Reranking candidate gene models with cross-species comparison for improved gene prediction

Background: Most gene finders score candidate gene models with state-based methods, typically HMMs, by combining local properties (coding potential, splice donor and acceptor patterns, etc). Competing models with similar state-based scores may be distinguishable with additional information. In particular, functional and comparative genomics datasets may help to select among competing models of comparable probability by exploiting features likely to be associated with the correct gene models, such as conserved exon/intron structure or protein sequence features. Results: We have investigated the utility of a simple post-processing step for selecting among a set of alternative gene models, using global scoring rules to rerank competing models for more accurate prediction. For each gene locus, we first generate the K best candidate gene models using the gene finder Evigan, and then rerank these models using comparisons with putative orthologous genes from closely-related species. Candidat...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Qian Liu, Koby Crammer, Fernando C. N. Pereira, David S. Roos
• Keywords: BMCBI 2008, Candidate Gene Models, Gene, Gene Models