On the convergence of a clustering algorithm for protein-coding regions in microbial genomes.
BACKGROUND
As the number of fully sequenced prokaryotic genomes continues to grow rapidly, computational methods for reliably detecting protein-coding regions become even more important. Audic and Claverie (1998) Proc. Natl Acad. Sci. USA, 95, 10026-10031, have proposed a clustering algorithm for protein-coding regions in microbial genomes. The algorithm is based on three Markov models of order k associated with subsequences extracted from a given genome. The parameters of the three Markov models are recursively updated by the algorithm which, in simulations, always appear to converge to a unique stable partition of the genome. The partition corresponds to three kinds of regions: (1) coding on the direct strand, (2) coding on the complementary strand, (3) non-coding.


RESULTS
Here we provide an explanation for the convergence of the algorithm by observing that it is essentially a form of the expectation maximization (EM) algorithm applied to the corresponding mixture model. We also provide a partial justification for the uniqueness of the partition based on identifiability. Other possible variations and improvements are briefly discussed.
Baldi
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Department of Information and Computer Science, University of California, Irvine, CA 92697-3425, USA. pfbaldi@ics.uci.edu
D000465:Algorithms; D003198:Computer Simulation; D008390:Markov Chains; D011506:Proteins / Q000235:genetics*
D011506:Proteins