PT  - JOURNAL ARTICLE
AU  - Omichessan Hanane
AU  - Severi Gianluca
AU  - Perduca Vittorio
TI  - Computational tools to detect signatures of mutational processes in DNA from tumours: a review and empirical comparison of performance
AID  - 10.1101/483982
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 483982
4099  - http://biorxiv.org/content/early/2019/05/17/483982.short
4100  - http://biorxiv.org/content/early/2019/05/17/483982.full
AB  - 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.