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Users of Segway will probably be interested in our new review of segmentation and genome annotation (SAGA) algorithms generally. Now available on arXiv: Libbrecht MW*, Chan RCW*, Hoffman MM. “Segmentation and genome annotation algorithms.” <http://arxiv.org/abs/2101.00688> 2020. Preprint: http://arxiv.org/abs/2101.00688 Abstract: 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. Michael Michael M. Hoffman, PhD Senior Scientist, Princess Margaret Cancer Centre Associate Professor, Department of Medical Biophysics, University of Toronto Associate Professor, Department of Computer Science, University of Toronto Faculty Affiliate, Vector Institute Princess Margaret Cancer Research Tower 11-311 101 College St Toronto, ON M5G 1L7 https://hoffmanlab.org/