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

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.

Keywords: ATAC-seq; ChIP-seq; RNA-seq; gene expression; multi-omics analysis.

Figure 1

Bibliometrics and datasets statistics of ATAC-seq. (A) The number of articles using combinations of ATAC-seq, ChIP-seq and RNA-seq published from 2013 to 2021 from PubMed database. Keywords of ATAC-seq, ChIP-seq and RNA-seq are searched, respectively, under the Title/Abstract term in the query box. (B) The names of top 50 journals published ATAC-seq articles. (C) The stacked bar plot shows the number of cancer studies versus other diseases using ATAC-seq and/or other sequencing techniques from 2015 to 2021.

Figure 2

Keywords correlation analysis and the changes of clusters in ATAC-seq articles. (A) Keyword correlation analysis. The colour of the words represents the average time of occurrence of each keyword in the literature corpus. The colour bar represents the normalized Z-score of average time. The size of the circle behind each word represents its frequency of occurrence. The three ellipses indicate three clusters of highly correlated keywords (abbreviation of TF). (B) The change of clusters over time. Keywords are divided into three clusters according to their relevance. The horizontal axis represents the average time normalized by Z-score. The vertical axis shows its proportion to all the keywords in each cluster.

Figure 3

Principles and workflow of ATAC-seq. (A) Principles of ATAC-seq, ChIP-seq and RNA-seq. (B) The generic ATAC-seq data analysis workflow.

Figure 4

Phylogenetic tree of 65 species to which ATAC-seq data are applied. The four colours correspond to four kingdoms: protozoa, animals, plants and fungi. The outer circle indicates the number of GEO experiments for each species.