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Computational tools to detect signatures of mutational processes in DNA from tumours: a review and empirical comparison of performance

View ORCID ProfileOmichessan Hanane, View ORCID ProfileSeveri Gianluca, View ORCID ProfilePerduca Vittorio
doi: https://doi.org/10.1101/483982
Omichessan Hanane
1CESP (UMR INSERM 1018), Université Paris-Saclay, UPSud, UVSQ, Villejuif Cedex, F-94805, France
2Gustave Roussy, Villejuif, F-94805, France
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Severi Gianluca
1CESP (UMR INSERM 1018), Université Paris-Saclay, UPSud, UVSQ, Villejuif Cedex, F-94805, France
2Gustave Roussy, Villejuif, F-94805, France
3Cancer Epidemiology Centre, Cancer Council Victoria, and Centre for Epidemiology and Biostatistics, Melbourne School for Population and Global Health, The University of Melbourne, Australia
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Perduca Vittorio
4Laboratoire de Mathématiques Appliquées – MAP5 (UMR CNRS 8145), Université Paris Descartes, 75006, Paris, France
1CESP (UMR INSERM 1018), Université Paris-Saclay, UPSud, UVSQ, Villejuif Cedex, F-94805, France
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  • For correspondence: vittorio.perduca@parisdescartes.fr
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Abstract

Mutational signatures refer to patterns in the occurrence of somatic mutations that reflect underlying mutational processes. To date, after the analysis of tens of thousands of exomes and genomes from about 40 different cancers types, tens of mutational signatures characterized by a unique probability profile across the 96 trinucleotide-based mutation types have been identified, validated and catalogued. At the same time, several concurrent methods have been developed for either the quantification of the contribution of catalogued signatures in a given cancer sequence or the identification of new signatures from a sample of cancer sequences. A review of existing computational tools has been recently published to guide researchers and practitioners through their mutational signature analyses, but other tools have been introduced since its publication and, a systematic evaluation and comparison of the performance of such tools is still lacking. In order to fill this gap, we have carried out an empirical evaluation of the main packages available to date, using both real and simulated data.

Footnotes

  • This revised version reviews additional computational tools for mutational signatures that have been recently published.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted May 17, 2019.
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Computational tools to detect signatures of mutational processes in DNA from tumours: a review and empirical comparison of performance
Omichessan Hanane, Severi Gianluca, Perduca Vittorio
bioRxiv 483982; doi: https://doi.org/10.1101/483982
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Computational tools to detect signatures of mutational processes in DNA from tumours: a review and empirical comparison of performance
Omichessan Hanane, Severi Gianluca, Perduca Vittorio
bioRxiv 483982; doi: https://doi.org/10.1101/483982

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