Interpolated markov chains for eukaryotic promoter recognition

Interpolated markov chains for eukaryotic promoter recognition

MOTIVATION We describe a new content-based approach for the detection of promoter regions of eukaryotic protein encoding genes. Our system is based on three interpolated Markov chains (IMCs) of different order which are trained on coding, non-coding and promoter sequences. It was recently shown that the interpolation of Markov chains leads to stable parameters and improves on the results in mic...

Interpolated Markov models for eukaryotic gene finding.

Computational gene finding research has emphasized the development of gene finders for bacterial and human DNA. This has left genome projects for some small eukaryotes without a system that addresses their needs. This paper reports on a new system, GlimmerM, that was developed to find genes in the malaria parasite Plasmodium falciparum. Because the gene density in P. falciparum is relatively hi...

Eukaryotic promoter recognition.

On ly recen tly h as it becom e common to determ in e eukaryotic gen om ic sequen ces large en ough to con tain several gen es. With th ese data com es a n ew problem for gen e fin din g program s: to partit ion a set of exon s correctly am on g several gen es. On e lin e of developm en t in eukaryot ic gen e iden tification begin s with codin g region iden tificat ion by statist ical m ean s a...

Interpolated Hidden Markov Models Estimated Using Conditional ML for Eukaryotic Gene Annotation

Taylor Expansion for the Entropy Rate of Hidden Markov Chains

We study the entropy rate of a hidden Markov process, defined by observing the output of a symmetric channel whose input is a first order Markov process. Although this definition is very simple, obtaining the exact amount of entropy rate in calculation is an open problem. We introduce some probability matrices based on Markov chain's and channel's parameters. Then, we try to obtain an estimate ...