Big Data Analytics in RNA-sequencing
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초록
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.
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.
참고문헌 (44)
- Jung BC, "Epigenetic regulation of inflammatory factors in adipose tissue", Biochim Biophys Acta Mol Cell Biol Lipids, 1866, pp.159019~ (2021)[DOI]
- Subramanian A, "Gene set enrichment analysis : a knowledgebased approach for interpreting genome-wide expression profiles", Proc Natl Acad Sci U S A, 102, pp.15545~15550 (2005)[DOI]
- Schena M, "Quantitative monitoring of gene expression patterns with a complementary DNA microarray", Science, 270, pp.467~470 (1995)[DOI]
- Mutz KO, "Transcriptome analysis using next-generation sequencing", Curr Opin Biotechnol, 24, pp.22~30 (2013)[DOI]
- Zhao S, "Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells", PLoS One, 9, pp.e78644~ (2014)[DOI]
- Stark R, "RNA sequencing : the teenage years", Nat Rev Genet, 20, pp.631~656 (2019)[DOI]
- Wang Z, "RNA-Seq : a revolutionary tool for transcriptomics", Nat Rev Genet, 10, pp.57~63 (2009)[DOI]
- Sigurgeirsson B, "Sequencing degraded RNA addressed by 3’ tag counting", PLoS One, 9, pp.e91851~ (2014)[DOI]
- Jung BC, "TET3 plays a critical role in white adipose development and diet-induced remodeling", Cell Rep, 42, pp.113196~ (2023)[DOI]
- Weng X, "A high-throughput 3’-Tag RNA sequencing for large-scale time-series transcriptome studies", Methods Mol Biol, 2398, pp.151~172 (2022)[DOI]
- Wu X, "Widespread influence of 3’-end structures on mammalian mRNA processing and stability", Cell, 169, pp.905~917 (2017)[DOI]
- Raghavan V, "A simple guide to de novo transcriptome assembly and annotation", Brief Bioinform, 23, pp.bbab563~ (2022)[DOI]
- Liao X, "Current challenges and solutions of de novo assembly", Quant Biol, 7, pp.90~109 (2019)[DOI]
- Teo YY, "Exploratory data analysis in large-scale genetic studies", Biostatistics, 11, pp.70~81 (2010)[DOI]
- Koch CM, "A beginner’s guide to analysis of RNA sequencing data", Am J Respir Cell Mol Biol, 59, pp.145~157 (2018)[DOI]
- Chen X, "Robust principal component analysis for accurate outlier sample detection in RNA-Seq data", BMC Bioinformatics, 21, pp.269~ (2020)[DOI]
- Ringnér M, "What is principal component analysis?", Nat Biotechnol, 26, pp.303~304 (2008)[DOI]
- Jolliffe IT, "Principal component analysis : a review and recent developments", Philos Trans A Math Phys Eng Sci, 374, pp.20150202~ (2016)[DOI]
- Khomtchouk BB, "HeatmapGenerator : high performance RNAseq and microarray visualization software suite to examine differential gene expression levels using an R and C++ hybrid computational pipeline", Source Code Biol Med, 9, pp.30~ (2014)[DOI]
- Engle S, "Unboxing cluster heatmaps", BMC Bioinformatics, 18(Suppl 2), pp.63~ (2017)[DOI]
- Gu Z, "Complex heatmap visualization", iMeta, 1, pp.e43~ (2022)[DOI]
- El Bouchefry K, "Knowledge discovery in big data from astronomy and earth observation:AstroGeoInformatics", pp.225~249 (2020), Elsevier
- Gu Z, "Complex heatmaps reveal patterns and correlations in multidimensional genomic data", Bioinformatics, 32, pp.2847~2849 (2016)[DOI]
- Li W, "Volcano plots in analyzing differential expressions with mRNA microarrays", J Bioinform Comput Biol, 10, pp.1231003~ (2012)[DOI]
- Ebrahimpoor M, "Inflated false discovery rate due to volcano plots : problem and solutions", Brief Bioinform, 22, pp.bbab053~ (2021)[DOI]
- "reneshbedre/bioinfokit: bioinformatics data analysis and visualization toolkit"[URL]
- Leek JT, "Tackling the widespread and critical impact of batch effects in high-throughput data", Nat Rev Genet, 11, pp.733~739 (2010)[DOI]
- Reimand J, "Pathway enrichment analysis and visualization of omics data using g : Profiler, GSEA, Cytoscape and EnrichmentMap", Nat Protoc, 14, pp.482~517 (2019)[DOI]
- García-Campos MA, "Pathway analysis : state of the art", Front Physiol, 6, pp.383~ (2015)[DOI]
- Khatri P, "Ten years of pathway analysis : current approaches and outstanding challenges", PLoS Comput Biol, 8, pp.e1002375~ (2012)[DOI]
- Jung SH, "Stratified Fisher’s exact test and its sample size calculation", Biom J, 56, pp.129~140 (2014)[DOI]
- Dudoit S, "Multiple hypothesis testing in microarray experiments", Stat Sci, 18, pp.71~103 (2003)[DOI]
- Camargo A, "Permutation-based statistical tests for multiple hypotheses", Source Code Biol Med, 3, pp.15~ (2008)[DOI]
- Hochberg Y, "More powerful procedures for multiple significance testing", Stat Med, 9, pp.811~818 (1990)[DOI]
- Xie C, "Popularity and performance of bioinformatics software : the case of gene set analysis", BMC Bioinformatics, 22, pp.191~ (2021)[DOI]
- Fang Z, "GSEApy : a comprehensive package for performing gene set enrichment analysis in Python", Bioinformatics, 39, pp.btac757~ (2023)[DOI]
- Tamayo P, "The limitations of simple gene set enrichment analysis assuming gene independence", Stat Methods Med Res, 25, pp.472~487 (2016)[DOI]
- Wang Y, "Comparing Bayesian-based reconstruction strategies in topology-based pathway enrichment analysis", Biomolecules, 12, pp.906~ (2022)[DOI]
- Tarca AL, "A novel signaling pathway impact analysis", Bioinformatics, 25, pp.75~82 (2009)[DOI]
- Grassi M, "SEMgsa : topology-based pathway enrichment analysis with structural equation models", BMC Bioinformatics, 23, pp.344~ (2022)[DOI]
- Ma J, "A comparative study of topology-based pathway enrichment analysis methods", BMC Bioinformatics, 20, pp.546~ (2019)[DOI]
- Ibrahim MA, "A topology-based score for pathway enrichment", J Comput Biol, 19, pp.563~573 (2012)[DOI]
- Zhao K, "Interpreting omics data with pathway enrichment analysis", Trends Genet, 39, pp.308~319 (2023)[DOI]
- Nguyen TM, "Identifying significantly impacted pathways : a comprehensive review and assessment", Genome Biol, 20, pp.203~ (2019)[DOI]