Orobas: A computational approach for scoring and analysis of quantitative chemical-genetic interactions from CRISPR-Cas9 screens
<h2>Summary</h2> 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<sub>2</sub>-fold-change scores across multiple screens, incorporating normalization to reduce technical artifacts and correct batch effects.
Authors
- Urvi Bhojoo
- Maximilian Billmann (ORCID: https://orcid.org/0000-0002-6556-9594)
- Salem Bajjali
- Michael Costanzo
- Arshia Z. Hassan
- Charles Boone
- Henry N. Ward
- Brenda Andrews
- Kevin Lin
- xiang zhang (ORCID: https://orcid.org/0000-0003-2957-0050)
- Chad L. Myers
- Katherine Chan
- Kevin R. Brown
- Jason Moffat
Institutions
- Canadian Institute for Advanced Research (CA)
- University of Minnesota (US)
- University of Bonn (DE)
- University of Toronto (CA)
- University Hospital Bonn (DE)
- Hospital for Sick Children (CA)
- RIKEN Center for Sustainable Resource Science (JP)
Publication Details
- Journal
- STAR Protocols
- Published
- 2026-05-29
- DOI
- https://doi.org/10.1016/j.xpro.2026.104594
- Primary Topic
- Bioinformatics and Genomic Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.00