Computational analysis of cancer genome sequencing data

Citation:

Cortés-Ciriano I, Gulhan DC, Lee JJ-K, Melloni GEM, Park PJ*. Computational analysis of cancer genome sequencing data. Nat Rev Genet 2022;23(5):298-314. Copy at http://www.tinyurl.com/y29mc89x

Date Published:

2022 May

Abstract:

Distilling biologically meaningful information from cancer genome sequencing data requires comprehensive identification of somatic alterations using rigorous computational methods. As the amount and complexity of sequencing data have increased, so has the number of tools for analysing them. Here, we describe the main steps involved in the bioinformatic analysis of cancer genomes, review key algorithmic developments and highlight popular tools and emerging technologies. These tools include those that identify point mutations, copy number alterations, structural variations and mutational signatures in cancer genomes. We also discuss issues in experimental design, the strengths and limitations of sequencing modalities and methodological challenges for the future.

Last updated on 05/03/2022