Recognition of genes in human DNA sequences
MS Gelfand, LI Podolsky, TV Astakhova… - Journal of …, 1996 - liebertpub.com
MS Gelfand, LI Podolsky, TV Astakhova, MA Roytberg
Journal of Computational Biology, 1996•liebertpub.comABSTRACT 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 monotonicity conditions. The algorithm constructs the set of
structures guaranteed to contain an optimal structure for every function. So, it uncouples the
time-consuming step of generation of this set from the fast step of structure scoring, thus
making it simple to experiment with different functions. One particular scoring function, taking …
DNA is suggested. It allows one to use not only linear functions for scoring structures, but all
functions satisfying natural monotonicity conditions. The algorithm constructs the set of
structures guaranteed to contain an optimal structure for every function. So, it uncouples the
time-consuming step of generation of this set from the fast step of structure scoring, thus
making it simple to experiment with different functions. One particular scoring function, taking …
Abstract
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 monotonicity conditions. The algorithm constructs the set of structures guaranteed to contain an optimal structure for every function. So, it uncouples the time-consuming step of generation of this set from the fast step of structure scoring, thus making it simple to experiment with different functions. One particular scoring function, taking into account only codon usage and positional nucleotide frequencies of the splicing sites, has been implemented in the Genome Recognition and Exon Assembly Tool program, and has been tested on an independent sample of human genes, yielding 88% sensitivity and 79% specificity.
Key words: exon–intron structure, gene recognition, exons, multicriterial optimization, Pareto set.
Mary Ann Liebert
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