Downloads for "Deep Annotation of Protein Function across Diverse Bacteria from Mutant Phenotypes"

by Morgan N. Price, Kelly M. Wetmore, R. Jordan Waters, Mark Callaghan, Jayashree Ray, Hualan Liu, Jennifer V. Kuehl, Ryan A. Melnyk, Jacob S. Lamson, Yumi Suh, Hans K. Carlson, Zuelma Esquivel, Harini Sadeeshkumar, Romy Chakraborty, Grant M. Zane, Benjamin E. Rubin, Judy D. Wall, Axel Visel, James Bristow, Matthew J. Blow, Adam P. Arkin, and Adam M. Deutschbauer


The function of nearly half of all protein-coding genes identified in bacterial genomes remains unknown. To systematically explore the functions of these proteins, we generated saturated transposon mutant libraries from 32 diverse bacteria and we assayed mutant phenotypes across hundreds of distinct conditions. From 4,870 genome-wide mutant fitness assays, we obtained 18.7 million gene phenotype measurements and we identified a mutant phenotype for 11,779 proteins with previously unknown functions. The majority of these hypothetical proteins (62%) had phenotypes that were either specific to a few conditions or were similar to that of another gene in the same bacterium, thus enabling us to make informed predictions of protein function. For 2,316 of these hypothetical proteins, the functional associations are conserved across related proteins from different bacteria, which confirms that these associations are genuine. Based on the functional associations, we identified 13 novel families of DNA repair proteins, we proposed functions for 19 other uncharacterized protein families, and we identified 444 transporters or catabolic enzymes that had been annotated incorrectly. Across all sequenced bacteria, 12% of proteins that lack detailed annotations have an ortholog with a functional association in our data set. Our study demonstrates the utility and scalability of high-throughput genetics for annotating the functions of bacterial proteins.

See bioRxiv preprint (with just 25 bacteria)

Data Downloads

The easiest way to view the data is with the Fitness Browser. You can also download the data for each organism here: or as a tarball for all genomes here (large! 84 GB)

You can get information about the organisms and their genomes here:

Alternatively, you can download all of the data in the Fitness Browser from doi: 10.6084/m9.figshare.5134840

Also note that for some organisms, the Fitness Browser contains additional experiments beyond those described here. For these organisms, the cofitness values will not match.

Other Downloads

Page by Morgan N. Price, Arkin group, June 2017