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. CONCLUSION:The approach allows us to build a combined caller across the full range of stringency levels, which outperforms all of the individual callers.

journal_name

BMC Bioinformatics

journal_title

BMC bioinformatics

authors

Kim SY,Jacob L,Speed TP

doi

10.1186/1471-2105-15-154

subject

Has Abstract

pub_date

2014-05-21 00:00:00

pages

154

issn

1471-2105

pii

1471-2105-15-154

journal_volume

15

pub_type

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