Inferring gene structures in genomic sequences using pattern recognition and expressed sequence tags

Computational methods for gene identification in genomic sequences typically have two phases: coding region prediction and gene parsing. While there are many effective methods for predicting coding regions (exons), parsing the predicted exons into proper gene structures, to a large extent, remains an unsolved problem. This paper presents an algorithm for inferring gene structures from predicted exon candidates, based on Expressed Sequence Tags (ESTs) and biological intuition/rules. The algorithm first finds all the related ESTs in the EST database (dbEST) for each predicted exon, and infers the boundaries of one or a series of genes based on the available EST information and biological rules. Then it constructs gene models within each pair of gene boundaries, that are most consistent with the EST information. By exploiting EST information and biological rules, the algorithm can (1) model complicated multiple gene structures, including embedded genes, (2) identify falsely-predicted exons and locate missed exons, and (3) make more accurate exon boundary predictions. The algorithm has been implemented and tested on long genomic sequences with a number of genes. Test results show that very accurate (predicted) gene models can be expected when related ESTs exist for the predicted exons

Inferring gene structures in genomic sequences using pattern recognition and expressed sequence tags

Computational methods for gene identification in genomic sequences typically have two phases: coding region prediction and gene parsing. While there are many effective methods for predicting coding regions (exons), parsing the predicted exons into proper gene structures, to a large extent, remains an unsolved problem. This paper presents an algorithm for inferring gone structures from predicted exon candidates, based on Expressed Sequence Tags (ESTs) and biological intuition/rules. The algorithm first finds all the related ESTs in the EST database (dbEST.) for each predicted exon, and infers the boundaries of one or a series of genes based on the available EST information and biological rules. Then it constructs gone models within each pair of genc boundaries, that are most consistent with the EST information. By exploiting EST information and biological rules, the algorithm can (1) model complicated multiple gone structures, including embedded genes, (2) identify falsely-predicted exons and locate missed exons, and (3) make more accurate exon boundary predictions. The algorithm has been implemented and tested on long genomic sequences with a number of genes. Test results show that very accurate (predicted) gene models can be expected when related ESTs exist for the predicted exons