Abstract
Identifying variants using high-throughput sequencing data is currently a challenge because true biological variants can be indistinguishable from technical artifacts. One source of technical artifact results from incorrectly aligning experimentally observed sequences to their true genomic origin (‘mismapping’) and inferring differences in mismapped sequences to be true variants. We developed BlackOPs, an open-source tool that simulates experimental RNA-seq and DNA whole exome sequences derived from the reference genome, aligns these sequences by custom parameters, detects variants and outputs a blacklist of positions and alleles caused by mismapping. Blacklists contain thousands of artifact variants that are indistinguishable from true variants and, for a given sample, are expected to be almost completely false positives. We show that these blacklist positions are specific to the alignment algorithm and read length used, and BlackOPs allows users to generate a blacklist specific to their experimental setup. We queried the dbSNP and COSMIC variant databases and found numerous variants indistinguishable from mapping errors. We demonstrate how filtering against blacklist positions reduces the number of potential false variants using an RNA-seq glioblastoma cell line data set. In summary, accounting for mapping-caused variants tuned to experimental setups reduces false positives and, therefore, improves genome characterization by high-throughput sequencing.
Document Type
Article
Publication Date
2013
Digital Object Identifier (DOI)
http://dx.doi.org/10.1093/nar/gkt692
Repository Citation
Cabanski, Christopher R.; Wilkerson, Matthew D.; Soloway, Matthew; Parker, Joel S.; Liu, Jinze; Prins, Jan F.; Marron, J. S.; Perou, Charles M.; and Hayes, D. Neil, "BlackOPs: Increasing Confidence in Variant Detection Through Mappability Filtering" (2013). Computer Science Faculty Publications. 2.
https://uknowledge.uky.edu/cs_facpub/2
Supplementary Data
Notes/Citation Information
Published in Nucleic Acids Research, v. 41, no. 19, e178, p. 1-10.
© The Author(s) 2013. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.