title: Combining calls from multiple somatic mutation-callers.
abstract: BACKGROUND
  Accurate somatic mutation-calling is essential for insightful mutation analyses in cancer studies. Several mutation-callers are publicly available and more are likely to appear. Nonetheless, mutation-calling is still challenging and there is unlikely to be one established caller that systematically outperforms all others. Therefore, fully utilizing multiple callers can be a powerful way to construct a list of final calls for one's research.
  
  
  RESULTS
  Using a set of mutations from multiple callers that are impartially validated, we present a statistical approach for building a combined caller, which can be applied to combine calls in a wider dataset generated using a similar protocol. Using the mutation outputs and the validation data from The Cancer Genome Atlas endometrial study (6,746 sites), we demonstrate how to build a statistical model that predicts the probability of each call being a somatic mutation, based on the detection status of multiple callers and a few associated features.
  
  
  CONCLUSIONS
  The approach allows us to build a combined caller across the full range of stringency levels, which outperforms all of the individual callers.
authors.affiliation: Department of Statistics, University of California at Berkeley, Berkeley CA 94720, USA. suyeonkim08@gmail.com.
authors.forename: Su Yeon
authors.initials: SY
authors.lastname: Kim
authors.forename: Laurent
authors.initials: L
authors.lastname: Jacob
authors.forename: Terence P
authors.initials: TP
authors.lastname: Speed
mesh_terms: D004252:DNA Mutational Analysis / Q000379:methods*; D016889:Endometrial Neoplasms / Q000235:genetics; D005260:Female; D016678:Genome; D023281:Genomics; D006801:Humans; D016015:Logistic Models; D015233:Models, Statistical; D009154:Mutation