Data and code for "Towards an Informative Mutant Phenotype for Every Bacterial Gene"

This page describes large-scale mutant fitness data sets for Zymomonas mobilis ZM4 and Shewanella oneidensis MR-1. Some of the fitness data was previously published, see PLoS Genetics 2011, MSB 2013a, MSB 2013b, or ACS Chem Bio 2013.

By Adam M. Deutschbauer, Morgan N. Price, Kelly M. Wetmore, Dan R. Tarjan, Zhuchen Xu, Wenjun Shao, Dacia Leon, Adam P. Arkin, and Jeffrey M. Skerker.


Mutant phenotypes provide strong clues to the functions of the underlying genes and could allow annotation of the millions of sequenced yet uncharacterized bacterial genes. However, it is not known how many genes have a phenotype under laboratory conditions, how many phenotypes are biologically interpretable for predicting gene function, and what experimental conditions are optimal to maximize the number of genes with a phenotype. To address these issues, we measured the mutant fitness of 1,586 genes of the ethanol-producing bacterium Zymomonas mobilis ZM4 across 492 diverse experiments and found statistically significant phenotypes for 89% of all assayed genes. Thus, in Z. mobilis, most genes have a functional consequence under laboratory conditions. We demonstrate that 41% of Z. mobilis genes have both a strong phenotype and a similar fitness pattern (cofitness) to another gene, and are therefore good candidates for functional annotation using mutant fitness. Among 502 poorly characterized Z. mobilis genes, we identified a significant cofitness relationship for 174. For 57 of these genes without a specific functional annotation, we found additional evidence to support the biological significance of these gene-gene associations, and in 33 instances, we were able to predict specific physiological or biochemical roles for the poorly characterized genes. Last, we identified a set of 79 diverse mutant fitness experiments in Z. mobilis that are nearly as biologically informative as the entire set of 492 experiments. Therefore, our work provides a blueprint for the functional annotation of diverse bacteria using mutant fitness.

The paper is freely available at the Journal of Bacteriology (2014)

Mutant fitness data in MicrobesOnline

Tables for ZM4

These are all tab-delimited tables. The fitness tables are also suitable for viewing in MeV.

Tables for MR-1

R code and R images

Page by Morgan Price, October 2013