ArticleNucleic acids research2020
Measuring significant changes in chromatin conformation with ACCOST.
Article in Nucleic acids research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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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.
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Who cites it
12 citing papers in PubMed, 29 citations in OpenAlex.
- A comprehensive review and benchmark of differential analysis tools for Hi-C data.Briefings in bioinformatics · 2025Review
- Systematic identification of interchromosomal interaction networks supports the existence of specialized RNA factories.Genome research · 2024Article
- DiffGR: Detecting Differentially Interacting Genomic Regions from Hi-C Contact Maps.Genomics, proteomics & bioinformatics · 2024Article
- A Comprehensive Evaluation of Generalizability of Deep Learning-Based Hi-C Resolution Improvement Methods.Genes · 2023Article
- Reply to: Revisiting the use of structural similarity index in Hi-C.Nature genetics · 2023Article
- SnapHiC-D: a computational pipeline to identify differential chromatin contacts from single-cell Hi-C data.Briefings in bioinformatics · 2023Article
- HiConfidence: a novel approach uncovering the biological signal in Hi-C data affected by technical biases.Briefings in bioinformatics · 2023Article
- Normalization and de-noising of single-cell Hi-C data with BandNorm and scVI-3D.Genome biology · 2022Article
- Decrypting the complexity of the human malaria parasite biology through systems biology approaches.Frontiers in systems biology · 2022Article
- HiC-DC+ enables systematic 3D interaction calls and differential analysis for Hi-C and HiChIP.Nature communications · 2021Article
- Deciphering hierarchical organization of topologically associated domains through change-point testing.BMC bioinformatics · 2021Article
- CHESS enables quantitative comparison of chromatin contact data and automatic feature extraction.Nature genetics · 2020Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
Chromatin conformation assays such as Hi-C cannot directly measure differences in 3D architecture between cell types or cell states. For this purpose, two or more Hi-C experiments must be carried out, but direct comparison of the resulting Hi-C matrices is confounded by several features of Hi-C data. Most notably, the genomic distance effect, whereby contacts between pairs of genomic loci that are proximal along the chromosome exhibit many more Hi-C contacts that distal pairs of loci, dominates every Hi-C matrix. Furthermore, the form that this distance effect takes often varies between different Hi-C experiments, even between replicate experiments. Thus, a statistical confidence measure designed to identify differential Hi-C contacts must accurately account for the genomic distance effect or risk being misled by large-scale but artifactual differences. ACCOST (Altered Chromatin COnformation STatistics) accomplishes this goal by extending the statistical model employed by DEseq, re-purposing the 'size factors,' which were originally developed to account for differences in read depth between samples, to instead model the genomic distance effect. We show via analysis of simulated and real data that ACCOST provides unbiased statistical confidence estimates that compare favorably with competing methods such as diffHiC, FIND and HiCcompare. ACCOST is freely available with an Apache license at https://bitbucket.org/noblelab/accost.
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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.