Combining calls from multiple somatic mutation-callers
- Details
- Publication Year 2014,Volume 15,Issue #1,Page 154
- Journal Title
- BMC Bioinformatics
- Publication Type
- Journal Article
- 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.
- Publisher
- BioMed Central
- Research Division(s)
- Bioinformatics
- Link To PubMed Central Version
- http://www.ncbi.nlm.nih.gov.ezp.lib.unimelb.edu.au/pmc/articles/PMC4035752/
- Publisher's Version
- https://doi.org/10.1186/1471-2105-15-154
- Open Access at Publisher's Site
http://www.biomedcentral/1471-2105/15/154- Terms of Use/Rights Notice
- © 2014 Kim et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Creation Date: 2014-07-10 09:26:33