LCR-modules: a collection of workflows for cancer genome analysis

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Authors: Kostiantyn Dreval, Laura K. Hilton, Bruno M. Grande, Giuliano Banco, Krysta Mila Coyle, Manuela Cruz, Sierra Gillis, Luke Klossok, Prasath Pararajalingam, Christopher K. Rushton, Haya Shaalan, Nicole Thomas
+6 more Helena K. Winata, Jasper Wong, Jacky Yiu, Christian Steidl, David William Scott, Ryan D. Morin
Cited by: 1
References: 27

Abstract

MOTIVATION: The surge of genomic data from advanced sequencing technologies is outpacing current analytical pipelines. We introduce LCR-modules, an open-source suite of bioinformatics tools designed for flexible and automated cancer genome data analysis. LCR-modules enables reproducible analysis of diverse cancer genomics data at scale. The suite comprises 49 Snakemake-based workflows organized into three levels, facilitating tasks from low-level quality control to complex cohort-level analyses. LCR-modules supports various sequencing types and integrates pipelines such as mutation calling, expression quantification, and cohort-level aggregation, ensuring flexibility and reproducibility. LCR-modules represents a significant advancement in genomic data analysis, reducing barriers in reproducibility and scalability and has already been applied to a combination of exomes and genomes from over 10 800 samples. AVAILABILITY: No new data were generated in support of this research. The source code for the LCR-modules is openly available at https://github.com/LCR-BCCRC/lcr-modules.

Fields of Study & Subjects

Genetics Bioinformatics and Biomedical Research Gene expression and cancer classification Cancer Genomics and Diagnostics

Publication details

Journal / source
Bioinformatics
Publisher
Oxford University Press (OUP)
Volume
42
Issue
6
Publication date
1 June 2026
Language
EN