ArticleBMC genomics2022
ProbC: joint modeling of epigenome and transcriptome effects in 3D genome.
Article in BMC genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 17 citations in OpenAlex.
- Assessing reproducibility of Hi-C chromatin interactions using stratum-adjusted irreproducible discovery rate.Bioinformatics (Oxford, England) · 2026Article
- Enhancing tumor T cell antigen prediction by integrating deep protein representations.BMC bioinformatics · 2026Article
- Integrated flexible DNA methylation-chromatin segmentation modeling enhances epigenomic state annotation.Nucleic acids research · 2026Article
- LLM-guided expert feature extraction with fusion-based complementary learning network for fish freshness classification using eye images.Scientific reports · 2026Article
- A comparative analysis of topological domain callers over RNA-associated interactome.BMC bioinformatics · 2026Article
- Deep DNA and protein level feature integration for robust clinical variant interpretation using probabilistic gradient boosting.Frontiers in digital health · 2026Article
- OMNIS: a spatially informed multi-omics deep-learning framework for tumor recurrence prediction and primary-metastatic tumor differentiation title page.Frontiers in artificial intelligence · 2026Article
- Analysis of chromatin structure reveals the connection between sQTLs and the splicing of distant genes.Scientific reports · 2025Article
- Joint analysis of sQTL and Hi-C reveals spatial proximity between sQTLs and target genes in cancer tissues.Scientific reports · 2025Article
- Review
Corrections and comments
- Erratum issued
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHi-C and its high nucleosome resolution variant Micro-C provide a window into the spatial packing of a genome in 3D within the cell. Even though both techniques do not directly depend on the binding of specific antibodies, previous work has revealed enriched interactions and domain structures around multiple chromatin marks; epigenetic modifications and transcription factor binding sites. However, the joint impact of chromatin marks in Hi-C and Micro-C interactions have not been globally characterized, which limits our understanding of 3D genome characteristics. An emerging question is whether it is possible to deduce 3D genome characteristics and interactions by integrative analysis of multiple chromatin marks and associate interactions to functionality of the interacting loci.
resultWe come up with a probabilistic method PROBC to decompose Hi-C and Micro-C interactions by known chromatin marks. PROBC is based on convex likelihood optimization, which can directly take into account both interaction existence and nonexistence. Through PROBC, we discover histone modifications (H3K27ac, H3K9me3, H3K4me3, H3K4me1) and CTCF as particularly predictive of Hi-C and Micro-C contacts across cell types and species. Moreover, histone modifications are more effective than transcription factor binding sites in explaining the genome's 3D shape through these interactions. PROBC can successfully predict Hi-C and Micro-C interactions in given species, while it is trained on different cell types or species. For instance, it can predict missing nucleosome resolution Micro-C interactions in human ES cells trained on mouse ES cells only from these 5 chromatin marks with above 0.75 AUC. Additionally, PROBC outperforms the existing methods in predicting interactions across almost all chromosomes.
conclusionVia our proposed method, we optimally decompose Hi-C interactions in terms of these chromatin marks at genome and chromosome levels. We find a subset of histone modifications and transcription factor binding sites to be predictive of both Hi-C and Micro-C interactions and TADs across human, mouse, and different cell types. Through learned models, we can predict interactions on species just from chromatin marks for which Hi-C data may be limited.
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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.