MED: a new non-supervised gene prediction algorithm for bacterial and archaeal genomes

Background: Despite a remarkable success in the computational prediction of genes in Bacteria and Archaea, a lack of comprehensive understanding of prokaryotic gene structures prevents from further elucidation of differences among genomes. It continues to be interesting to develop new ab initio algorithms which not only accurately predict genes, but also facilitate comparative studies of prokaryotic genomes. Results: This paper describes a new prokaryotic genefinding algorithm based on a comprehensive statistical model of protein coding Open Reading Frames (ORFs) and Translation Initiation Sites (TISs). The former is based on a linguistic "Entropy Density Profile" (EDP) model of coding DNA sequence and the latter comprises several relevant features related to the translation initiation. They are combined to form a so-called Multivariate Entropy Distance (MED) algorithm, MED 2.0, that incorporates several strategies in the iterative program. The iterations enable us to develo...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Huaiqiu Zhu, Gang-Qing Hu, Yi-Fan Yang, Jin Wang, Zhen-Su She
• Keywords: BMCBI 2007, Genomes, Prokaryotic, Prokaryotic Genomes