Assessment of protein coding measures.
A number of methods for recognizing protein coding genes in DNA sequence have been published over the last 13 years, and new, more comprehensive algorithms, drawing on the repertoire of existing techniques, continue to be developed. To optimize continued development, it is valuable to systematically review and evaluate published techniques. At the core of most gene recognition algorithms is one or more coding measures--functions which produce, given any sample window of sequence, a number or vector intended to measure the degree to which a sample sequence resembles a window of 'typical' exonic DNA. In this paper we review and synthesize the underlying coding measures from published algorithms. A standardized benchmark is described, and each of the measures is evaluated according to this benchmark. Our main conclusion is that a very simple and obvious measure--counting oligomers--is more effective than any of the more sophisticated measures. Different measures contain different information. However there is a great deal of redundancy in the current suite of measures. We show that in future development of gene recognition algorithms, attention can probably be limited to six of the twenty or so measures proposed to date.
Fickett
J W
JW
Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, NM 87545.
Tung
C S
CS
D000465:Algorithms; D001482:Base Composition; D001483:Base Sequence; D003062:Codon / Q000235:genetics; D004247:DNA / Q000235:genetics*; D005091:Exons; D005583:Fourier Analysis; D005796:Genes; D005821:Genetic Techniques; D006801:Humans; D011506:Proteins / Q000235:genetics*
D003062:Codon; D011506:Proteins; D004247:DNA