A Decision Tree System for Finding Genes in DNA

@article{Salzberg1998ADT,
  title={A Decision Tree System for Finding Genes in DNA},
  author={Steven L. Salzberg and Arthur L. Delcher and Kenneth H. Fasman and John Henderson},
  journal={Journal of computational biology : a journal of computational molecular cell biology},
  year={1998},
  volume={5 4},
  pages={
          667-80
        }
}
MORGAN is an integrated system for finding genes in vertebrate DNA sequences. MORGAN uses a variety of techniques to accomplish this task, the most distinctive of which is a decision tree classifier. The decision tree system is combined with new methods for identifying start codons, donor sites, and acceptor sites, and these are brought together in a frame-sensitive dynamic programming algorithm that finds the optimal segmentation of a DNA sequence into coding and noncoding regions (exons and… 

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References

SHOWING 1-10 OF 36 REFERENCES

Identification of protein coding regions in genomic DNA.

A computer program, GeneParser, which identifies and determines the fine structure of protein genes in genomic DNA sequences and can rapidly generate ranked suboptimal solutions, each of which is the optimum solution containing a given intron-exon junction is developed.

Locating Protein Coding Regions in Human DNA Using a Decision Tree Algorithm

  • S. Salzberg
  • Biology, Computer Science
    J. Comput. Biol.
  • 1995
The conclusion is that decision trees are a highly effective tool for identifying protein coding regions, on DNA sequences ranging from 54 to 162 base pairs in length.

Prediction of gene structure.

Identification of coding regions in genomic DNA sequences: an application of dynamic programming and neural networks.

Dynamic programming (DP) is applied to the problem of precisely identifying internal exons and introns in genomic DNA sequences and the program GeneParser employs the DP algorithm to enforce the constraints that introns and exons must be adjacent and non-overlapping and finds the highest scoring combination of intron and exon subject to these constraints.

Finding Genes in DNA with a Hidden Markov Model

A new Hidden Markov Model (HMM) system for segmenting uncharacterized genomic DNA sequences into exons, introns, and intergenic regions, called VEIL (Viterbi Exon-Intron Locator), obtains an overall accuracy on test data of 92% of total bases correctly labelled.

A Generalized Hidden Markov Model for the Recognition of Human Genes in DNA

A Generalized Hidden Markov Model (GHMM) provides the framework for describing the grammar of a legal parse of a DNA sequence and provides simple solutions for integrating cardinality constraints, reading frame constraints, "indels", and homology searching.

Assessment of protein coding measures.

This paper reviews and synthesizes the underlying coding measures from published algorithms and concludes that a very simple and obvious measure--counting oligomers--is more effective than any of the more sophisticated measures.

Gene recognition via spliced sequence alignment.

A spliced alignment algorithm and software tool that explores all possible exon assemblies in polynomial time and finds the multiexon structure with the best fit to a related protein.

Automated Gene Identification in Large-Scale Genomic Sequences

A computer program which can automatically parse the recognized exons into gene models that are most consistent with the available Expressed Sequence Tags (ESTs) and a set of biological heuristics, derived empirically.

Recognition of Genes in Human DNA Sequences

A new approach to computer-assisted gene recognition in higher eukaryote DNA is suggested. It allows one to use not only linear functions for scoring structures, but all functions satisfying natural