DETECTION OF EUKARYOTIC PROMOTERS USING MARKOV TRANSITION MATRICES

Eukaryotic promoters are among the most important functional domains y et to be characterized in a satisfactory manner in genomic sequences. Most current detection methods rely on the recognition of individual t ranscription elements using position-weight matrices (PWM) or consensu s sequences. Here, we study a simple promoter detection algorithm base d on Markov transition matrices built from sequences upward from prove n transcription initiation sites. The performances have been evaluated on the training set and on a test set of promoter-containing sequence s. The results on the training set are surprisingly good, given that t he algorithm does not incorporate any specific knowledge about promote rs. Yet, the program exhibits the pathological behaviour typical of al l training set-based methods: a significant decline in performance whe n confronted with previously unseen sequences. Thus, the Markov algori thm, like the others presently available, does not truly capture the e ssence of eukaryotic promoters. A detection program based on a Markov model is likely to be blind to categories of promoters without close r epresentatives in the training set. (C) 1997 Elsevier Science Ltd.