ArticleGenome biology2019
Measuring the reproducibility and quality of Hi-C data.
Article in Genome biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 96 papers.
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Who cites it
96 citing papers in PubMed, 197 citations in OpenAlex.
- Article
- Identification of differential topologically associating domains from low sequencing depth and pseudobulk chromatin contact maps.Genome research · 2026Article
- Characterization of Protein Co-Localization and Histone Modifications at Enhancers Anchored in Chromatin Loops.Biology · 2026Article
- HiChIA-Rep quantifies the similarity between enrichment-based chromatin interactions datasets.bioRxiv : the preprint server for biology · 2026Article
- CLAMP: predicting specific protein-mediated chromatin loops in diverse species with a chromatin accessibility language model.Genome biology · 2026Article
- Article
- Proteins driving liquid-liquid phase separation and histone modifications cooperatively associate with chromatin looping and transcriptional regulation.Epigenetics & chromatin · 2025Article
- Integrated analysis of differential intra-chromosomal community interactions: A study of breast cancer.Artificial intelligence in medicine · 2025Article
- HiC4D-SPOT: a spatiotemporal outlier detection tool for Hi-C data.Briefings in bioinformatics · 2025Article
- Reconstruction of diploid higher-order human 3D genome interactions from noisy Pore-C data using Dip3D.Nature structural & molecular biology · 2025Article
- Linking regulatory variants to target genes by integrating single-cell multiome methods and genomic distance.Nature genetics · 2025Article
- StripePy: fast and robust characterization of architectural stripes.Bioinformatics (Oxford, England) · 2025Article
- Comparing chromatin contact maps at scale: methods and insights.Nature methods · 2025Article
- Novel AI-powered computational method using tensor decomposition for identification of common optimal bin sizes when integrating multiple Hi-C datasets.Scientific reports · 2025Article
- Deciphering single-cell genomic architecture: insights into cellular heterogeneity and regulatory dynamics.Genomics & informatics · 2025Review
- Article
- CWL-Based Analysis Pipeline for Hi-C Data: From FASTQ Files to Matrices.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Epigenomic and 3D genomic mapping reveals developmental dynamics and subgenomic asymmetry of transcriptional regulatory architecture in allotetraploid cotton.Nature communications · 2024Article
- An integrative TAD catalog in lymphoblastoid cell lines discloses the functional impact of deletions and insertions in human genomes.Genome research · 2024Article
- CHARMER: detecting and harmonizing high-confidence chromatin interactions across tissues and Hi-C protocols.bioRxiv : the preprint server for biology · 2024Article
36 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
19 authors at 9 institutions in 1 country.
Funding
Abstract
backgroundHi-C is currently the most widely used assay to investigate the 3D organization of the genome and to study its role in gene regulation, DNA replication, and disease. However, Hi-C experiments are costly to perform and involve multiple complex experimental steps; thus, accurate methods for measuring the quality and reproducibility of Hi-C data are essential to determine whether the output should be used further in a study.
resultsUsing real and simulated data, we profile the performance of several recently proposed methods for assessing reproducibility of population Hi-C data, including HiCRep, GenomeDISCO, HiC-Spector, and QuASAR-Rep. By explicitly controlling noise and sparsity through simulations, we demonstrate the deficiencies of performing simple correlation analysis on pairs of matrices, and we show that methods developed specifically for Hi-C data produce better measures of reproducibility. We also show how to use established measures, such as the ratio of intra- to interchromosomal interactions, and novel ones, such as QuASAR-QC, to identify low-quality experiments.
conclusionsIn this work, we assess reproducibility and quality measures by varying sequencing depth, resolution and noise levels in Hi-C data from 13 cell lines, with two biological replicates each, as well as 176 simulated matrices. Through this extensive validation and benchmarking of Hi-C data, we describe best practices for reproducibility and quality assessment of Hi-C experiments. We make all software publicly available at http://github.com/kundajelab/3DChromatin_ReplicateQC to facilitate adoption in the community.
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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.