BlackOPs: increasing confidence in variant detection through mappability filtering.
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.
Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC 27599, USA, The Genome Institute at Washington University, St. Louis, MO 63108, USA, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Computer Science, University of Kentucky, Lexington, KY 40506, USA, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA and Division of Medical Oncology, Department of Internal Medicine, University of North Carolina, Chapel Hill, NC 27599, USA.
Christopher R
CR
Cabanski
Matthew D
MD
Wilkerson
Matthew
M
Soloway
Joel S
JS
Parker
Jinze
J
Liu
Jan F
JF
Prins
J S
JS
Marron
Charles M
CM
Perou
D Neil
DN
Hayes
D016477:Artifacts; D045744:Cell Line, Tumor; D002874:Chromosome Mapping; D030561:Databases, Nucleic Acid; D059472:Exome; D014644:Genetic Variation; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods; D017423:Sequence Analysis, RNA / Q000379:methods; D012984:Software