CoRe: a robustly benchmarked R package for identifying core-fitness genes in genome-wide pooled CRISPR-Cas9 screens

• Published in: BMC genomics Vol. 22; no. 1; p. 828
• Main Authors: Vinceti, Alessandro, Karakoc, Emre, Pacini, Clare, Perron, Umberto, De Lucia, Riccardo Roberto, Garnett, Mathew J, Iorio, Francesco
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 17-11-2021; BioMed Central; BMC
• Subjects: algorithms; Applications software; benchmark; Benchmarking; Cancer; cancer dependency; Cell survival; Computer applications; core-fitness genes; CRISPR; CRISPR-Cas Systems; CRISPR-Cas9 screens; Cytotoxicity; Data analysis; Data mining; Fitness; Genes; Genes, Essential; Genetic aspects; Genetic disorders; Genetic research; Genomes; Genomics; Humans; Methods; Neoplasms - genetics; R (Programming language); Reproductive fitness; Software; Therapeutic applications; Italy; algorithms; CRISPR-Cas9 screens; core-fitness genes; benchmark; cancer dependency
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/s12864-021-08129-5

CRISPR-Cas9 genome-wide screens are being increasingly performed, allowing systematic explorations of cancer dependencies at unprecedented accuracy and scale. One of the major computational challenges when analysing data derived from such screens is to identify genes that are essential for cell survival invariantly across tissues, conditions, and genomic-contexts (core-fitness genes), and to distinguish them from context-specific essential genes. This is of paramount importance to assess the safety profile of candidate therapeutic targets and for elucidating mechanisms involved in tissue-specific genetic diseases.
We have developed CoRe: an R package implementing existing and novel methods for the identification of core-fitness genes (at two different level of stringency) from joint analyses of multiple CRISPR-Cas9 screens. We demonstrate, through a fully reproducible benchmarking pipeline, that CoRe outperforms state-of-the-art tools, yielding more reliable and biologically relevant sets of core-fitness genes.
CoRe offers a flexible pipeline, compatible with many pre-processing methods for the analysis of CRISPR data, which can be tailored onto different use-cases. The CoRe package can be used for the identification of high-confidence novel core-fitness genes, as well as a means to filter out potentially cytotoxic hits while analysing cancer dependency datasets for identifying and prioritising novel selective therapeutic targets.