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Bioinformatics Vol. 16 no. 4 2000
Pages 367-371
© 2000 Oxford University Press

On the convergence of a clustering algorithm for protein-coding regions in microbial genomes

Pierre Baldi 1

1 Department of Information and Computer Science, University of California, Irvine, CA 92697-3425, USA

Received on October 3, 1999 ; revised on November 1, 1999 ; accepted on November 1, 1999

1Also at the Department of Biological Chemistry, College of Medicine, University of California, Irvine, USA. To whom all correspondence should be addressed.

Motivation: 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.

Contact: pfbaldi{at}ics.uci.edu


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