Functional enrichment analyses and construction of functional similarity networks with high confidence function prediction by PF

Background: A new paradigm of biological investigation takes advantage of technologies that produce large high throughput datasets, including genome sequences, interactions of proteins, and gene expression. The ability of biologists to analyze and interpret such data relies on functional annotation of the included proteins, but even in highly characterized organisms many proteins can lack the functional evidence necessary to infer their biological relevance. Results: Here we have applied high confidence function predictions from our automated prediction system, PFP, to three genome sequences, Escherichia coli, Saccharomyces cerevisiae, and Plasmodium falciparum (malaria). The number of annotated genes is increased by PFP to over 90% for all of the genomes. Using the large coverage of the function annotation, we introduced the functional similarity networks which represent the functional space of the proteomes. Four different functional similarity networks are constructed for each prot...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Troy Hawkins, Meghana Chitale, Daisuke Kihara
• Keywords: BMCBI 2010, Business, Functional Similarity Networks, Genome, Protein-protein Interaction Network