SRBreak: A read-depth and split-read framework to identify breakpoints of different events inside simple copy-number variable regions
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 structures of genomes as well as their evo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2016-09-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fgene.2016.00160/full |
| _version_ | 1818841123776364544 |
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| author | HOANG T NGUYEN HOANG T NGUYEN HOANG T NGUYEN HOANG T NGUYEN James Boocock James Boocock James Boocock Tony R Merriman Tony R Merriman Mik A Black Mik A Black |
| author_facet | HOANG T NGUYEN HOANG T NGUYEN HOANG T NGUYEN HOANG T NGUYEN James Boocock James Boocock James Boocock Tony R Merriman Tony R Merriman Mik A Black Mik A Black |
| author_sort | HOANG T NGUYEN |
| collection | DOAJ |
| description | 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 structures of genomes 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 maximise 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/. |
| first_indexed | 2024-12-19T04:21:05Z |
| format | Article |
| id | doaj.art-77908b03557448e0ac877fd1daf46778 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-19T04:21:05Z |
| publishDate | 2016-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-77908b03557448e0ac877fd1daf467782022-12-21T20:36:10ZengFrontiers Media S.A.Frontiers in Genetics1664-80212016-09-01710.3389/fgene.2016.00160205146SRBreak: A read-depth and split-read framework to identify breakpoints of different events inside simple copy-number variable regionsHOANG T NGUYEN0HOANG T NGUYEN1HOANG T NGUYEN2HOANG T NGUYEN3James Boocock4James Boocock5James Boocock6Tony R Merriman7Tony R Merriman8Mik A Black9Mik A Black10Otago UniversityVirtual Institute of Statistical Genetics, New ZealandMount Sinai School of MedicineCao Thang College of TechnologyOtago UniversityVirtual Institute of Statistical Genetics, New ZealandMount Sinai School of MedicineOtago UniversityVirtual Institute of Statistical Genetics, New ZealandOtago UniversityVirtual Institute of Statistical Genetics, New ZealandCopy-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 structures of genomes 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 maximise 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/.http://journal.frontiersin.org/Journal/10.3389/fgene.2016.00160/fullCopy number variant (CNV)structural variation (SV)read depthbreakpoint cluster regionsplit read |
| spellingShingle | HOANG T NGUYEN HOANG T NGUYEN HOANG T NGUYEN HOANG T NGUYEN James Boocock James Boocock James Boocock Tony R Merriman Tony R Merriman Mik A Black Mik A Black SRBreak: A read-depth and split-read framework to identify breakpoints of different events inside simple copy-number variable regions Frontiers in Genetics Copy number variant (CNV) structural variation (SV) read depth breakpoint cluster region split read |
| title | SRBreak: A read-depth and split-read framework to identify breakpoints of different events inside simple copy-number variable regions |
| title_full | SRBreak: A read-depth and split-read framework to identify breakpoints of different events inside simple copy-number variable regions |
| title_fullStr | SRBreak: A read-depth and split-read framework to identify breakpoints of different events inside simple copy-number variable regions |
| title_full_unstemmed | SRBreak: A read-depth and split-read framework to identify breakpoints of different events inside simple copy-number variable regions |
| title_short | SRBreak: A read-depth and split-read framework to identify breakpoints of different events inside simple copy-number variable regions |
| title_sort | srbreak a read depth and split read framework to identify breakpoints of different events inside simple copy number variable regions |
| topic | Copy number variant (CNV) structural variation (SV) read depth breakpoint cluster region split read |
| url | http://journal.frontiersin.org/Journal/10.3389/fgene.2016.00160/full |
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