The distribution and mutagenesis of short coding INDELs from 1,128 whole exomes

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 me...

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Bibliographic Details
Published in:BMC genomics 2015-02, Vol.16 (1), p.143-143, Article 143
Main Authors: Challis, Danny, Antunes, Lilian, Garrison, Erik, Banks, Eric, Evani, Uday S, Muzny, Donna, Poplin, Ryan, Gibbs, Richard A, Marth, Gabor, Yu, Fuli
Format: Article
Language:English
Subjects:
Computational Biology
Exome - genetics
Genome, Human
Genomes
High-Throughput Nucleotide Sequencing
Human Genome Project
Humans
INDEL Mutation - genetics
Machine Learning
Mutagenesis
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Summary: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. 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%. 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.
ISSN:1471-2164
1471-2164
DOI:10.1186/s12864-015-1333-7