  Segmentation and genome annotation (SAGA) algorithms are widely used to
understand genome activity and gene regulation. These algorithms take as input
epigenomic datasets, such as chromatin immunoprecipitation-sequencing
(ChIP-seq) measurements of histone modifications or transcription factor
binding. They partition the genome and assign a label to each segment such that
positions with the same label exhibit similar patterns of input data. SAGA
algorithms discover categories of activity such as promoters, enhancers, or
parts of genes without prior knowledge of known genomic elements. In this
sense, they generally act in an unsupervised fashion like clustering
algorithms, but with the additional simultaneous function of segmenting the
genome. Here, we review the common methodological framework that underlies
these methods, review variants of and improvements upon this basic framework,
catalogue existing large-scale reference annotations, and discuss the outlook
for future work.
