Identification of copy number variants in whole-genome data using Reference Coverage Profiles
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, particu...
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doaj-ee5ba32d4beb4615b54ca8b9ce4005a62020-11-24T21:17:56ZengFrontiers Media S.A.Frontiers in Genetics1664-80212015-02-01610.3389/fgene.2015.00045128424Identification of copy number variants in whole-genome data using Reference Coverage ProfilesGustavo eGlusman0Alissa eSeverson1Varsha eDhankani2Max eRobinson3Terry eFarrah4Denise E. Mauldin5Anna B. Stittrich6Seth A. Ament7Jared C. Roach8Mary E. Brunkow9Dale L. Bodian10Joseph G. Vockley11Ilya eShmulevich12John E. Niederhuber13Leroy eHood14Institute for Systems BiologyInstitute for Systems BiologyInstitute for Systems BiologyInstitute for Systems BiologyInstitute for Systems BiologyInstitute for Systems BiologyInstitute for Systems BiologyInstitute for Systems BiologyInstitute for Systems BiologyInstitute for Systems BiologyInova Translational Medicine InstituteInova Translational Medicine InstituteInstitute for Systems BiologyInova Translational Medicine InstituteInstitute for Systems BiologyThe 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 (150-1000x compression) that enables such analyses. Current methods for analyzing variants in whole-genome sequencing 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 (>40x) 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.http://journal.frontiersin.org/Journal/10.3389/fgene.2015.00045/fullSignal processingstructural variationWhole-genome sequencingclinical genomicsdepth of coverage |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Gustavo eGlusman Alissa eSeverson Varsha eDhankani Max eRobinson Terry eFarrah Denise E. Mauldin Anna B. Stittrich Seth A. Ament Jared C. Roach Mary E. Brunkow Dale L. Bodian Joseph G. Vockley Ilya eShmulevich John E. Niederhuber Leroy eHood |
spellingShingle |
Gustavo eGlusman Alissa eSeverson Varsha eDhankani Max eRobinson Terry eFarrah Denise E. Mauldin Anna B. Stittrich Seth A. Ament Jared C. Roach Mary E. Brunkow Dale L. Bodian Joseph G. Vockley Ilya eShmulevich John E. Niederhuber Leroy eHood Identification of copy number variants in whole-genome data using Reference Coverage Profiles Frontiers in Genetics Signal processing structural variation Whole-genome sequencing clinical genomics depth of coverage |
author_facet |
Gustavo eGlusman Alissa eSeverson Varsha eDhankani Max eRobinson Terry eFarrah Denise E. Mauldin Anna B. Stittrich Seth A. Ament Jared C. Roach Mary E. Brunkow Dale L. Bodian Joseph G. Vockley Ilya eShmulevich John E. Niederhuber Leroy eHood |
author_sort |
Gustavo eGlusman |
title |
Identification of copy number variants in whole-genome data using Reference Coverage Profiles |
title_short |
Identification of copy number variants in whole-genome data using Reference Coverage Profiles |
title_full |
Identification of copy number variants in whole-genome data using Reference Coverage Profiles |
title_fullStr |
Identification of copy number variants in whole-genome data using Reference Coverage Profiles |
title_full_unstemmed |
Identification of copy number variants in whole-genome data using Reference Coverage Profiles |
title_sort |
identification of copy number variants in whole-genome data using reference coverage profiles |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Genetics |
issn |
1664-8021 |
publishDate |
2015-02-01 |
description |
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 (150-1000x compression) that enables such analyses. Current methods for analyzing variants in whole-genome sequencing 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 (>40x) 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. |
topic |
Signal processing structural variation Whole-genome sequencing clinical genomics depth of coverage |
url |
http://journal.frontiersin.org/Journal/10.3389/fgene.2015.00045/full |
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