ReviewPLoS computational biology2021
Segmentation and genome annotation algorithms for identifying chromatin state and other genomic patterns.
Review in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Pan-cell type continuous chromatin state annotation of 1,698 epigenomes from the International Human Epigenome Consortium.Genome biology · 2026Article
- Integrated flexible DNA methylation-chromatin segmentation modeling enhances epigenomic state annotation.Nucleic acids research · 2026Article
- Segzoo: a turnkey system that summarizes genome annotations.Bioinformatics (Oxford, England) · 2026Article
- MissenseHMM: state-based annotations for missense variants through joint modeling of pathogenicity scores.bioRxiv : the preprint server for biology · 2026Article
- CDACHIE: chromatin domain annotation by integrating chromatin interaction and epigenomic data with contrastive learning.Bioinformatics (Oxford, England) · 2025Article
- Epilogos: information-theoretic navigation of multi-tissue functional genomic annotations.bioRxiv : the preprint server for biology · 2025Article
- RobusTAD: reference panel based annotation of nested topologically associating domains.Genome biology · 2025Article
- ChromActivity: integrative epigenomic and functional characterization assay based annotation of regulatory activity across diverse human cell types.Genome biology · 2025Article
- Perspective on recent developments and challenges in regulatory and systems genomics.Bioinformatics advances · 2025Review
- Interspecies regulatory landscapes and elements revealed by novel joint systematic integration of human and mouse blood cell epigenomes.Genome research · 2024Article
- Robust chromatin state annotation.Genome research · 2024Article
- EpiSegMix: a flexible distribution hidden Markov model with duration modeling for chromatin state discovery.Bioinformatics (Oxford, England) · 2024Article
- JMnorm: a novel joint multi-feature normalization method for integrative and comparative epigenomics.Nucleic acids research · 2024Article
- DeepRegFinder: deep learning-based regulatory elements finder.Bioinformatics advances · 2024Article
- ChromGene: gene-based modeling of epigenomic data.Genome biology · 2023Article
- Universal chromatin state annotation of the mouse genome.Genome biology · 2023Article
- Snapshot: a package for clustering and visualizing epigenetic history during cell differentiation.BMC bioinformatics · 2023Article
- A framework for group-wise summarization and comparison of chromatin state annotations.Bioinformatics (Oxford, England) · 2023Article
- Integrative chromatin domain annotation through graph embedding of Hi-C data.Bioinformatics (Oxford, England) · 2023Article
- Enhancer Function and Evolutionary Roles of Human Accelerated Regions.Annual review of genetics · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.