Segway – a tool for unsupervised segmentation of the human genome

Segway provides a method for automatically segmenting the genome into functional regions by analyzing different kinds of high-throughput data from different experiments. The approach is described in a recent paper from the Noble research lab. Segway uses a Dynamic Bayesian network (DBN) to model the interdependencies between different genomic sections, which is trained using ChIP-seq, DNase-seq, and FAIRE-seq data from ENCODE. They condensed the many discovered segment types into 25 labels which were then assigned functional categories, including familiar terms like gene start, gene middle, gene end, and enhancer. Using this labeling, they recovered many well-known genomic features.

They next compared their results to genome annotations from ChromHMM. While both models produce the same sort of output, the input is different; ChromHMM is trained only with histone modification data, while Segway uses a variety of data types. The authors find that Segway better identifies known elements, has higher segment resolution, and handles missing data better. They focus less on differences across cell type then in the ChromHMM analysis, although their model does appear to accomodate these differences. They conclude by suggesting a hierarchical segmentation approach that could make genome annotation more comprehensible.