ReviewaBIOTECH2022
Fundamental and practical approaches for single-cell ATAC-seq analysis.
Review in aBIOTECH, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 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
9 citing papers in PubMed, 12 citations in OpenAlex.
- The Application of Single-cell RNA Sequencing Technology in the Research of Sino-Nasal Diseases.Clinical reviews in allergy & immunology · 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
- 3D Genome Architecture in Stem Cell Lineage Commitment: from Structural Organization to Precision Regulation.Advanced genetics (Hoboken, N.J.) · 2025Review
- The Application of Single-Cell Technologies for Vaccine Development Against Viral Infections.Vaccines · 2025Review
- Towards Precision Aging Biology: Single-Cell Multi-Omics and Advanced AI-Driven Strategies.Aging and disease · 2025Review
- Single-Cell Sequencing Redefines Immune Heterogeneity and Communication Networks in ARDS: Toward Precision Therapeutics.International journal of genomics · 2025Review
- MOCHA's advanced statistical modeling of scATAC-seq data enables functional genomic inference in large human cohorts.Nature communications · 2024Article
- A review of single-cell transcriptomics and epigenomics studies in maternal and child health.Epigenomics · 2024Review
- Epigenetics of Early Cardiometabolic Disease: Mechanisms and Precision Medicine.Circulation research · 2023Review
Corrections and comments
- Erratum issued
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Assays for transposase-accessible chromatin through high-throughput sequencing (ATAC-seq) are effective tools in the study of genome-wide chromatin accessibility landscapes. With the rapid development of single-cell technology, open chromatin regions that play essential roles in epigenetic regulation have been measured at the single-cell level using single-cell ATAC-seq approaches. The application of scATAC-seq has become as popular as that of scRNA-seq. However, owing to the nature of scATAC-seq data, which are sparse and noisy, processing the data requires different methodologies and empirical experience. This review presents a practical guide for processing scATAC-seq data, from quality evaluation to downstream analysis, for various applications. In addition to the epigenomic profiling from scATAC-seq, we also discuss recent studies in which the function of non-coding variants has been investigated based on cell type-specific cis-regulatory elements and how to use the by-product genetic information obtained from scATAC-seq to infer single-cell copy number variants and trace cell lineage. We anticipate that this review will assist researchers in designing and implementing scATAC-seq assays to facilitate research in diverse fields.
Indexed as
Identifiers
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.