TY - JOUR

T1 - ncdDetect2

T2 - Improved models of the site-specific mutation rate in cancer and driver detection with robust significance evaluation

AU - Juul, Malene

AU - Madsen, Tobias

AU - Guo, Qianyun

AU - Bertl, Johanna

AU - Hobolth, Asger

AU - Kellis, Manolis

AU - Pedersen, Jakob Skou

PY - 2019/1/15

Y1 - 2019/1/15

N2 - 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.

AB - 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.

KW - EXCISION-REPAIR

KW - EXPRESSION

KW - FRAMEWORK

KW - GENOME-WIDE ANALYSIS

KW - RECURRENT

KW - REGULATORY MUTATIONS

KW - SOMATIC MUTATIONS

KW - TERT PROMOTER MUTATIONS

KW - VARIANTS

UR - http://www.scopus.com/inward/record.url?scp=85060051748&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/bty511

DO - 10.1093/bioinformatics/bty511

M3 - Journal article

C2 - 29945188

VL - 35

SP - 189

EP - 199

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 2

ER -