mmsig: a fitting approach to accurately identify somatic mutational signatures in hematological malignancies.
作者： Ola Landgren , Ola Landgren , Xose S. Puente , Elias Campo , Ferran Nadeu
DOI: 10.1038/S42003-021-01938-0
关键词: Genome 、 Fitting algorithm 、 Software package 、 APOBEC 、 R package 、 Tumor Sample 、 Computational biology 、 Somatic cell 、 Computer science
摘要: Mutational signatures have emerged as powerful biomarkers in cancer patients, with prognostic and therapeutic implications. Wider clinical utility requires access to reproducible algorithms, which allow characterization of mutational a given tumor sample. Here, we show how signature fitting can be applied hematological genomes identify biologically clinically important processes, highlighting the importance careful interpretation light biological knowledge. Our newly released R package mmsig comes dynamic error-suppression procedure that improves specificity settings. In particular, allows accurate detection mutational signatures low abundance, such those introduced by APOBEC cytidine deaminases. This is particularly most recent reference (COSMIC v3.1) where each more clearly defined. algorithm robust tool implemented immediately clinic. Rustad et al. present software for statistical environment accurately quantify somatic malignancies
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