核糖核酸
计算生物学
工作流程
标准化
破译
RNA序列
生物
移植
计算机科学
生物信息学
基因
遗传学
基因表达
转录组
医学
数据库
外科
操作系统
作者
Gaurav Thareja,Hemant Suryawanshi,Xunrong Luo,Thangamani Muthukumar
出处
期刊:Transplantation
[Ovid Technologies (Wolters Kluwer)]
日期:2023-04-06
卷期号:107 (10): 2155-2167
被引量:2
标识
DOI:10.1097/tp.0000000000004558
摘要
RNA-sequencing (RNA-seq) is a technique to determine the order of nucleotides in an RNA segment. Modern sequencing platforms simultaneously sequence millions of RNA molecules. Advances in bioinformatics have allowed us to collect, store, analyze, and disseminate data from RNA-seq experiments and decipher biological insights from large sequencing datasets. Although bulk RNA-seq has significantly advanced our understanding of tissue-specific gene expression and regulation, recent advances in single-cell RNA-seq have allowed such information to be mapped to individual cells, thus remarkably enhancing our insight into discrete cellular functions within a biospecimen. These different RNA-seq experimental approaches require specialized computational tools. Herein, we will first review the RNA-seq experimental workflow, discuss the common terminologies used in RNA-seq, and suggest approaches for standardization across multiple studies. Next, we will provide an up-to-date appraisal of the applications of bulk RNA-seq and single-cell/nucleus RNA-seq in preclinical and clinical research on kidney transplantation, as well as typical bioinformatic workflows utilized in such analysis. Lastly, we will deliberate on the limitations of this technology in transplantation research and briefly summarize newer technologies that could be combined with RNA-seq to permit more powerful dissections of biological functions. Because each step in RNA-seq workflow has numerous variations and could potentially impact the results, as conscientious citizens of the research community, we must strive to continuously modernize our analytical pipelines and exhaustively report their technical details.
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