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MiST: A new approach to variant detection in deep sequencing datasets

MiST is a novel approach to variant calling from deep sequencing data, using the inverted mapping approach developed for Geoseq. Reads that can map to a targeted exonic region are identified using exact matches to tiles from the region. The reads are then aligned to the targets to discover variants....

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Autores principales: Subramanian, Sailakshmi, Di Pierro, Valentina, Shah, Hardik, Jayaprakash, Anitha D., Weisberger, Ian, Shim, Jaehee, George, Ajish, Gelb, Bruce D., Sachidanandam, Ravi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3763541/
https://www.ncbi.nlm.nih.gov/pubmed/23828039
http://dx.doi.org/10.1093/nar/gkt551
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author Subramanian, Sailakshmi
Di Pierro, Valentina
Shah, Hardik
Jayaprakash, Anitha D.
Weisberger, Ian
Shim, Jaehee
George, Ajish
Gelb, Bruce D.
Sachidanandam, Ravi
author_facet Subramanian, Sailakshmi
Di Pierro, Valentina
Shah, Hardik
Jayaprakash, Anitha D.
Weisberger, Ian
Shim, Jaehee
George, Ajish
Gelb, Bruce D.
Sachidanandam, Ravi
author_sort Subramanian, Sailakshmi
collection PubMed
description MiST is a novel approach to variant calling from deep sequencing data, using the inverted mapping approach developed for Geoseq. Reads that can map to a targeted exonic region are identified using exact matches to tiles from the region. The reads are then aligned to the targets to discover variants. MiST carefully handles paralogous reads that map ambiguously to the genome and clonal reads arising from PCR bias, which are the two major sources of errors in variant calling. The reduced computational complexity of mapping selected reads to targeted regions of the genome improves speed, specificity and sensitivity of variant detection. Compared with variant calls from the GATK platform, MiST showed better concordance with SNPs from dbSNP and genotypes determined by an exonic-SNP array. Variant calls made only by MiST confirm at a high rate (>90%) by Sanger sequencing. Thus, MiST is a valuable alternative tool to analyse variants in deep sequencing data.
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spelling pubmed-37635412013-09-10 MiST: A new approach to variant detection in deep sequencing datasets Subramanian, Sailakshmi Di Pierro, Valentina Shah, Hardik Jayaprakash, Anitha D. Weisberger, Ian Shim, Jaehee George, Ajish Gelb, Bruce D. Sachidanandam, Ravi Nucleic Acids Res Methods Online MiST is a novel approach to variant calling from deep sequencing data, using the inverted mapping approach developed for Geoseq. Reads that can map to a targeted exonic region are identified using exact matches to tiles from the region. The reads are then aligned to the targets to discover variants. MiST carefully handles paralogous reads that map ambiguously to the genome and clonal reads arising from PCR bias, which are the two major sources of errors in variant calling. The reduced computational complexity of mapping selected reads to targeted regions of the genome improves speed, specificity and sensitivity of variant detection. Compared with variant calls from the GATK platform, MiST showed better concordance with SNPs from dbSNP and genotypes determined by an exonic-SNP array. Variant calls made only by MiST confirm at a high rate (>90%) by Sanger sequencing. Thus, MiST is a valuable alternative tool to analyse variants in deep sequencing data. Oxford University Press 2013-09 2013-07-04 /pmc/articles/PMC3763541/ /pubmed/23828039 http://dx.doi.org/10.1093/nar/gkt551 Text en © The Author(s) 2013. Published by Oxford University Press. http://creativecommons.org/licenses/by/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methods Online
Subramanian, Sailakshmi
Di Pierro, Valentina
Shah, Hardik
Jayaprakash, Anitha D.
Weisberger, Ian
Shim, Jaehee
George, Ajish
Gelb, Bruce D.
Sachidanandam, Ravi
MiST: A new approach to variant detection in deep sequencing datasets
title MiST: A new approach to variant detection in deep sequencing datasets
title_full MiST: A new approach to variant detection in deep sequencing datasets
title_fullStr MiST: A new approach to variant detection in deep sequencing datasets
title_full_unstemmed MiST: A new approach to variant detection in deep sequencing datasets
title_short MiST: A new approach to variant detection in deep sequencing datasets
title_sort mist: a new approach to variant detection in deep sequencing datasets
topic Methods Online
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3763541/
https://www.ncbi.nlm.nih.gov/pubmed/23828039
http://dx.doi.org/10.1093/nar/gkt551
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