Bibliometric review of ATAC-Seq and its application in gene expression.
Bibliometric review of ATAC-Seq and its application in gene expression.
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ATAC-Seq的文献计量学综述及其在基因表达中的应用
DOI：
10.1093/bib/bbac061
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发表时间：
2022-05-13
影响因子：
9.5
通讯作者：
中科院分区：
文献类型：
--
作者：
摘要随着高通量下一代测序技术的发展，可以在多个水平上描述基因的调控和表达。使用测序的转座酶可接近染色质的测定（ATAC-seq），其使用Tn 5转座酶来测序基因组的无蛋白质结合区域，可以与染色质免疫沉淀结合深度测序（ChIP-seq）和核糖核酸测序（RNA-seq）组合，以提供基因表达的详细描述。在这里，我们回顾了有关ATAC-seq的文献，并描述了ATAC-seq出版物的特点。然后简要介绍了RNA-seq、ChIP-seq和ATAC-seq的原理，重点介绍了这些技术的主要特点。我们从以前使用ATAC-seq研究的物种中建立了一个系统发育树。小家鼠和智人的研究约占ATAC-seq总数据的90%，而其他物种仍在积累数据的过程中。我们总结了人类疾病和其他物种的发现，说明了前沿发现和多组学数据分析在当前研究中的作用。此外，我们收集并比较了ATAC-seq分析管道，这使得缺乏编程技能的生物研究人员能够更好地分析和探索ATAC-seq数据。通过本文的综述，可以看出多组学分析和单细胞测序技术将成为未来研究的主流方法。
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.
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影响因子：
11.1
作者：
Birnbaum KD
通讯作者：
Birnbaum KD
影响因子：
4.4
作者：
Deschamps S;Crow JA;Chaidir N;Peterson-Burch B;Kumar S;Lin H;Zastrow-Hayes G;May GD
通讯作者：
May GD
影响因子：
9.2
作者：
Shrestha RK;Ding P;Jones JDG;MacLean D
通讯作者：
MacLean D
影响因子：
3.7
作者：
Divate M;Cheung E
通讯作者：
Cheung E
影响因子：
64.8
作者：
Gorkin, David U.;Barozzi, Iros;Ren, Bing
通讯作者：
Ren, Bing