title: Computational modeling of gene structure in Arabidopsis thaliana.
abstract: Computational gene identification by sequence inspection remains a challenging problem. For a typical Arabidopsis thaliana gene with five exons, at least one of the exons is expected to have at least one of its borders predicted incorrectly by ab initio gene finding programs. More detailed analysis for individual genomic loci can often resolve the uncertainty on the basis of EST evidence or similarity to potential protein homologues. Such methods are part of the routine annotation process. However, because the EST and protein databases are constantly growing, in many cases original annotation must be re-evaluated, extended, and corrected on the basis of the latest evidence. The Arabidopsis Genome Initiative is undertaking this task on the whole-genome scale via its participating genome centers. The current Arabidopsis genome annotation provides an excellent starting point for assessing the protein repertoire of a flowering plant. More accurate whole-genome annotation will require the combination of high-throughput and individual gene experimental approaches and computational methods. The purpose of this article is to discuss tools available to an individual researcher to evaluate gene structure prediction for a particular locus.
authors.affiliation: Department of Zoology & Genetics, Iowa State University, Ames 50010, USA. vbrendel@iastate.edu
authors.forename: Volker
authors.initials: V
authors.lastname: Brendel
authors.forename: Wei
authors.initials: W
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D017360:Arabidopsis / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D017343:Genes, Plant / Q000235:genetics*; D020407:Internet; D008969:Molecular Sequence Data; D016415:Sequence Alignment / Q000379:methods; D017386:Sequence Homology, Amino Acid