Evidence map›Paper›PMID 35255493›Full record

ReviewBriefings in bioinformatics2022

Bibliometric review of ATAC-Seq and its application in gene expression.

Liheng Luo, Michael Gribskov, Sufang Wang

Open access · bronzeAbstract readReview
In one paragraph

Review in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
4.5field-weighted citation impact, top 4% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed, 56 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Review
  10. Article
  11. Review
  12. Article
  13. Review
  14. A review of Ribosome profiling and tools used in Ribo-seq data analysis.Computational and structural biotechnology journal · 2024
    Review
  15. Article
  16. Article
  17. Androgen signaling inhibitsZoological research · 2024
    Article
  18. Article
  19. Article
  20. Dissecting the Immune System through Gene Regulation.Advances in experimental medicine and biology · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 2 countries.

Liheng LuoSchool of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi, China, 710072.
Michael GribskovDepartment of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA.
Sufang WangSchool of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi, China, 710072.ORCID 0000-0002-7691-9895
Northwestern Polytechnical University · CNPurdue University West Lafayette · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With recent advances in high-throughput next-generation sequencing, it is possible to describe the regulation and expression of genes at multiple levels. An assay for transposase-accessible chromatin using sequencing (ATAC-seq), which uses Tn5 transposase to sequence protein-free binding regions of the genome, can be combined with chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) and ribonucleic acid sequencing (RNA-seq) to provide a detailed description of gene expression. Here, we reviewed the literature on ATAC-seq and described the characteristics of ATAC-seq publications. We then briefly introduced the principles of RNA-seq, ChIP-seq and ATAC-seq, focusing on the main features of the techniques. We built a phylogenetic tree from species that had been previously studied by using ATAC-seq. Studies of Mus musculus and Homo sapiens account for approximately 90% of the total ATAC-seq data, while other species are still in the process of accumulating data. We summarized the findings from human diseases and other species, illustrating the cutting-edge discoveries and the role of multi-omics data analysis in current research. Moreover, we collected and compared ATAC-seq analysis pipelines, which allowed biological researchers who lack programming skills to better analyze and explore ATAC-seq data. Through this review, it is clear that multi-omics analysis and single-cell sequencing technology will become the mainstream approach in future research.

Indexed as

Chromatin Immunoprecipitation SequencingHigh-Throughput Nucleotide SequencingAnimalsBibliometricsGene ExpressionHumansMicePhylogenyRNASequence Analysis, DNARNAATAC-seqChIP-seqgene expressionmulti-omics analysisRNA-seq

Identifiers

PMID35255493
PMCPMC9116206
OpenAlexW4210835001

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.