OncodriveCLUSTL: a sequence-based clustering method to identify cancer drivers

Author:

Arnedo-Pac Claudia1,Mularoni Loris1,Muiños Ferran1,Gonzalez-Perez Abel12,Lopez-Bigas Nuria123

Affiliation:

1. Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain

2. Research Program on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Spain

3. Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona 08010, Spain

Abstract

Abstract Motivation Identification of the genomic alterations driving tumorigenesis is one of the main goals in oncogenomics research. Given the evolutionary principles of cancer development, computational methods that detect signals of positive selection in the pattern of tumor mutations have been effectively applied in the search for cancer genes. One of these signals is the abnormal clustering of mutations, which has been shown to be complementary to other signals in the detection of driver genes. Results We have developed OncodriveCLUSTL, a new sequence-based clustering algorithm to detect significant clustering signals across genomic regions. OncodriveCLUSTL is based on a local background model derived from the simulation of mutations accounting for the composition of tri- or penta-nucleotide context substitutions observed in the cohort under study. Our method can identify known clusters and bona-fide cancer drivers across cohorts of tumor whole-exomes, outperforming the existing OncodriveCLUST algorithm and complementing other methods based on different signals of positive selection. Our results indicate that OncodriveCLUSTL can be applied to the analysis of non-coding genomic elements and non-human mutations data. Availability and implementation OncodriveCLUSTL is available as an installable Python 3.5 package. The source code and running examples are freely available at https://bitbucket.org/bbglab/oncodriveclustl under GNU Affero General Public License. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

Spanish Ministry of Economy and Competitiveness

MINECO

FEDER

European Research Council

Severo Ochoa Centre of Excellence Award

Ministry of Economy and Competitiveness

CERCA

“la Caixa” Foundation

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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