Research article

OHMM: a Hidden Markov Model accurately predicting the occupancy of a transcription factor with a self-overlapping binding motif

Amar Drawid^1,2 , Nupur Gupta^{* 3,4} , Vijayalakshmi H Nagaraj^{* 1} , Céline Gélinas^3,5 and Anirvan M Sengupta^1,6

¹BioMAPS Institute for Quantitative Biology, Rutgers University, Piscataway, NJ, USA

²Bioinformatics Group, sanofi-aventis U.S., Bridgewater, NJ, USA

³Center for Advanced Biotechnology and Medicine, Robert Wood Johnson Medical School, Piscataway, NJ, USA

⁴Graduate Program in Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Piscataway, NJ, USA

⁵Department of Biochemistry, Robert Wood Johnson Medical School, Piscataway, NJ, USA

⁶Department of Physics and Astronomy, Rutgers University, Piscataway, NJ, USA

author email corresponding author email* Contributed equally

BMC Bioinformatics 2009, 10:208doi:10.1186/1471-2105-10-208

Published:	7 July 2009

Abstract

Background

DNA sequence binding motifs for several important transcription factors happen to be self-overlapping. Many of the current regulatory site identification methods do not explicitly take into account the overlapping sites. Moreover, most methods use arbitrary thresholds and fail to provide a biophysical interpretation of statistical quantities. In addition, commonly used approaches do not include the location of a site with respect to the transcription start site (TSS) in an integrated probabilistic framework while identifying sites. Ignoring these features can lead to inaccurate predictions as well as incorrect design and interpretation of experimental results.

Results

We have developed a tool based on a Hidden Markov Model (HMM) that identifies binding location of transcription factors with preference for self-overlapping DNA motifs by combining the effects of their alternative binding modes. Interpreting HMM parameters as biophysical quantities, this method uses the occupancy probability of a transcription factor on a DNA sequence as the discriminant function, earning the algorithm the name OHMM: Occupancy via Hidden Markov Model. OHMM learns the classification threshold by training emission probabilities using unaligned sequences containing known sites and estimating transition probabilities to reflect site density in all promoters in a genome. While identifying sites, it adjusts parameters to model site density changing with the distance from the transcription start site. Moreover, it provides guidance for designing padding sequences in gel shift experiments. In the context of binding sites to transcription factor NF-κB, we find that the occupancy probability predicted by OHMM correlates well with the binding affinity in gel shift experiments. High evolutionary conservation scores and enrichment in experimentally verified regulated genes suggest that NF-κB binding sites predicted by our method are likely to be functional.

Conclusion

Our method deals specifically with identifying locations with multiple overlapping binding sites by computing the local occupancy of the transcription factor. Moreover, considering OHMM as a biophysical model allows us to learn the classification threshold in a principled manner. Another feature of OHMM is that we allow transition probabilities to change with location relative to the TSS. OHMM could be used to predict physical occupancy, and provides guidance for proper design of gel-shift experiments. Based upon our predictions, new insights into NF-κB function and regulation and possible new biological roles of NF-κB were uncovered.