ArticleNature communications2019
Binless normalization of Hi-C data provides significant interaction and difference detection independent of resolution.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 14 citations in OpenAlex.
- MethyLasso: a segmentation approach to analyze DNA methylation patterns and identify differentially methylated regions from whole-genome datasets.Nucleic acids research · 2024Article
- Pairtools: From sequencing data to chromosome contacts.PLoS computational biology · 2024Article
- The 3D genome and its impacts on human health and disease.Life medicine · 2023Review
- Three-dimensional chromatin architecture datasets for aging and Alzheimer's disease.Scientific data · 2023Article
- Molecular and computational approaches to map regulatory elements in 3D chromatin structure.Epigenetics & chromatin · 2021Review
- HIPPIE2: a method for fine-scale identification of physically interacting chromatin regions.NAR genomics and bioinformatics · 2020Article
- Serpentine: a flexible 2D binning method for differential Hi-C analysis.Bioinformatics (Oxford, England) · 2020Article
- Chromatin architecture reorganization in murine somatic cell nuclear transfer embryos.Nature communications · 2020Article
- Toward understanding the dynamic state of 3D genome.Computational and structural biotechnology journal · 2020Review
- Cumulative contact frequency of a chromatin region is an intrinsic property linked to its function.PeerJ · 2020Article
- Hi-C analysis: from data generation to integration.Biophysical reviews · 2019Review
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chromosome conformation capture techniques, such as Hi-C, are fundamental in characterizing genome organization. These methods have revealed several genomic features, such as chromatin loops, whose disruption can have dramatic effects in gene regulation. Unfortunately, their detection is difficult; current methods require that the users choose the resolution of interaction maps based on dataset quality and sequencing depth. Here, we introduce Binless, a resolution-agnostic method that adapts to the quality and quantity of available data, to detect both interactions and differences. Binless relies on an alternate representation of Hi-C data, which leads to a more detailed classification of paired-end reads. Using a large-scale benchmark, we demonstrate that Binless is able to call interactions with higher reproducibility than other existing methods. Binless, which is freely available, can thus reliably be used to identify chromatin loops as well as for differential analysis of chromatin interaction maps.
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