Segway
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.
Citation
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.
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