ReviewGenomics, proteomics & bioinformatics2025
Computational Analyses and Challenges of Single-cell ATAC-seq.
Review in Genomics, proteomics & bioinformatics, 2025. 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
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
8 citing papers in PubMed.
- Topology-guided dual-masked contrastive learning for weakly supervised cell-type annotation in single-cell multi-omics.Briefings in bioinformatics · 2026Article
- Coala: a standard-based framework for converting CWL-described command-line tools into agentic toolsets.Bioinformatics (Oxford, England) · 2026Article
- [Advances in single cell omics applications in mantle cell lymphoma].Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi · 2026Review
- Nuclear-specific reductive carboxylation of alpha-ketoglutarate fuels histone acetylation to induce chromatin accessibility and gene activation.Nature communications · 2026Article
- STED: flexible cross-modal topic modeling infers cell-type-specific regulatory landscapes from bulk epigenomics.Briefings in bioinformatics · 2026Article
- Deep learning-driven prediction of on-target activity, off-target risk, and repair outcomes in CRISPR/Cas9: current landscape and multi-scale perspectives.Journal of translational medicine · 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
- Understanding the immune microenvironment of ovarian cancer.Frontiers in oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) has emerged as a powerful technique to study cell-specific epigenetic landscapes and to provide a multidimensional portrait of gene regulation. However, low genomic coverage per cell results in intrinsic data sparsity and missing-data issues, presenting unique methodological challenges. Consequently, numerous computational methods and techniques have been developed to address these challenges. This review provides a concise overview of published workflows for scATAC-seq analysis, covering preprocessing through downstream analysis including quality control, alignment, peak calling, dimensionality reduction, clustering, gene regulation score calculation, cell type annotation, and multiomics integration. Additionally, we survey key scATAC-seq databases that offer curated, accessible resources; discuss emerging deep-learning methods and Artificial Intelligence (AI) foundation models tailored to scATAC-seq data; and highlight recent advances in spatial ATAC-seq technologies and associated computational approaches. Our objective is to equip readers with a clear understanding of current scATAC-seq methodologies so they can select appropriate tools and construct customized workflows for exploring gene regulation and cellular diversity.
Indexed as
Identifiers
What Socratic 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 Socratic graph.