Korean Journal of Clinical Laboratory Science (대한임상검사과학회지)

Korean Society for Clinical Laboratory Science (대한임상검사과학회)
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Big Data Analytics in RNA-sequencing

RNA 시퀀싱 기법으로 생성된 빅데이터 분석

  • Sung-Hun WOO (Department of Biomedical Laboratory Science, College of Software and Digital Healthcare Convergence, Yonsei University) ;
  • Byung Chul JUNG (Department of Nutritional Sciences and Toxicology, University of California)
  • 우성훈 (연세대학교 소프트웨어디지털헬스케어융합대학 임상병리학과) ;
  • 정병출 (캘리포니아대학교 버클리캠퍼스 영양과학 및 독성학과)
  • Received : 2023.10.31
  • Accepted : 2023.11.13
  • Published : 2023.12.31
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Abstract

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.

차세대 염기서열 분석이 개발되고 널리 사용됨에 따라 RNA-시퀀싱(RNA-sequencing, RNA-seq)이 글로벌 전사체 프로파일링을 검증하기 위한 도구의 첫번째 선택으로 급부상하게 되었다. RNA-seq의 상당한 발전으로 다양한 유형의 RNA-seq가 생물정보학(bioinformatics) 발전과 함께 진화했으나, 다양한 RNA-seq 기법 및 생물정보학에 대한 전반적인 이해 없이는 RNA-seq의 복잡한 데이터를 해석하여 생물학적 의미를 도출하기는 어렵다. 이와 관련하여 본 리뷰에서는 RNA-seq의 두 가지 주요 섹션을 논의하고 있다. 첫째, Standard RNA-seq과 주요하게 자주 사용되는 두 가지 RNA-seq variant method를 비교하였다. 이 비교는 어떤 RNA-seq 방법이 연구 목적에 가장 적절한지에 대한 시사점을 제공한다. 둘째, 가장 널리 사용되는 RNA-seq에서 생성된 데이터 분석; (1) 탐색적 자료 분석 및 (2) enriched pathway 분석에 대해 논의하였다. 데이터 세트의 전반적인 추세를 제공할 수 있는 주 성분 분석, Heatmap 및 Volcano plot과 같이 RNA-seq에 대해 가장 널리 사용되는 탐색적 자료 분석을 소개하였다. Enriched pathway 분석 섹션에서는 3가지 세대의 enriched pathway 분석에 대해 소개하고 각 세대가 어떤 식으로 RNA-seq 데이터 세트로부터 enriched pathway를 도출하는지를 소개하였다.

Keywords

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