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The distribution and mutagenesis of short coding INDELs from 1,128 whole exomes
BACKGROUND: Identifying insertion/deletion polymorphisms (INDELs) with high confidence has been intrinsically challenging in short-read sequencing data. Here we report our approach for improving INDEL calling accuracy by using a machine learning algorithm to combine call sets generated with three in...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4352271/ https://www.ncbi.nlm.nih.gov/pubmed/25765891 http://dx.doi.org/10.1186/s12864-015-1333-7 |
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author | Challis, Danny Antunes, Lilian Garrison, Erik Banks, Eric Evani, Uday S Muzny, Donna Poplin, Ryan Gibbs, Richard A Marth, Gabor Yu, Fuli |
author_facet | Challis, Danny Antunes, Lilian Garrison, Erik Banks, Eric Evani, Uday S Muzny, Donna Poplin, Ryan Gibbs, Richard A Marth, Gabor Yu, Fuli |
author_sort | Challis, Danny |
collection | PubMed |
description | BACKGROUND: Identifying insertion/deletion polymorphisms (INDELs) with high confidence has been intrinsically challenging in short-read sequencing data. Here we report our approach for improving INDEL calling accuracy by using a machine learning algorithm to combine call sets generated with three independent methods, and by leveraging the strengths of each individual pipeline. Utilizing this approach, we generated a consensus exome INDEL call set from a large dataset generated by the 1000 Genomes Project (1000G), maximizing both the sensitivity and the specificity of the calls. RESULTS: This consensus exome INDEL call set features 7,210 INDELs, from 1,128 individuals across 13 populations included in the 1000 Genomes Phase 1 dataset, with a false discovery rate (FDR) of about 7.0%. CONCLUSIONS: In our study we further characterize the patterns and distributions of these exonic INDELs with respect to density, allele length, and site frequency spectrum, as well as the potential mutagenic mechanisms of coding INDELs in humans. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-015-1333-7) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4352271 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-43522712015-03-08 The distribution and mutagenesis of short coding INDELs from 1,128 whole exomes Challis, Danny Antunes, Lilian Garrison, Erik Banks, Eric Evani, Uday S Muzny, Donna Poplin, Ryan Gibbs, Richard A Marth, Gabor Yu, Fuli BMC Genomics Research Article BACKGROUND: Identifying insertion/deletion polymorphisms (INDELs) with high confidence has been intrinsically challenging in short-read sequencing data. Here we report our approach for improving INDEL calling accuracy by using a machine learning algorithm to combine call sets generated with three independent methods, and by leveraging the strengths of each individual pipeline. Utilizing this approach, we generated a consensus exome INDEL call set from a large dataset generated by the 1000 Genomes Project (1000G), maximizing both the sensitivity and the specificity of the calls. RESULTS: This consensus exome INDEL call set features 7,210 INDELs, from 1,128 individuals across 13 populations included in the 1000 Genomes Phase 1 dataset, with a false discovery rate (FDR) of about 7.0%. CONCLUSIONS: In our study we further characterize the patterns and distributions of these exonic INDELs with respect to density, allele length, and site frequency spectrum, as well as the potential mutagenic mechanisms of coding INDELs in humans. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-015-1333-7) contains supplementary material, which is available to authorized users. BioMed Central 2015-02-28 /pmc/articles/PMC4352271/ /pubmed/25765891 http://dx.doi.org/10.1186/s12864-015-1333-7 Text en © Challis et al.; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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 | Research Article Challis, Danny Antunes, Lilian Garrison, Erik Banks, Eric Evani, Uday S Muzny, Donna Poplin, Ryan Gibbs, Richard A Marth, Gabor Yu, Fuli The distribution and mutagenesis of short coding INDELs from 1,128 whole exomes |
title | The distribution and mutagenesis of short coding INDELs from 1,128 whole exomes |
title_full | The distribution and mutagenesis of short coding INDELs from 1,128 whole exomes |
title_fullStr | The distribution and mutagenesis of short coding INDELs from 1,128 whole exomes |
title_full_unstemmed | The distribution and mutagenesis of short coding INDELs from 1,128 whole exomes |
title_short | The distribution and mutagenesis of short coding INDELs from 1,128 whole exomes |
title_sort | distribution and mutagenesis of short coding indels from 1,128 whole exomes |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4352271/ https://www.ncbi.nlm.nih.gov/pubmed/25765891 http://dx.doi.org/10.1186/s12864-015-1333-7 |
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