期刊
CURRENT ISSUES IN MOLECULAR BIOLOGY
卷 45, 期 3, 页码 1860-1874出版社
MDPI
DOI: 10.3390/cimb45030120
关键词
bioinformatics; transcriptomic data analysis; RNA-seq; alternative splicing; nascent mRNA analysis; scRNA-seq
Advancements in RNA-sequencing technologies have resulted in the development of diverse experimental setups and a massive accumulation of data, leading to a high demand for data analysis tools. Computational scientists have developed numerous analysis pipelines, but the selection of the most appropriate one is often overlooked. The RNA-sequencing data analysis pipeline consists of three major components: data pre-processing, main analysis, and downstream analysis. This article provides an overview of the tools used in bulk RNA-seq and single-cell RNA-seq, with a focus on alternative splicing and active RNA synthesis analysis. Quality control is essential in data pre-processing, determining the need for adapter removal, trimming, and filtering. The processed data is then analyzed using various tools, including differential gene expression, alternative splicing, and assessment of active synthesis, which requires dedicated sample preparation.
Advances in RNA-sequencing technologies have led to the development of intriguing experimental setups, a massive accumulation of data, and high demand for tools to analyze it. To answer this demand, computational scientists have developed a myriad of data analysis pipelines, but it is less often considered what the most appropriate one is. The RNA-sequencing data analysis pipeline can be divided into three major parts: data pre-processing, followed by the main and downstream analyses. Here, we present an overview of the tools used in both the bulk RNA-seq and at the single-cell level, with a particular focus on alternative splicing and active RNA synthesis analysis. A crucial part of data pre-processing is quality control, which defines the necessity of the next steps; adapter removal, trimming, and filtering. After pre-processing, the data are finally analyzed using a variety of tools: differential gene expression, alternative splicing, and assessment of active synthesis, the latter requiring dedicated sample preparation. In brief, we describe the commonly used tools in the sample preparation and analysis of RNA-seq data.
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