ReviewComputational and structural biotechnology journal2020
Single-cell ATAC sequencing analysis: From data preprocessing to hypothesis generation.
Review in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers, 1 of them a synthesis that pooled it.
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
91 citing papers in PubMed, 1 synthesis or guideline pooled it, 153 citations in OpenAlex.
- Pooled it
- Integrated single-cell multiomics reveals neutrophil-driven immune-metabolic reprogramming of Kupffer cells via thrombospondin-1/CD36 after surgical stress.JCI insight · 2026Article
- From epigenetic mark detection to rational design of epigenetic editing strategies.Molecular therapy. Advances · 2026Review
- Global StationaryOT: trajectory inference for aging time courses of single-cell snapshots.Bioinformatics (Oxford, England) · 2026Article
- Dissecting epigenetic heterogeneity in single-cell DNA methylomes with a unified framework.Nature communications · 2026Article
- Exploring specialized metabolic pathways in medicinal plants with single-cell and spatial omics.Acta pharmaceutica Sinica. B · 2026Review
- The Cell Ontology in the age of single-cell omics.Scientific data · 2026Article
- Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation.PLoS computational biology · 2026Article
- Illuminating cell states by a comprehensive and interpretable single cell foundation model.Nature communications · 2026Article
- Human Systems Immunology in the Omics Era: Challenges, Methods, and Emerging Directions.European journal of immunology · 2026Review
- Genome- and peak-informed two-stage framework for scATAC-seq cell type identification.Bioinformatics (Oxford, England) · 2026Article
- Transgelin defines pro-tumorigenic cancer-associated fibroblasts in pancreatic cancer.British journal of cancer · 2026Article
- A hitchhiker's guide to single-cell epigenomics: Methods and applications for cancer research.International journal of cancer · 2026Review
- Advances in scCUT&Tag and computational analysis for single-cell gene regulatory element mapping.Briefings in bioinformatics · 2026Review
- Enhancer Trajectories in Lineage Commitment: Regulatory Logic of States and Cooperation.Biomolecules · 2026Review
- Technological advances in axonal regeneration and structural plasticity: optogenetics, single-cell omics, and tissue clearing.Frontiers in cellular neuroscience · 2026Review
- Crosstalk between innate immune signaling pathways and integrated TLR, NLRP3 inflammasome, cGAS-STING, and NF-κB networks in sepsis.Frontiers in cell and developmental biology · 2026Review
- Article
- Recent advances in single-cell bioinformatics for inferring higher-order chromatin contact maps.BMB reports · 2025Review
- scDIFF: automatic cell type annotation using scATAC-seq data by incorporating bulk-level genomic and epigenomic information in a deep diffusive transformer.Briefings in bioinformatics · 2025Article
31 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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Most genetic variations associated with human complex traits are located in non-coding genomic regions. Therefore, understanding the genotype-to-phenotype axis requires a comprehensive catalog of functional non-coding genomic elements, most of which are involved in epigenetic regulation of gene expression. Genome-wide maps of open chromatin regions can facilitate functional analysis of cis- and trans-regulatory elements via their connections with trait-associated sequence variants. Currently, Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq) is considered the most accessible and cost-effective strategy for genome-wide profiling of chromatin accessibility. Single-cell ATAC-seq (scATAC-seq) technology has also been developed to study cell type-specific chromatin accessibility in tissue samples containing a heterogeneous cellular population. However, due to the intrinsic nature of scATAC-seq data, which are highly noisy and sparse, accurate extraction of biological signals and devising effective biological hypothesis are difficult. To overcome such limitations in scATAC-seq data analysis, new methods and software tools have been developed over the past few years. Nevertheless, there is no consensus for the best practice of scATAC-seq data analysis yet. In this review, we discuss scATAC-seq technology and data analysis methods, ranging from preprocessing to downstream analysis, along with an up-to-date list of published studies that involved the application of this method. We expect this review will provide a guideline for successful data generation and analysis methods using appropriate software tools and databases for the study of chromatin accessibility at single-cell resolution.
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What OpenQuestion holds
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.