MATCHCLIP: locate precise breakpoints for copy number variation using CIGAR string by matching soft clipped reads

Wu, Yinghua; Tian, Lifeng; Pirastu, Mario; Stambolian, Dwight; Li, Hongzhe

doi:10.3389/fgene.2013.00157

Cited by 16 publications

(15 citation statements)

References 34 publications

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“…Four other packages which use split-read information to infer breakpoints were also used in this study. Pindel ( Ye et al, 2009 ), DELLY ( Rausch et al, 2012 ), SoftSearch ( Hart et al, 2013 ), and MATCHCLIP ( Wu et al, 2013 ). Pindel uses paired-end information as its main approach.…”

Section: Methods and Datamentioning

confidence: 99%

“…DELLY also uses paired-end information to obtain putative boundaries of SVs, and then infers breakpoints by aligning unmapped or partially mapped reads around the SVs to the reference genome. In contrast, both SoftSearch and MATCHCLIP do not use the realignment step, they use split-read information directly from BAM files to infer breakpoints ( Hart et al, 2013 ; Wu et al, 2013 ). SoftSearch and MATCHCLIP are single-sample based tools (one sample in each analysis) while Pindel and DELLY can use single or multiple samples.…”

Section: Methods and Datamentioning

confidence: 99%

“…For example, if the mapped distance of two reads is larger/smaller than their expected distance then this indicates a deletion/insertion event ( Korbel et al, 2007 ; Chen et al, 2009 ; Zeitouni et al, 2010 ; Rausch et al, 2012 ; Hart et al, 2013 ). Reads which are aligned across breakpoints are usually split into separate parts and only some parts are mapped, therefore split read based pipelines use the information from these reads to infer SV events and their breakpoints ( Ye et al, 2009 ; Rausch et al, 2012 ; Hart et al, 2013 ; Wu et al, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

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SRBreak: A Read-Depth and Split-Read Framework to Identify Breakpoints of Different Events Inside Simple Copy-Number Variable Regions

Nguyen¹,

Boocock

Merriman

et al. 2016

Front. Genet.

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Copy-number variation (CNV) has been associated with increased risk of complex diseases. High-throughput sequencing (HTS) technologies facilitate the detection of copy-number variable regions (CNVRs) and their breakpoints. This helps in understanding genome structure as well as their evolution process. Various approaches have been proposed for detecting CNV breakpoints, but currently it is still challenging for tools based on a single analysis method to identify breakpoints of CNVs. It has been shown, however, that pipelines which integrate multiple approaches are able to report more reliable breakpoints. Here, based on HTS data, we have developed a pipeline to identify approximate breakpoints (±10 bp) relating to different ancestral events within a specific CNVR. The pipeline combines read-depth and split-read information to infer breakpoints, using information from multiple samples to allow an imputation approach to be taken. The main steps involve using a normal mixture model to cluster samples into different groups, followed by simple kernel-based approaches to maximize information obtained from read-depth and split-read approaches, after which common breakpoints of groups are inferred. The pipeline uses split-read information directly from CIGAR strings of BAM files, without using a re-alignment step. On simulated data sets, it was able to report breakpoints for very low-coverage samples including those for which only single-end reads were available. When applied to three loci from existing human resequencing data sets (NEGR1, LCE3, IRGM) the pipeline obtained good concordance with results from the 1000 Genomes Project (92, 100, and 82%, respectively). The package is available at https://github.com/hoangtn/SRBreak, and also as a docker-based application at https://registry.hub.docker.com/u/hoangtn/srbreak/.

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Section: Methods and Datamentioning

confidence: 99%

Section: Methods and Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

SRBreak: A Read-Depth and Split-Read Framework to Identify Breakpoints of Different Events Inside Simple Copy-Number Variable Regions

Nguyen¹,

Boocock

Merriman

et al. 2016

Front. Genet.

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“…This approach is computationally taxing, so different methods use different heuristics to guide read alignment. MATCHCLIP (Wu et al, 2013 ) studies CIGAR strings (Li et al, 2009 ) to find reads with long soft clipped segments that overlap. The Pindel tool (Ye et al, 2009 ) looks for paired reads for which one read did not align to the reference, then searches nearby for split read mapping of the unaligned read.…”

Section: Introductionmentioning

confidence: 99%

Identification of copy number variants in whole-genome data using Reference Coverage Profiles

et al. 2015

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The identification of DNA copy numbers from short-read sequencing data remains a challenge for both technical and algorithmic reasons. The raw data for these analyses are measured in tens to hundreds of gigabytes per genome; transmitting, storing, and analyzing such large files is cumbersome, particularly for methods that analyze several samples simultaneously. We developed a very efficient representation of depth of coverage (150–1000× compression) that enables such analyses. Current methods for analyzing variants in whole-genome sequencing (WGS) data frequently miss copy number variants (CNVs), particularly hemizygous deletions in the 1–100 kb range. To fill this gap, we developed a method to identify CNVs in individual genomes, based on comparison to joint profiles pre-computed from a large set of genomes. We analyzed depth of coverage in over 6000 high quality (>40×) genomes. The depth of coverage has strong sequence-specific fluctuations only partially explained by global parameters like %GC. To account for these fluctuations, we constructed multi-genome profiles representing the observed or inferred diploid depth of coverage at each position along the genome. These Reference Coverage Profiles (RCPs) take into account the diverse technologies and pipeline versions used. Normalization of the scaled coverage to the RCP followed by hidden Markov model (HMM) segmentation enables efficient detection of CNVs and large deletions in individual genomes. Use of pre-computed multi-genome coverage profiles improves our ability to analyze each individual genome. We make available RCPs and tools for performing these analyses on personal genomes. We expect the increased sensitivity and specificity for individual genome analysis to be critical for achieving clinical-grade genome interpretation.

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“…Next generation of sequencing (NGS) allows us to identify CNVs of smaller sizes, to define the exact location of CNVs in the genome (WU et al, 2013) and to distinguish homozygous and heterozygous (WANG; BYERS, 2014). However, the method is still relatively expensive, time-consuming and with a high level of false positives (WANG; BYERS, 2014).…”

Section: Identification Of Cnvmentioning

confidence: 99%

Inherited copy number variation in the chicken genome and association with breast muscle traits

Godoy¹

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Inherited copy number variation in the chicken genome and association with breast muscle traits Copy number variation (CNV) is an important polymorphism that is associated with a wide range of traits in human, wild and livestock species. In chicken, an important source of animal protein and a developmental model organism, CNV is associated with several phenotypes and evolutionary footprints. However, identification and characterization of CNV inheritance on chicken genome lacks further investigation. We screened CNVs in chicken using two distinct populations with known pedigree. In 826 broilers we identified 25,819 CNVs (4,299 deletions and 21,520 duplications) of which 21,077 were inherited, 201 showed no inheritance and 4,541 were classified as de novo CNVs. In 514 F2 animals (layer and broiler cross) we identified 21,796 CNVs (2,254 deletions and 19,543 duplications) of which 18,230 were inherited, 587 not inherited and 2,979 were classified as de novo CNVs. After a strict filtering step to remove potential false positives and negative CNVs, only 220 (4.84%) and 430 (14.43%) de novo CNVs remained in the broiler and F2 populations, respectively. A total of 33.11% (50 out of 151) of the inherited CNVs identified in ten animals were validated by sequencing data. From the validated CNVs, 64% had more than 80% of their size (bp) validated. A total of 59% and 48.8% were classified as novel CNVs regions (CNVRs) in the broiler and F2, respectively. Considering the Bonferroni-corrected p-values for multiple testing and statistically significant p-values £ 0.01, we found two CNV segments significantly associated with breast weight, one with breast weight yield, six with breast meat weight, 18 CNV segments with breast meat yield, four with breast filet weight and two with breast yield. These CNV segments that were significantly associated overlapped with 181 protein-coding genes. The CNVseg 300, that was associated with all traits and encompass six CNVRs, overlapped a total of 26 protein-coding genes. Among these genes, the gene MYL1 (Myosin Light Chain 1) is expressed in the fast skeletal muscle fibers, and the genes MLPH (Melanophilin), PRLH (Prolactin Releasing Hormone) and RAB17 (Member RAS Oncogene Family), that were associated with the lavender phenotype (feather blue-grey color) and regulation of homeothermy and the metabolism. The present study improves our knowledge about CNV in the chicken genome and provides insight in the distribution and of different classes of CNVs, i.e. inherited and de novo CNVs, in two experimental chicken populations. In addition, the genome-wide association analyses were the first performed on broiler population with breast muscle traits, that are important characteristics for poultry production. The GWAS results allow us to understand the probably relationship between some genes and CNVRs that are significantly associated with breast muscle traits.

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MATCHCLIP: locate precise breakpoints for copy number variation using CIGAR string by matching soft clipped reads

Cited by 16 publications

References 34 publications

SRBreak: A Read-Depth and Split-Read Framework to Identify Breakpoints of Different Events Inside Simple Copy-Number Variable Regions

SRBreak: A Read-Depth and Split-Read Framework to Identify Breakpoints of Different Events Inside Simple Copy-Number Variable Regions

Identification of copy number variants in whole-genome data using Reference Coverage Profiles

Inherited copy number variation in the chicken genome and association with breast muscle traits

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