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

Abstract

BackgroundIdentifying 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.ResultsThis 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%.ConclusionsIn 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 materialThe online version of this article (doi:10.1186/s12864-015-1333-7) contains supplementary material, which is available to authorized users.

Mutational landscape of RNA-binding proteins in human cancers

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Explorations to improve the completeness of exome sequencing

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Teneurin transmembrane protein 4 is not a cause for essential tremor in a Canadian population

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