RNA-Seq as an Effective Tool for Modern Transcriptomics, A Review-based Study

Authors

  • Mekibib Million Institute of Biotechnology Addis Ababa University Ethiopia
  • Tileye Feyissa Institute of Biotechnology Addis Ababa University Ethiopia

DOI:

https://doi.org/10.38211/joarps.2022.3.2.29

Abstract

Transcriptome analysis is a useful method for identification and understanding genes. Finding genes that are differentially expressed between conditions is a crucial aspect of transcriptomics. The discovery of RNA seq has been revolutionized next-generation sequencing technology. The fact that RNA sequencing does not requires gene probes and provides a precise measure of gene expression over a much wider range proved its credibility over other common techniques. The expressed gene profile and transcriptome data are stored in a database and could be accessed freely. During RNA seq short read mapping to the reference transcriptome (the set of all known transcript RNA sequences for a species) or genome in the database, a variety of database search tools and alignment methods become visible. There are a variety of applications that help align short reads generated by fragment sequencing. The study of expressed genes is aided by quantifying reads that align to the reference genome or transcriptome. RNA sequencing gives crucial information regarding alternative splicing and gene isoforms, in addition to differential gene expression.

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References

Alamancos, G. P., Agirre, E., & Eyras, E. (2012). Methods to study splicing from high-throughput RNA Sequencing data. Table 9, 1–31.

Archer, S. K., Shirokikh, N. E., & Preiss, T. (2015). Probe‐directed degradation (PDD) for flexible removal of unwanted cDNA sequences from RNA‐Seq libraries. Current Protocols in Human Genetics, 85(1), 11-15.Au, K. F., Jiang, H., Lin, L., Xing, Y., & Wong, W. H. (2010). Detection of splice junctions from paired-end RNA-seq data by SpliceMap. Nucleic Acids Research, 38(14), 4570–4578. DOI: https://doi.org/10.1093/nar/gkq211

Ballereau, S., Glaab, E., Kolodkin, A., Chaiboonchoe, A., Biryukov, M., Vlassis, N., ... & Auffray, C. (2013). Functional genomics, proteomics, metabolomics and bioinformatics for systems biology. In Systems Biology (pp. 3-41). Springer, Dordrecht. DOI: https://doi.org/10.1007/978-94-007-6803-1_1

Cseke, L. J., Wu, W., & Kaufman, P. B. (2003). DNA sequencing and analysis. Handbook of Molecular and Cellular Methods in Biology and Medicine, Second Edition, 2015(11), 237–270.

Dida, F., & Yi, G. (2021). Empirical evaluation of methods for de novo genome assembly. PeerJ Computer Science, 7, e636. DOI: https://doi.org/10.7717/peerj-cs.636

Fakrudin, B., Tuberosa, R., & Varshney, R. K. (2012). Omics techniques in crop research: An overview. DOI: https://doi.org/10.1002/9783527632930.ch12

Gong, A. D., Lian, S. B., Wu, N. N., Zhou, Y. J., Zhao, S. Q., Zhang, L. M., ... & Yuan, H. Y. (2020). Integrated transcriptomics and metabolomics analysis of catechins, caffeine and theanine biosynthesis in tea plant (Camellia sinensis) over the course of seasons. BMC plant biology, 20(1), 1-14. DOI: https://doi.org/10.1186/s12870-020-02443-y

Hrdlickova, R., Toloue, M., & Tian, B. (2017). RNA‐Seq methods for transcriptome analysis. Wiley Interdisciplinary Reviews: RNA, 8(1), e1364. DOI: https://doi.org/10.1002/wrna.1364

Kchouk, M., Gibrat, J. F., & Elloumi, M. (2017). Generations of sequencing technologies: from first to next generation. Biology and Medicine, 9(3). DOI: https://doi.org/10.4172/0974-8369.1000395

Lowe, R., Shirley, N., Bleackley, M., Dolan, S., & Shafee, T. (2017). Transcriptomics technologies. PLoS computational biology, 13(5), e1005457. DOI: https://doi.org/10.1371/journal.pcbi.1005457

Mortazavi, A., Williams, B. A., & McCue, K. Schaffe er, L., and Wold, B.(2008). Mapping and quantifying mammalian transcriptomes by rna-seq. Nature methods, 5(7), 621628. DOI: https://doi.org/10.1038/nmeth.1226

Nagalakshmi, U., Waern, K., & Snyder, M. (2010). RNA‐Seq: a method for comprehensive transcriptome analysis. Current protocols in molecular biology, 89(1), 4-11. DOI: https://doi.org/10.1002/0471142727.mb0411s89

Orton, R. J., Gu, Q., Hughes, J., Maabar, M., Modha, S., Vattipally, S., & Davison, A. (2016). Bioinformatics tools for analysing viral genomic data. Revue scientifique et technique (International Office of Epizootics), 35(1), 241-285. DOI: https://doi.org/10.20506/rst.35.1.2432

Ozsolak, F., & Milos, P. M. (2011). RNA sequencing: advances, challenges and opportunities. Nature reviews genetics, 12(2), 87-98. DOI: https://doi.org/10.1038/nrg2934

Pandit, A. A., Shah, R. A., & Husaini, A. M. (2018). Transcriptomics: A time-efficient tool with wide applications in crop and animal biotechnology. J Pharmac Phytochem, 7, 1701-1704.

Rani, B., & Sharma, V. K. (2017). Transcriptome profiling: methods and applications-A review. Agricultural Reviews, 38(4). 271-281 DOI: https://doi.org/10.18805/ag.R-1549

Smid, M., Coebergh van den Braak, R. R., van de Werken, H. J., van Riet, J., van Galen, A., de Weerd, V., & Sieuwerts, A. M. (2018). Gene length corrected trimmed mean of M-values (GeTMM) processing of RNA-seq data performs similarly in intersample analyses while improving intrasample comparisons. BMC bioinformatics, 19(1), 1-13. DOI: https://doi.org/10.1186/s12859-018-2246-7

Stahl, F., Hitzmann, B., Mutz, K., Landgrebe, D., Lübbecke, M., Kasper, C., & Scheper, T. (2011). Transcriptome analysis. Genomics and Systems Biology of Mammalian Cell Culture, 1-25. DOI: https://doi.org/10.1007/10_2011_102

Teresa, A., & Gon, F. (2012). RNA sequencing for the study of gene expression regulation. September.

Trapnell, C., Pachter, L., & Salzberg, S. L. (2009). TopHat: discovering splice junctions with RNA-Seq. Bioinformatics, 25(9), 1105-1111. DOI: https://doi.org/10.1093/bioinformatics/btp120

Wang, L., Li, P., & Brutnell, T. P. (2010). Exploring plant transcriptomes using ultra high-throughput sequencing. Briefings in functional genomics, 9(2), 118-128. DOI: https://doi.org/10.1093/bfgp/elp057

Wang, Z., Gerstein, M., & Snyder, M. (2009). RNA-Seq: a revolutionary tool for transcriptomics. Nature reviews genetics, 10(1), 57-63. DOI: https://doi.org/10.1038/nrg2484

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Published

30-07-2022

How to Cite

RNA-Seq as an Effective Tool for Modern Transcriptomics, A Review-based Study (M. Million & T. Feyissa, Trans.). (2022). Journal of Applied Research in Plant Sciences, 3(02), 236-241. https://doi.org/10.38211/joarps.2022.3.2.29