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Author Correction: A benchmark of computational methods for correcting biases of established and unknown origin in CRISPR-Cas9 screening data
Genome Biology volume 25, Article number: 239 (2024)
The Original Article was published on 19 July 2024
Correction: Genome Biol 25, 192 (2024)
https://doi.org/10.1186/s13059-024-03336-1
Following publication of the original article [1], the authors identified an omission in the completing interests section. The omitted text is given in bold below.
Competing interests
FI receives funding from Open Targets, a public-private initiative involving academia and industry and performs consultancy for the joint CRUK-AstraZeneca Functional Genomics Centre and for Mosaic TX. JD is a consultant for and holds equity in Jumble Therapeutics. CDC performs consultancy for Droplet Biosciences and is a shareholder of Novartis. FV receives research support from the Dependency Map Consortium, Riva Therapeutics, Bristol Myers Squibb, Merck, Illumina, and Deerfield Management. FV is on the scientific advisory board of GSK, is a consultant and holds equity in Riva Therapeutics and is a co-founder and holds equity in Jumble Therapeutics. All other authors declare that they have no competing interests.
The original article [1] is corrected.
Reference
Vinceti A, Iannuzzi RM, Boyle I, et al. A benchmark of computational methods for correcting biases of established and unknown origin in CRISPR-Cas9 screening data. Genome Biol. 2024;25:192. https://doi.org/10.1186/s13059-024-03336-1.
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Vinceti, A., Iannuzzi, R.M., Boyle, I. et al. Author Correction: A benchmark of computational methods for correcting biases of established and unknown origin in CRISPR-Cas9 screening data. Genome Biol 25, 239 (2024). https://doi.org/10.1186/s13059-024-03387-4
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DOI: https://doi.org/10.1186/s13059-024-03387-4