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ncdDetect2: Improved models of the site-specific mutation rate in cancer and driver detection with robust significance evaluation
Name
bty511.pdf
Description
Published version
Size
1.41 MB
Format
Adobe PDF
Checksum (MD5)
12e810c18747c7afad8852f9d2f657b7
Author(s)
Juul, Malene
Madsen, Tobias
Guo, Qianyun
Bertl, Johanna
Hobolth, Asger
Kellis, Manolis
Pedersen, Jakob Skou
Date Issued
2019
Journal
Bioinformatics
Publisher
Oxford University Press (OUP)
Version
Final published version
Abstract
© 2018 The Author(s). Motivation Understanding the mutational processes that act during cancer development is a key topic of cancer biology. Nevertheless, much remains to be learned, as a complex interplay of processes with dependencies on a range of genomic features creates highly heterogeneous cancer genomes. Accurate driver detection relies on unbiased models of the mutation rate that also capture rate variation from uncharacterized sources. Results Here, we analyse patterns of observed-to-expected mutation counts across 505 whole cancer genomes, and find that genomic features missing from our mutation-rate model likely operate on a megabase length scale. We extend our site-specific model of the mutation rate to include the additional variance from these sources, which leads to robust significance evaluation of candidate cancer drivers. We thus present ncdDetect v.2, with greatly improved cancer driver detection specificity. Finally, we show that ranking candidates by their posterior mean value of their effect sizes offers an equivalent and more computationally efficient alternative to ranking by their P-values. Availability and implementation ncdDetect v.2 is implemented as an R-package and is freely available at http://github.com/TobiasMadsen/ncdDetect2 Supplementary informationSupplementary dataare available at Bioinformatics online.
Terms of Use
Creative Commons Attribution NonCommercial License 4.0
Persistent DSpace Link
DOI of Published Version
10.1093/BIOINFORMATICS/BTY511
