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.

• Documentation
       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  When referring to this software, please cite our unpublished manuscript describing the Bayesian hierarchical model of protein binding microarray (PBM) data: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