ReviewGenome biology2015
Analysis methods for studying the 3D architecture of the genome.
Review in Genome biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 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
88 citing papers in PubMed.
- Cross-cell-line conservation-resolved interactions reveal conservation-dependent relationships among CTCF loops.BMC genomics · 2026Article
- EpiExpr: Predicting gene expression using epigenetic data and chromatin interactions.bioRxiv : the preprint server for biology · 2026Article
- Replication timing uncovers a two-compartment nuclear architecture of interphase euchromatin.The Plant cell · 2026Article
- Loss of multilevel 3D genome organization during breast cancer progression.Genome research · 2026Article
- DiffHiChIP: Identifying differential chromatin contacts from HiChIP data.Cell reports methods · 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
- scHiClassifier: a deep learning framework for cell type prediction by fusing multiple feature sets from single-cell Hi-C data.Briefings in bioinformatics · 2024Article
- A review of deep learning models for the prediction of chromatin interactions with DNA and epigenomic profiles.Briefings in bioinformatics · 2024Review
- scHiCyclePred: a deep learning framework for predicting cell cycle phases from single-cell Hi-C data using multi-scale interaction information.Communications biology · 2024Article
- Comparative study on chromatin loop callers using Hi-C data reveals their effectiveness.BMC bioinformatics · 2024Article
- Computational methods for analysing multiscale 3D genome organization.Nature reviews. Genetics · 2024Review
- Hi-C, a chromatin 3D structure technique advancing the functional genomics of immune cells.Frontiers in genetics · 2024Review
- Revisiting Assessment of Computational Methods for Hi-C Data Analysis.International journal of molecular sciences · 2023Article
- Hi-C Analysis to Identify Genome-Wide Chromatin Structural Aberration in Cancer.Methods in molecular biology (Clifton, N.J.) · 2023Article
- DeepPHiC: predicting promoter-centered chromatin interactions using a novel deep learning approach.Bioinformatics (Oxford, England) · 2023Article
- Methods for the Differential Analysis of Hi-C Data.Methods in molecular biology (Clifton, N.J.) · 2022Article
- The 3D Genome: From Structure to Function.International journal of molecular sciences · 2021Review
- Integrative Analysis of Genome, 3D Genome, and Transcriptome Alterations of Clinical Lung Cancer Samples.Genomics, proteomics & bioinformatics · 2021Article
- Understanding 3D genome organization by multidisciplinary methods.Nature reviews. Molecular cell biology · 2021Review
28 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The rapidly increasing quantity of genome-wide chromosome conformation capture data presents great opportunities and challenges in the computational modeling and interpretation of the three-dimensional genome. In particular, with recent trends towards higher-resolution high-throughput chromosome conformation capture (Hi-C) data, the diversity and complexity of biological hypotheses that can be tested necessitates rigorous computational and statistical methods as well as scalable pipelines to interpret these datasets. Here we review computational tools to interpret Hi-C data, including pipelines for mapping, filtering, and normalization, and methods for confidence estimation, domain calling, visualization, and three-dimensional modeling.
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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.