Segmentation and genome annotation

Description: The free Segway software package contains a novel method for analyzing multiple tracks of functional genomics data. Our method uses a dynamic Bayesian network (DBN) model, which enables it to analyze the entire genome at 1-bp resolution even in the face of heterogeneous patterns of missing data. This method is the first application of DBN techniques to genome-scale data and the first genomic segmentation method designed for use with the maximum resolution data available from ChIP-seq experiments without down-sampling. Segway uses the Graphical Models Toolkit (GMTK) for efficient DBN inference. Our software has extensive documentation and was designed from the outset with external users in mind.
Authors: Michael Hoffman, Eric Roberts, Rachel Chan, Matthew McNeil
Lab: Hoffman
Version: 2.0.5
Keywords: Segway, genomics, statistical analysis, ChIP-seq, Bayesian network, segmentation, Graphical Models Toolkit, Python
Licensing: GPLv2


Hoffman, M. M., Buske, O. J., Wang, J., Weng, Z., Bilmes, J. A., & Noble, W. S. (2012). Unsupervised pattern discovery in human chromatin structure through genomic segmentation. Nature methods, 9(5), 473.