January 12-16, 2008
Town & Country Convention Center
San Diego, CA
First gene finding algorithms appeared more than 25 years ago and yet we do not have reliable algorithms to predict genes in novel plant and animal genomes. This talk will describe an integrative approach to novel genomes gene prediction that uses ab initio derived statistical models combined with models determined from limited EST data. Also we use processed EST information as guidance for the algorithm utilizing Hidden Markov models. The program training is flexible to use variable size naked genomic sequence, though at least 10 MB, and variable amount of EST data. The talk will highlight our experience of participation in several sequencing and annotation projects.