucleotides, play a significant role in cancer development and evolution but are notoriously more difficult to detect than small variants from short-read next-generation sequencing (NGS) data. This is due to a combination of challenges attributed to the purity of tumour samples, tumour heterogeneity, limitations of short-read information from NGS and sequence alignment ambiguities. In spite of active development of SV detection tools (callers) over the past few years, each method has inherent advantages and limitations. In this review, we highlight some of the important factors affecting somatic SV detection and compared the performance of seven commonly used SV callers. In particular, we focus on the extent of change in sensitivity and precision for detecting different SV types and size ranges from samples with differing variant allele frequencies and sequencing depths of coverage. We highlight the reasons for why some SV callers perform well in some settings but not others, allowing our evaluation findings to be extended beyond the seven SV callers examined in this paper. As the importance of large SVs become increasingly recognized in cancer genomics, this paper provides a timely review on some of the most impactful factors influencing somatic SV detection that should be considered when choosing SV callers.

| S-EPMC8138798 | biostudies-literature

Identification of copy number variants from exome sequence data.

Project description:BACKGROUND: With advances in next generation sequencing technologies and genomic capture techniques, exome sequencing has become a cost-effective approach for mutation detection in genetic diseases. However, computational prediction of copy number variants (CNVs) from exome sequence data is a challenging task. Whilst numerous programs are available, they have different sensitivities, and have low sensitivity to detect smaller CNVs (1-4 exons). Additionally, exonic CNV discovery using standard aCGH has limitations due to the low probe density over exonic regions. The goal of our study was to develop a protocol to detect exonic CNVs (including shorter CNVs that cover 1-4 exons), combining computational prediction algorithms and a high-resolution custom CGH array. RESULTS: We used six published CNV prediction programs (ExomeCNV, CONTRA, ExomeCopy, ExomeDepth, CoNIFER, XHMM) and an in-house modification to ExomeCopy and ExomeDepth (ExCopyDepth) for computational CNV prediction on 30 exomes from the 1000 genomes project and 9 exomes from primary immunodeficiency patients. CNV predictions were tested using a custom CGH array designed to capture all exons (exaCGH). After this validation, we next evaluated the computational prediction of shorter CNVs. ExomeCopy and the in-house modified algorithm, ExCopyDepth, showed the highest capability in detecting shorter CNVs. Finally, the performance of each computational program was assessed by calculating the sensitivity and false positive rate. CONCLUSIONS: In this paper, we assessed the ability of 6 computational programs to predict CNVs, focussing on short (1-4 exon) CNVs. We also tested these predictions using a custom array targeting exons. Based on these results, we propose a protocol to identify and confirm shorter exonic CNVs combining computational prediction algorithms and custom aCGH experiments.

| S-EPMC4132917 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data