ArticleNature communications2020
Characterizing chromatin landscape from aggregate and single-cell genomic assays using flexible duration modeling.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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.
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Who cites it
8 citing papers in PubMed, 17 citations in OpenAlex.
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Integrated multimodal cell atlas of Alzheimer's disease.Nature neuroscience · 2024Article
- MOCHA's advanced statistical modeling of scATAC-seq data enables functional genomic inference in large human cohorts.Nature communications · 2024Article
- Integrated multimodal cell atlas of Alzheimer's disease.Research square · 2023Article
- Coordinated control of neuronal differentiation and wiring by sustained transcription factors.Science (New York, N.Y.) · 2022Article
- Nucleome programming is required for the foundation of totipotency in mammalian germline development.The EMBO journal · 2022Article
- New horizons in the stormy sea of multimodal single-cell data integration.Molecular cell · 2022Review
- ATAC-seq with unique molecular identifiers improves quantification and footprinting.Communications biology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
ATAC-seq has become a leading technology for probing the chromatin landscape of single and aggregated cells. Distilling functional regions from ATAC-seq presents diverse analysis challenges. Methods commonly used to analyze chromatin accessibility datasets are adapted from algorithms designed to process different experimental technologies, disregarding the statistical and biological differences intrinsic to the ATAC-seq technology. Here, we present a Bayesian statistical approach that uses latent space models to better model accessible regions, termed ChromA. ChromA annotates chromatin landscape by integrating information from replicates, producing a consensus de-noised annotation of chromatin accessibility. ChromA can analyze single cell ATAC-seq data, correcting many biases generated by the sparse sampling inherent in single cell technologies. We validate ChromA on multiple technologies and biological systems, including mouse and human immune cells, establishing ChromA as a top performing general platform for mapping the chromatin landscape in different cellular populations from diverse experimental designs.
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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.