BayesPeak
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Application data |
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| Biological application domain(s) | ChIP-Seq |
|---|---|
| Principal bioinformatics method(s) | Hidden Markov Model |
| Maintained? | Maybe |
| Programming language(s) | C, Perl |
Summary: A fully Bayesian hidden Markov model to detect enriched locations in the genome.
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Our proposed statistical algorithm, BayesPeak, uses a fully Bayesian hidden Markov model to detect enriched locations in the genome. The structure accommodates the natural features of the Solexa/Illumina sequencing data and allows for overdispersion in the abundance of reads in different regions. Moreover, a control sample can be incorporated in the analysis to account for experimental and sequence biases. Markov chain Monte Carlo algorithms are applied to estimate the posterior distributions of the model parameters, and posterior probabilities are used to detect the sites of interest. CONCLUSION: We have presented a flexible approach for identifying peaks from ChIP-seq reads, suitable for use on both transcription factor binding and histone modification data. Our method estimates probabilities of enrichment that can be used in downstream analysis.
