Bayesian Hierarchical Model of Protein Binding Microarray (PBM) Data
Online manual, source code and executables
This webpage accompanies the paper by Jiang et al., 2013; full citation below.
The Bayesian Protein Binding Microarray (PBM) Analysis Suite provides the in-house tools and the procedural methods used in the background noise estimation and correction, transcription factor (TF) subclassfication and TF-common and TF-preferred k-mer identification based on universal protein binding microarrays (PBMs; see Berger et al., 2006) k-mer data.
This document acts as a tutorial for the Bayesian analysis of universal PBMs and as a guide to the software and in-house scripts involved in procedures such as decomposition of observed enrichment score, correction based on estimated k-mer background noise and identification of TF-common and TF-preferred k-mers.
Bayesian PBM Analysis Suite
A zip file containing the components (includes source codes and examples) of the Bayesian PBM Analysis Suite.
- Citing this software:
When referring to this software, please cite our unpublished manuscript describing the Bayesian hierarchical model of protein binding microarray (PBM) data:
Jiang B, Liu JS and Bulyk ML (2013) Bayesian hierarchical model of protein binding microarray k-mer data reduces noise and identifies transcription factor subclasses and preferred k-mers. Bioinformatics, 29(11): 1390-1398.
Questions, comments or suggestions regarding the materials presented here should be directed to Martha L. Bulyk (mlbulyk at receptor.med.harvard.edu).
This page was last updated on March 26, 2013