Statistical mechanics of transcription-factor binding site discovery using Hidden Markov Models

Details Statistical mechanics of transcription-factor binding site discovery using Hidden Markov Models Statistical mechanics of transcription-factor binding site discovery using Hidden Markov Models

2010-08-18

arxiv.org/abs/1008.3151v2

Physics

Condensed Matter

Statistical Mechanics

25 pages, 2 figures, 1 table V2 - typos fixed and new references added

Scientific paper

Hidden Markov Models (HMMs) are a commonly used tool for inference of transcription factor (TF) binding sites from DNA sequence data. We exploit the mathematical equivalence between HMMs for TF binding and the "inverse" statistical mechanics of hard rods in a one-dimensional disordered potential to investigate learning in HMMs. We derive analytic expressions for the Fisher information, a commonly employed measure of confidence in learned parameters, in the biologically relevant limit where the density of binding sites is low. We then use techniques from statistical mechanics to derive a scaling principle relating the specificity (binding energy) of a TF to the minimum amount of training data necessary to learn it.

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