Segway: simultaneous segmentation of multiple functional genomics data sets with heterogeneous patterns of missing data

New functional genomics methods enabled by high-throughput DNA sequencing have begun to produce an unprecedented amount of data anchored to the genome of humans and other species. We have developed a method to identify joint patterns in the results of multiple classes of functional genomics experiments. The method partitions the genome into variable-length segments using a dynamic Bayesian network where the dynamic (or “time”) axis represents genomic position. Segments are assigned one of a finite number of labels such that the vectors of observations are similar in segments with the same label. A multinet switching structure allows inference on sequences with combinations of missing data in different tracks that vary at each position, without downsampling or interpolation. This permits us to take full advantage of the high-resolution data generated by sequencing assays, working at up to 1-base-pair resolution. Our system can also incorporate other kinds of data into its classification, including lower-resolution continuous data such as microarray data, or discrete data such as the dinucleotide sequence beginning at each position. We demonstrate the use of the method in both unsupervised and semisupervised training of segment parameters.