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Bayesian analysis of ChIP-chip BAC


Detects transcription factor bound regions, which incorporate the dependence between probes while making little assumptions about the bound regions (e.g., length). BAC is robust to probe outliers with an exchangeable prior for the variances, which allows different variances for the probes but still shrink extreme empirical variances. Parameter estimation is carried out using Markov chain Monte Carlo and inference is based on the joint distribution of the parameters. Bound regions are detected using posterior probabilities computed from the joint posterior distribution of neighboring probes. It was showed that these posterior probabilities are well calibrated and can be used to obtain an estimate of the false discovery rate.

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BAC classification

BAC specifications

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Command line interface
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Unix/Linux, Mac OS, Windows
Artistic License version 2.0

BAC distribution


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BAC support



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Department of Statistics, University of British Columbia, Vancouver, BC, Canada; Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, MA, USA

Funding source(s)

This work was funded by NIH grant 1R01 HG004069-01.

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