1,485
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Prediction of complete gene structures in human genomic DNA.

      1 ,
      Journal of molecular biology
      Elsevier BV

      Read this article at

      ScienceOpenPublisherPubMed
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          We introduce a general probabilistic model of the gene structure of human genomic sequences which incorporates descriptions of the basic transcriptional, translational and splicing signals, as well as length distributions and compositional features of exons, introns and intergenic regions. Distinct sets of model parameters are derived to account for the many substantial differences in gene density and structure observed in distinct C + G compositional regions of the human genome. In addition, new models of the donor and acceptor splice signals are described which capture potentially important dependencies between signal positions. The model is applied to the problem of gene identification in a computer program, GENSCAN, which identifies complete exon/intron structures of genes in genomic DNA. Novel features of the program include the capacity to predict multiple genes in a sequence, to deal with partial as well as complete genes, and to predict consistent sets of genes occurring on either or both DNA strands. GENSCAN is shown to have substantially higher accuracy than existing methods when tested on standardized sets of human and vertebrate genes, with 75 to 80% of exons identified exactly. The program is also capable of indicating fairly accurately the reliability of each predicted exon. Consistently high levels of accuracy are observed for sequences of differing C + G content and for distinct groups of vertebrates.

          Related collections

          Author and article information

          Journal
          J Mol Biol
          Journal of molecular biology
          Elsevier BV
          0022-2836
          0022-2836
          Apr 25 1997
          : 268
          : 1
          Affiliations
          [1 ] Department of Mathematics, Stanford University, CA 94305, USA.
          Article
          S0022-2836(97)90951-7
          10.1006/jmbi.1997.0951
          9149143
          2e26af28-4fbe-4ee5-95c6-c2ae82fd10f8
          History

          Comments

          Comment on this article