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GeneGenerator--a flexible algorithm for gene prediction and its application to maize sequences

J Kleffe1, K Hermann, W Vahrson

  • 1Freie Universität Berlin, Abteilung Molekularbiologie und Bioinformatik, Institut für Molekularbiologie und Biochemie, Arnimallee 22, 14195 Berlin, Germany. jkleffe@euler.grumed.fu-berlin-de

Bioinformatics (Oxford, England)
|June 6, 1998
PubMed
Summary
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GeneGenerator predicts gene structures using flexible algorithms and Markov models, aiding gene finding in less-studied organisms. It identifies correct gene structures within high-scoring sets, improving accuracy for genomic sequences.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Developing computational tools for gene structure prediction is crucial, especially for organisms with limited training data.
  • Existing methods often require pre-defined scoring parameters, limiting their applicability.
  • GeneGenerator was created to address these limitations by offering a flexible approach to gene prediction.

Purpose of the Study:

  • To introduce GeneGenerator, a novel algorithm for predicting gene structures in genomic sequences.
  • To demonstrate the algorithm's flexibility and effectiveness, particularly for organisms with small training sets.
  • To evaluate the accuracy of gene structure predictions using maize genomic data.

Main Methods:

  • GeneGenerator employs a flexible algorithm to generate multiple feasible gene structures based on user-defined constraints.

Related Experiment Videos

  • It utilizes logit-linear models for scoring translation start and splice sites.
  • Markov sequence models generate log-likelihood ratios for coding/non-coding potential and exon/intron lengths.

    • Main Results:

    • The study used a database of 46 maize genomic sequences for illustration.
    • Gene structures were predicted, and it was observed that correct structures were often within a small set of high-scoring predictions.
    • The algorithm achieved an exon sensitivity of 0.81 and specificity of 0.75 on an independent set of 14 novel maize genomic segments.

    Conclusions:

    • GeneGenerator successfully predicts gene structures, even when correct structures do not maximize the target function.
    • The algorithm provides a valuable tool for gene prediction in diverse organisms, including those with limited genomic data.
    • Further refinement is possible by considering additional variables like splice site strength for improved accuracy.