Segmentation and genome annotation algorithms for identifying chromatin state and other genomic patterns

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, and discuss the outlook for future work. This review is intended for those interested in applying SAGA methods and for computational researchers interested in improving upon them.

A framework for summarizing chromatin state annotations within and identifying differential annotations across groups of samples

“…To facilitate the use of CSREP, we provide an implementation of CSREP as a snakemake pipeline (Mölder et al ., 2021) with a detailed tutorial that only requires users to modify parameters in a yaml file. The program can be run either on local computers or on computing clusters, in which case snakemake will optimize the workflow for execution.…”

“…After CSREP trains the multi-class logistic regression model on training data that constitute 10% of the genome, and l 2-norm penalty. The model is implemented using Python’s sklearn, pybedtools package and snakemake (Dale et al ., 2011; Quinlan and Hall, 2010; Mölder et al ., 2021). CSREP applies the model to generate predictions of genome-wide probabilistic chromatin state map for sample n , which is presented in a matrix of size G * S .…”

“…Efforts by large consortia and individual labs have produced chromatin state maps for many cell and tissue types (Roadmap Epigenomics Consortium et al, 2015;Consortium, 2012;Zhu et al, 2013;Barski et al, 2007). A popular representation of such data is chromatin states defined by the combinatorial and spatial patterns of multiple marks, which are generated by methods such as ChromHMM and Segway (Libbrecht et al, 2021) (Ernst andKellis, 2010, 2012;Hoffman et al, 2012), and correspond to diverse classes of genomic elements including various types of enhancers and promoters.…”

ChromGene: Gene-Based Modeling of Epigenomic Data

“…Notably, approaches have been developed for annotating the genome into 'chromatin states' based on the combinatorial and spatial patterns of epigenomic marks inferred de novo from the data. These different 'chromatin states' can correspond to different classes of genomic elements, including enhancers, promoters, and repressive regions [8][9][10][11]. Annotations from these methods have been used for a diverse range of applications, including understanding gene regulation and genetic variants associated with disease [2,11,12].…”

“…These different 'chromatin states' can correspond to different classes of genomic elements, including enhancers, promoters, and repressive regions [8][9][10][11]. Annotations from these methods have been used for a diverse range of applications, including understanding gene regulation and genetic variants associated with disease [2,11,12].…”