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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....
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2013
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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. |
format | Online Article Text |
id | pubmed-3763541 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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