Work

Orobas: A computational approach for scoring and analysis of quantitative chemical-genetic interactions from CRISPR-Cas9 screens

Year: 2026

Type: article

Abstract: We present Orobas, a computational approach for transforming raw read count data from CRISPR-Cas9 chemical-genetic screens into quantitative interaction scores. We describe steps for computing differential interaction scores with statistical tests that account for multiple CRISPR guides per gene. We then outline approaches for post-processing differential log 2 -fold-change scores across multiple screens, incorporating normalization to reduce technical artifacts and correct batch effects.

Source: STAR Protocols

DOI: 10.1016/j.xpro.2026.104594

Authors: Arshia Z. Hassan, Xiang Zhang, Henry N. Ward, Maximilian Billmann, Salem Bajjali, Kevin R. Brown, Urvi Bhojoo, Katherine Chan, Kevin Lin, Michael Costanzo, Brenda Andrews, Charles Boone, Jason Moffat, Chad L. Myers

Institutions: University of Minnesota, University of Bonn, University Hospital Bonn, Hospital for Sick Children, University of Toronto, Canadian Institute for Advanced Research, RIKEN Center for Sustainable Resource Science

Language: English

Topic: CRISPR and Genetic Engineering

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $1,550

Funders: National Science Foundation, National Institutes of Health

Awards: G5547676425, Large-scale mapping of genetic interactions across diverse cell types, G9000168061

OpenAlex ID: W7162632761

FWCI:

Cites: 25

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