Identifying essential genes in bacterial metabolic networks with machine learning methods.

ABSTRACT:

Background

Identifying essential genes in bacteria supports to identify potential drug targets and an understanding of minimal requirements for a synthetic cell. However, experimentally assaying the essentiality of their coding genes is resource intensive and not feasible for all bacterial organisms, in particular if they are infective.

Results

We developed a machine learning technique to identify essential genes using the experimental data of genome-wide knock-out screens from one bacterial organism to infer essential genes of another related bacterial organism. We used a broad variety of topological features, sequence characteristics and co-expression properties potentially associated with essentiality, such as flux deviations, centrality, codon frequencies of the sequence

SUBMITTER: Plaimas K 

PROVIDER: S-EPMC2874528 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature