ArticleBioinformatics (Oxford, England)2019
Selfish: discovery of differential chromatin interactions via a self-similarity measure.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed, 58 citations in OpenAlex.
- Targeting ZMIZ1 induces differentiation in acute myeloid leukemia via chromatin remodeling.Signal transduction and targeted therapy · 2026Article
- Epigenetics and chromatin structure regulateeLife · 2026Article
- An EGR1-dependent cascade modulates genome architecture at the CSF1R locus.Nucleic acids research · 2026Article
- Epigenetics and chromatin structure regulatebioRxiv : the preprint server for biology · 2026Article
- From 2D to 4D: a containerized workflow and browser to explore dynamic chromatin architecture.BMC bioinformatics · 2026Article
- DiffHiChIP: Identifying differential chromatin contacts from HiChIP data.Cell reports methods · 2025Article
- PB-DiffHiC: a statistical framework for detecting differential chromatin interactions from high resolution pseudo-bulk Hi-C data.BMC genomics · 2025Article
- From 2D to 4D: a Containerized Workflow and Browser to Explore Dynamic Chromatin Architecture.bioRxiv : the preprint server for biology · 2025Article
- Examining the dynamics of three-dimensional genome organization with multitask matrix factorization.Genome research · 2025Article
- A comprehensive review and benchmark of differential analysis tools for Hi-C data.Briefings in bioinformatics · 2025Review
- Mapping the 3D genome architecture.Computational and structural biotechnology journal · 2025Review
- Article
- A mini-review of single-cell Hi-C embedding methods.Computational and structural biotechnology journal · 2024Review
- ENT3C: an entropy-based similarity measure for Hi-C and micro-C derived contact matrices.NAR genomics and bioinformatics · 2024Article
- Article
- DiffDomain enables identification of structurally reorganized topologically associating domains.Nature communications · 2024Article
- HiConfidence: a novel approach uncovering the biological signal in Hi-C data affected by technical biases.Briefings in bioinformatics · 2023Article
- Article
- Methods for the Differential Analysis of Hi-C Data.Methods in molecular biology (Clifton, N.J.) · 2022Article
- CoolBox: a flexible toolkit for visual analysis of genomics data.BMC bioinformatics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
motivationHigh-throughput conformation capture experiments, such as Hi-C provide genome-wide maps of chromatin interactions, enabling life scientists to investigate the role of the three-dimensional structure of genomes in gene regulation and other essential cellular functions. A fundamental problem in the analysis of Hi-C data is how to compare two contact maps derived from Hi-C experiments. Detecting similarities and differences between contact maps are critical in evaluating the reproducibility of replicate experiments and for identifying differential genomic regions with biological significance. Due to the complexity of chromatin conformations and the presence of technology-driven and sequence-specific biases, the comparative analysis of Hi-C data is analytically and computationally challenging.
resultsWe present a novel method called Selfish for the comparative analysis of Hi-C data that takes advantage of the structural self-similarity in contact maps. We define a novel self-similarity measure to design algorithms for (i) measuring reproducibility for Hi-C replicate experiments and (ii) finding differential chromatin interactions between two contact maps. Extensive experimental results on simulated and real data show that Selfish is more accurate and robust than state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION: https://github.com/ucrbioinfo/Selfish.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.