Transcription binding site prediction using Markov models.

Irina Abnizova; Alistair G Rust; Mark Robinson; Rene Te Boekhorst; Walter R Gilks

doi:10.1142/s0219720006001813

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Transcription binding site prediction using Markov models.

Irina Abnizova·Alistair G Rust·Mark Robinson·Rene Te Boekhorst·Walter R Gilks

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Abstract

One of the main goals of analysing DNA sequences is to understand the temporal and positional information that specifies gene expression. An important step in this process is the recognition of gene expression regulatory elements. Experimental procedures for this are slow and costly. In this paper we present a computational non-supervised algorithm that facilitates the process by statistically identifying the most likely regions within a putative regulatory sequence. A probabilistic technique is presented, based on the approximation of regulatory DNA with a Markov chain, for the location of putative transcription factor binding sites in a single stretch of DNA. Hereto we developed a procedure to approximate the order of Markov model for a given DNA sequence that circumvents some of the prohibitive assumptions underlying Markov modeling. Application of the algorithm to data from 55 genes in five species shows the high sensitivity of this Markov search algorithm. Our algorithm does not require any prior knowledge in the form of description or cross-genomic comparison; it is context sensitive and takes DNA heterogeneity into account.

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Artificial Intelligence Binding Sites Chromosome Mapping Computer Simulation DNA Markov Chains Models, Genetic Models, Statistical Pattern Recognition, Automated Protein Binding Regulatory Sequences, Nucleic Acid Sequence Alignment Sequence Analysis, DNA Transcription Factors Journal Article

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Irina Abnizova, Alistair G Rust, Mark Robinson, Rene Te Boekhorst, Walter R Gilks. 2006. Transcription binding site prediction using Markov models.. https://doi.org/10.1142/s0219720006001813

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