Understanding protein dispensability through machine-learning analysis of high-throughput data
Year: 2004
Type: article
Abstract: MOTIVATION: Protein dispensability is fundamental to the understanding of gene function and evolution. Recent advances in generating high-throughput data such as genomic sequence data, protein-protein interaction data, gene-expression data and growth-rate data of mutants allow us to investigate protein dispensability systematically at the genome scale. RESULTS: In our studies, protein dispensability is represented as a fitness score that is measured by the growth rate of gene-deletion mutants. By the analyses of high-throughput data in yeast Saccharomyces cerevisiae, we found that a protein's dispensability had significant correlations with its evolutionary rate and duplication rate, as well as its connectivity in protein-protein interaction network and gene-expression correlation network. Neural network and support vector machine were applied to predict protein dispensability through high-throughput data. Our studies shed some lights on global characteristics of protein dispensability and evolution. AVAILABILITY: The original datasets for protein dispensability analysis and prediction, together with related scripts, are available at http://digbio.missouri.edu/~ychen/ProDispen/ CONTACT: [email protected].
Source: Bioinformatics
Institution: Oak Ridge National Laboratory
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: bronze
Funder: U.S. Department of Energy
OpenAlex ID: W2118830783