OncodriveCLUSTL: a sequence-based clustering method to identify cancer drivers
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- dc.contributor.author Arnedo Pac, Claudia
- dc.contributor.author Mularoni, Loris
- dc.contributor.author Muiños, Ferran
- dc.contributor.author Gonzalez-Perez, Abel
- dc.contributor.author López Bigas, Núria
- dc.date.accessioned 2021-01-29T11:24:14Z
- dc.date.available 2021-01-29T11:24:14Z
- dc.date.issued 2019
- dc.description.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.
- dc.description.sponsorship This work was supported by funding from the Spanish Ministry of Economy and Competitiveness [SAF2015-66084-R, MINECO/FEDER, UE] and by the European Research Council [Consolidator Grant 68239]. IRB Barcelona is the recipient of a Severo Ochoa Centre of Excellence Award from the Spanish Ministry of Economy and Competitiveness (MINECO; Government of Spain) and is supported by CERCA (Generalitat de Catalunya). A.G.-P. is supported by a Ramón y Cajal contract from the Spanish Ministry of Economy and Competitiveness [RYC-2013-1455]. C.A.-P. is supported by “la Caixa” Foundation (ID 100010434) with code [LCF/BQ/ES18/11670011].
- dc.format.mimetype application/pdf
- dc.identifier.citation Arnedo-Pac C, Mularoni L, Muiños F, Gonzalez-Perez A, Lopez-Bigas N. OncodriveCLUSTL: a sequence-based clustering method to identify cancer drivers. Bioinformatics. 2019; 35(22):4788-90. DOI: 10.1093/bioinformatics/btz501
- dc.identifier.doi http://dx.doi.org/10.1093/bioinformatics/btz501
- dc.identifier.issn 1367-4803
- dc.identifier.uri http://hdl.handle.net/10230/46297
- dc.language.iso eng
- dc.publisher Oxford University Press
- dc.relation.ispartof Bioinformatics. 2019; 35(22):4788-90
- dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/682398
- dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/SAF2015-66084-R
- dc.rights © The Author(s) 2019. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/
- dc.title OncodriveCLUSTL: a sequence-based clustering method to identify cancer drivers
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion