Adding sequence context to a Markov background model improves the identification of regulatory elements | Zendy

Nak-Kyeong Kim | Zendy; Kannan Tharakaraman | Zendy; John L. Spouge | Zendy

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Adding sequence context to a Markov background model improves the identification of regulatory elements

Author(s) -

Nak-Kyeong Kim,

Kannan Tharakaraman,

John L. Spouge

Publication year - 2006

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btl528

Subject(s) - markov chain , markov model , computer science , context (archaeology) , hidden markov model , identification (biology) , variable order markov model , maximum entropy markov model , markov process , statistical model , parametric statistics , correlation , machine learning , data mining , sequence (biology) , statistics , artificial intelligence , mathematics , biology , paleontology , botany , geometry , genetics

Many computational methods for identifying regulatory elements use a likelihood ratio between motif and background models. Often, the methods use a background model of independent bases. At least two different Markov background models have been proposed with the aim of increasing the accuracy of predicting regulatory elements. Both Markov background models suffer theoretical drawbacks, so this article develops a third, context-dependent Markov background model from fundamental statistical principles.

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