ArticleBioinformatics (Oxford, England)2021
Fast detection of differential chromatin domains with SCIDDO.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Continuous multi-omics pathway enrichment analysis resolves hidden functional heterogeneity.Briefings in bioinformatics · 2026Article
- chromIDEAS reveals epigenetic dynamics via multi-dimensional clustering of chromatin states.Nucleic acids research · 2026Article
- IKAROS Gene Regulatory Network Reveal ERG as a Vulnerability in B-cell Acute Lymphoblastic Leukemia.bioRxiv : the preprint server for biology · 2025Article
- Robust chromatin state annotation.Genome research · 2024Article
- Alterations in the hepatocyte epigenetic landscape in steatosis.Epigenetics & chromatin · 2023Article
- A framework for group-wise summarization and comparison of chromatin state annotations.Bioinformatics (Oxford, England) · 2023Article
- Computational methods to explore chromatin state dynamics.Briefings in bioinformatics · 2022Review
- Segmentation and genome annotation algorithms for identifying chromatin state and other genomic patterns.PLoS computational biology · 2021Review
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2 authors.
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Abstract
motivationThe generation of genome-wide maps of histone modifications using chromatin immunoprecipitation sequencing is a standard approach to dissect the complexity of the epigenome. Interpretation and differential analysis of histone datasets remains challenging due to regulatory meaningful co-occurrences of histone marks and their difference in genomic spread. To ease interpretation, chromatin state segmentation maps are a commonly employed abstraction combining individual histone marks. We developed the tool SCIDDO as a fast, flexible and statistically sound method for the differential analysis of chromatin state segmentation maps.
resultsWe demonstrate the utility of SCIDDO in a comparative analysis that identifies differential chromatin domains (DCD) in various regulatory contexts and with only moderate computational resources. We show that the identified DCDs correlate well with observed changes in gene expression and can recover a substantial number of differentially expressed genes (DEGs). We showcase SCIDDO's ability to directly interrogate chromatin dynamics, such as enhancer switches in downstream analysis, which simplifies exploring specific questions about regulatory changes in chromatin. By comparing SCIDDO to competing methods, we provide evidence that SCIDDO's performance in identifying DEGs via differential chromatin marking is more stable across a range of cell-type comparisons and parameter cut-offs. AVAILABILITY AND IMPLEMENTATION: The SCIDDO source code is openly available under github.com/ptrebert/sciddo. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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