Author

John S. Hawkins

University of California, San Francisco

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Genomics· 2 Systems Biology· 1

Signature Assays, Organisms & Methods

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Assay:CRISPR screen· 3 Approach:experimental assay· 3 Research Focus:gene regulatory network inference· 2 Research Focus:perturbation response prediction· 2 Organism:bacteria (other)· 1 Approach:deep learning· 1 Organism:E. coli· 1 Research Focus:genome assembly· 1

Frequent Co-Authors

Carol A. GrossiD· 3 shared Byoung‐Mo KooiD· 2 shared Cameron C. Hearne· 2 shared Horia TodoriD· 2 shared Jason M. PetersiD· 2 shared Jonathan S. WeissmaniD· 2 shared Marco JostiD· 2 shared Amy B. BantaiD· 1 shared

Activity by Year

2020-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 33 mutual co-author links

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3 shared papersGenomics

Carol A. GrossORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Comprehensive double-mutant analysis of the Bacillus subtilis envelope using double-CRISPRi

Byoung‐Mo Koo, Horia Todor, Jiawei Sun, Jordi van Gestel, John S. Hawkins, Cameron C. Hearne, Amy B. Banta, Kerwyn Casey Huang, Jason M. Peters, Carol A. Gross

The paper uses double-CRISPRi to systematically analyze genetic interactions in Bacillus subtilis, identifying over 1000 interactions and revealing roles of paralogous genes in cell division.

bioRxiv · Aug 14, 2024 · PreprintGenomics

Modulated efficacy CRISPRi reveals evolutionary conservation of essential gene expression-fitness relationships in bacteria

John S. Hawkins, Melanie R. Silvis, Byoung‐Mo Koo, Jason M. Peters, Hendrik Osadnik, Marco Jost, Cameron C. Hearne, Jonathan S. Weissman, Horia Todor, Carol A. Gross

The paper develops a CRISPRi system to modulate gene expression in bacteria and shows that essential gene expression-fitness relationships are conserved across species.

Cell Systems · Oct 19, 2020Peer-reviewedSystems Biology

Titrating gene expression with series of systematically compromised CRISPR guide RNAs

Marco Jost, Daniel A. Santos, Reuben A. Saunders, Max A. Horlbeck, John S. Hawkins, Sonia M. Scaria, Thomas M. Norman, Jeffrey A. Hussmann, Christina R. Liem, Carol A. Gross, Jonathan S. Weissman

This paper develops a method to control gene expression using CRISPR interference and deep learning, revealing gene-specific expression-phenotype relationships.

Nature Biotechnology · Jan 13, 2020Peer-reviewedGenomics