Segmentation and genome annotation algorithms
This review provides a comprehensive overview of SAGA algorithms, which are crucial for understanding genome activity and gene regulation for researchers in genomics and bioinformatics.
This paper reviews Segmentation and Genome Annotation (SAGA) algorithms, which are unsupervised methods used to partition the genome and assign labels to segments based on epigenomic datasets like ChIP-seq measurements. These algorithms discover categories of genome activity such as promoters and enhancers.
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.