Big Data Analytics in RNA-sequencing

• Published in: Korean Journal of Clinical Laboratory Science 2023, 55(4), , pp.235-243
• Main Authors: WOO, Sung-Hun, JUNG, Byung Chul
• Format: Article
• Language: English
• Subjects: big data; computational biology; rna; sequence analysis; 기타자연과학
• ISSN: 1738-3544; 2288-1662
• DOI: 10.15324/kjcls.2023.55.4.235

As next-generation sequencing has been developed and used widely, RNA-sequencing (RNA-seq) has rapidly emerged as the first choice of tools to validate global transcriptome profiling. With the significant advances in RNA-seq, various types of RNA-seq have evolved in conjunction with the progress in bioinformatic tools. On the other hand, it is difficult to interpret the complex data underlying the biological meaning without a general understanding of the types of RNA-seq and bioinformatic approaches. In this regard, this paper discusses the two main sections of RNA-seq. First, two major variants of RNA-seq are described and compared with the standard RNA-seq. This provides insights into which RNA-seq method is most appropriate for their research. Second, the most widely used RNA-seq data analyses are discussed: (1) exploratory data analysis and (2) pathway enrichment analysis. This paper introduces the most widely used exploratory data analysis for RNA-seq, such as principal component analysis, heatmap, and volcano plot, which can provide the overall trends in the dataset. The pathway enrichment analysis section introduces three generations of pathway enrichment analysis and how they generate enriched pathways with the RNA-seq dataset.