Review of RNA Sequencing Methods

Kuo Fang; Lihua Ding; Ruijie Lin; Sike Yao; Yikun Liu; Yiyan Luo

doi:10.54691/ha45ef74

Authors

Kuo Fang
Lihua Ding
Ruijie Lin
Sike Yao
Yikun Liu
Yiyan Luo

DOI:

https://doi.org/10.54691/ha45ef74

Keywords:

RNA sequencing, transcriptome, long-read sequencing, differential expression, alternative splicing.

Abstract

Since its inception, RNA sequencing (RNA-seq) technology has revolutionized our ability to analyze gene expression, discover novel transcripts, and identify differential splicing events at the whole-transcriptome level. This review systematically outlines the development of RNA-seq technologies, focusing on comparing the principles, workflows, advantages and disadvantages, and application scenarios of three mainstream technical approaches: short-read cDNA sequencing, long-read cDNA sequencing, and long-read direct RNA sequencing. Short-read sequencing remains the gold standard for quantitative analysis due to its high accuracy and low cost; long-read cDNA sequencing (e.g., PacBio and Nanopore cDNA sequencing) can seamlessly span repetitive regions and homologous genes, enabling precise reconstruction of transcript isoforms; while the emerging long-read direct RNA sequencing (e.g., Nanopore) preserves native RNA modification information, opening new avenues for epitranscriptomics research. The article also reviews the latest applications of RNA-seq in disease research, developmental biology, and single-cell analysis, and discusses future directions, such as multi-omics integration, improvement of long-read quantification accuracy, and development of bioinformatics tools.

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References

[1] Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009;10(1):57-63.

[2] Schena M, Shalon D, Davis RW, Brown PO. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science. 1995;270(5235):467-470.

[3] Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5(7):621-628.

[4] Nagalakshmi U, Wang Z, Waern K, et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science. 2008;320(5881):1344-1349.

[5] Wilhelm BT, Marguerat S, Watt S, et al. Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution. Nature. 2008;453(7199):1239-1243.

[6] Conesa A, Madrigal P, Tarazona S, et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 2016;17:13.

[7] The Cancer Genome Atlas Research Network. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013;45(10):1113-1120.

[8] Kivioja T, Vähärautio A, Karlsson K, et al. Counting absolute numbers of molecules using unique molecular identifiers. Nat Methods. 2011;9(1):72-74.

[9] Rhoads A, Au KF. PacBio Sequencing and Its Applications. Genomics Proteomics Bioinformatics. 2015;13(5):278-289.

[10] Garalde DR, Snell EA, Jachimowicz D, et al. Highly parallel direct RNA sequencing on an array of nanopores. Nat Methods. 2018;15(3):201-206.

[11] Amarasinghe SL, Su S, Dong X, et al. Opportunities and challenges in long-read sequencing data analysis. Genome Biol. 2020;21(1):30.

[12] Liu H, Begik O, Lucas MC, et al. Accurate detection of m6A RNA modifications in native RNA sequences. Nat Commun. 2019;10(1):4079.

[13] Marx V. Method of the Year: spatially resolved transcriptomics. Nat Methods. 2021;18(1):9-14.

[14] Heitzer E, Haque IS, Roberts CES, Speicher MR. Current and future perspectives of liquid biopsies in genomics-driven oncology. Nat Rev Genet. 2019;20(2):71-88.

[15] Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18(1):83.