BlackOPs: Increasing confidence in variant detection through mappability filtering

Christopher R. Cabanski, Matthew D. Wilkerson, Matthew Soloway, Joel S. Parker, Jinze Liu, Jan F. Prins, J. S. Marron, Charles M. Perou, D. Neil Hayes*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


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.

Original languageEnglish
Pages (from-to)e178
JournalNucleic Acids Research
Issue number19
StatePublished - Oct 2013
Externally publishedYes


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