Functional knowledge transfer for high-accuracy prediction of under-studied biological processes.

ABSTRACT: A key challenge in genetics is identifying the functional roles of genes in pathways. Numerous functional genomics techniques (e.g. machine learning) that predict protein function have been developed to address this question. These methods generally build from existing annotations of genes to pathways and thus are often unable to identify additional genes participating in processes that are not already well studied. Many of these processes are well studied in some organism, but not necessarily in an investigator's organism of interest. Sequence-based search methods (e.g. BLAST) have been used to transfer such annotation information between organisms. We demonstrate that functional genomics can complement traditional sequence similarity to improve the transfer of gene annotations between or

SUBMITTER: Park CY 

PROVIDER: S-EPMC3597527 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature