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SV-STAT accurately detects structural variation via alignment to reference-based assemblies

BACKGROUND: Genomic deletions, inversions, and other rearrangements known collectively as structural variations (SVs) are implicated in many human disorders. Technologies for sequencing DNA provide a potentially rich source of information in which to detect breakpoints of structural variations at ba...

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Autores principales: Davis, Caleb F., Ritter, Deborah I., Wheeler, David A., Wang, Hongmei, Ding, Yan, Dugan, Shannon P., Bainbridge, Matthew N., Muzny, Donna M., Rao, Pulivarthi H., Man, Tsz-Kwong, Plon, Sharon E., Gibbs, Richard A., Lau, Ching C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4913042/
https://www.ncbi.nlm.nih.gov/pubmed/27330550
http://dx.doi.org/10.1186/s13029-016-0051-0
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author Davis, Caleb F.
Ritter, Deborah I.
Wheeler, David A.
Wang, Hongmei
Ding, Yan
Dugan, Shannon P.
Bainbridge, Matthew N.
Muzny, Donna M.
Rao, Pulivarthi H.
Man, Tsz-Kwong
Plon, Sharon E.
Gibbs, Richard A.
Lau, Ching C.
author_facet Davis, Caleb F.
Ritter, Deborah I.
Wheeler, David A.
Wang, Hongmei
Ding, Yan
Dugan, Shannon P.
Bainbridge, Matthew N.
Muzny, Donna M.
Rao, Pulivarthi H.
Man, Tsz-Kwong
Plon, Sharon E.
Gibbs, Richard A.
Lau, Ching C.
author_sort Davis, Caleb F.
collection PubMed
description BACKGROUND: Genomic deletions, inversions, and other rearrangements known collectively as structural variations (SVs) are implicated in many human disorders. Technologies for sequencing DNA provide a potentially rich source of information in which to detect breakpoints of structural variations at base-pair resolution. However, accurate prediction of SVs remains challenging, and existing informatics tools predict rearrangements with significant rates of false positives or negatives. RESULTS: To address this challenge, we developed ‘Structural Variation detection by STAck and Tail’ (SV-STAT) which implements a novel scoring metric. The software uses this statistic to quantify evidence for structural variation in genomic regions suspected of harboring rearrangements. To demonstrate SV-STAT, we used targeted and genome-wide approaches. First, we applied a custom capture array followed by Roche/454 and SV-STAT to three pediatric B-lineage acute lymphoblastic leukemias, identifying five structural variations joining known and novel breakpoint regions. Next, we detected SVs genome-wide in paired-end Illumina data collected from additional tumor samples. SV-STAT showed predictive accuracy as high as or higher than leading alternatives. The software is freely available under the terms of the GNU General Public License version 3 at https://gitorious.org/svstat/svstat. CONCLUSIONS: SV-STAT works across multiple sequencing chemistries, paired and single-end technologies, targeted or whole-genome strategies, and it complements existing SV-detection software. The method is a significant advance towards accurate detection and genotyping of genomic rearrangements from DNA sequencing data. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13029-016-0051-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-49130422016-06-20 SV-STAT accurately detects structural variation via alignment to reference-based assemblies Davis, Caleb F. Ritter, Deborah I. Wheeler, David A. Wang, Hongmei Ding, Yan Dugan, Shannon P. Bainbridge, Matthew N. Muzny, Donna M. Rao, Pulivarthi H. Man, Tsz-Kwong Plon, Sharon E. Gibbs, Richard A. Lau, Ching C. Source Code Biol Med Software BACKGROUND: Genomic deletions, inversions, and other rearrangements known collectively as structural variations (SVs) are implicated in many human disorders. Technologies for sequencing DNA provide a potentially rich source of information in which to detect breakpoints of structural variations at base-pair resolution. However, accurate prediction of SVs remains challenging, and existing informatics tools predict rearrangements with significant rates of false positives or negatives. RESULTS: To address this challenge, we developed ‘Structural Variation detection by STAck and Tail’ (SV-STAT) which implements a novel scoring metric. The software uses this statistic to quantify evidence for structural variation in genomic regions suspected of harboring rearrangements. To demonstrate SV-STAT, we used targeted and genome-wide approaches. First, we applied a custom capture array followed by Roche/454 and SV-STAT to three pediatric B-lineage acute lymphoblastic leukemias, identifying five structural variations joining known and novel breakpoint regions. Next, we detected SVs genome-wide in paired-end Illumina data collected from additional tumor samples. SV-STAT showed predictive accuracy as high as or higher than leading alternatives. The software is freely available under the terms of the GNU General Public License version 3 at https://gitorious.org/svstat/svstat. CONCLUSIONS: SV-STAT works across multiple sequencing chemistries, paired and single-end technologies, targeted or whole-genome strategies, and it complements existing SV-detection software. The method is a significant advance towards accurate detection and genotyping of genomic rearrangements from DNA sequencing data. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13029-016-0051-0) contains supplementary material, which is available to authorized users. BioMed Central 2016-06-18 /pmc/articles/PMC4913042/ /pubmed/27330550 http://dx.doi.org/10.1186/s13029-016-0051-0 Text en © Davis et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
Davis, Caleb F.
Ritter, Deborah I.
Wheeler, David A.
Wang, Hongmei
Ding, Yan
Dugan, Shannon P.
Bainbridge, Matthew N.
Muzny, Donna M.
Rao, Pulivarthi H.
Man, Tsz-Kwong
Plon, Sharon E.
Gibbs, Richard A.
Lau, Ching C.
SV-STAT accurately detects structural variation via alignment to reference-based assemblies
title SV-STAT accurately detects structural variation via alignment to reference-based assemblies
title_full SV-STAT accurately detects structural variation via alignment to reference-based assemblies
title_fullStr SV-STAT accurately detects structural variation via alignment to reference-based assemblies
title_full_unstemmed SV-STAT accurately detects structural variation via alignment to reference-based assemblies
title_short SV-STAT accurately detects structural variation via alignment to reference-based assemblies
title_sort sv-stat accurately detects structural variation via alignment to reference-based assemblies
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4913042/
https://www.ncbi.nlm.nih.gov/pubmed/27330550
http://dx.doi.org/10.1186/s13029-016-0051-0
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