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Variant detection sensitivity and biases in whole genome and exome sequencing

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    Rights statement: © 2014 Meynert et al.; licensee BioMed Central Ltd. 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.

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Original languageEnglish
Pages (from-to)247
JournalBMC Bioinformatics
Issue number1
StatePublished - 19 Jul 2014


BACKGROUND: Less than two percent of the human genome is protein coding, yet that small fraction harbours themajority of known disease causing mutations. Despite rapidly falling whole genome sequencing(WGS) costs, much research and increasingly the clinical use of sequence data is likely to remainfocused on the protein coding exome. We set out to quantify and understand howWGS compares withthe targeted capture and sequencing of the exome (exome-seq), for the specific purpose of identifyingsingle nucleotide polymorphisms (SNPs) in exome targeted regions.

RESULTS: We have compared polymorphism detection sensitivity and systematic biases using a set of tissuesamples that have been subject to both deep exome and whole genome sequencing. The scoringof detection sensitivity was based on sequence down sampling and reference to a set of goldstandardSNP calls for each sample. Despite evidence of incremental improvements in exome capturetechnology over time, whole genome sequencing has greater uniformity of sequence read coverageand reduced biases in the detection of non-reference alleles than exome-seq. Exome-seq achieves95% SNP detection sensitivity at a mean on-target depth of 40 reads, whereas WGS only requires amean of 14 reads. Known disease causing mutations are not biased towards easy or hard to sequenceareas of the genome for either exome-seq or WGS.

CONCLUSIONS: From an economic perspective, WGS is at parity with exome-seq for variant detection in the targetedcoding regions. WGS offers benefits in uniformity of read coverage and more balanced alleleratio calls, both of which can in most cases be offset by deeper exome-seq, with the caveat thatsome exome-seq targets will never achieve sufficient mapped read depth for variant detection due to technical difficulties or probe failures. AsWGS is intrinsically richer data that can provide insight intopolymorphisms outside coding regions and reveal genomic rearrangements, it is likely to progressivelyreplace exome-seq for many applications.

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