Title
Optimized filtering reduces the error rate in detecting genomic variants by short-read sequencingOptimized filtering reduces the error rate in detecting genomic variants by short-read sequencing
Author
Faculty/Department
Faculty of Medicine and Health Sciences
Faculty of Pharmaceutical, Biomedical and Veterinary Sciences . Biomedical Sciences
Research group
Advanced Database Research and Modeling (ADReM)
Collaborative Antwerp Psychiatric Research Institute (CAPRI)
VIB DMG - Applied Molecular Genomics
Publication type
article
Publication
New York, N.Y.,
Subject
Biology
Human medicine
Engineering sciences. Technology
Computer. Automation
Source (journal)
Nature biotechnology. - New York, N.Y., 1996, currens
Volume/pages
30(2012):1, p. 61-68
30(2012):1, p. 61-U103
ISSN
1087-0156
ISI
000299110600021
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Distinguishing single-nucleotide variants (SNVs) from errors in whole-genome sequences remains challenging. Here we describe a set of filters, together with a freely accessible software tool, that selectively reduce error rates and thereby facilitate variant detection in data from two short-read sequencing technologies, Complete Genomics and Illumina. By sequencing the nearly identical genomes from monozygotic twins and considering shared SNVs as 'true variants' and discordant SNVs as 'errors', we optimized thresholds for 12 individual filters and assessed which of the 1,048 filter combinations were effective in terms of sensitivity and specificity. Cumulative application of all effective filters reduced the error rate by 290-fold, facilitating the identification of genetic differences between monozygotic twins. We also applied an adapted, less stringent set of filters to reliably identify somatic mutations in a highly rearranged tumor and to identify variants in the NA19240 HapMap genome relative to a reference set of SNVs.
E-info
https://repository.uantwerpen.be/docman/iruaauth/e17fb7/9eac754b5bc.pdf
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