Volume 10, Issue 4 (2019)                   JMBS 2019, 10(4): 617-625 | Back to browse issues page

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Sharifi Alishah M, Darvishzadeh R, Ahmadabadi M, Piri Kashtiban Y, Hasanpur K. Gene Expression Analysis Using RNA-Seq. JMBS 2019; 10 (4) :617-625
URL: http://biot.modares.ac.ir/article-22-21777-en.html
1- Plant Breeding & Biotechnology Department, Agriculture Faculty, Urmia University, Urmia, Iran
2- “Institute of Biotechnology” and “Plant Breeding & Biotechnology Department, Agriculture Faculty”, Urmia University, Urmia, Iran, Plant Breeding & Biotechnology Department, Agriculture Faculty, Urmia University, 11 Kilometer of Sero Road, Daneshgah Boulevard, Urmia, Iran. Postal Code: 5756151818 , r.darvishzadeh@urmia.ac.ir
3- Agricultural Biotechnology Department, Agriculture Faculty, Azarbaijan Shahid Madani University, Tabriz, Iran
4- Agricultural Biotechnology Department, National Center of Genetic Engineering, Tehran, Iran
5- Department of Animal Science, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
Abstract:   (6122 Views)
Revealing DNA sequences is vital for all branches of biological sciences. Next-Generation Sequencing (NGS) is a different approach in this area so that it has created a great evolution in biology science and covers various aspects of genome, transcriptome, epigenome and metagenome-level studies. NGS is considered as a high-performance method for genomic and transcriptomic information analysis in comparison with traditional methods due to providing good genomic coverage, determining each single pairs of bases and eliminating the first generation sequencing disadvantages (Sanger sequencing). Use of NGS has begun since 2005 and 2006, after the commercialization of various apparatus companies such as ABI/SOLiD Illumina, Science Roch/454Life, and Solexa to study the transcriptome of the model and non-model organisms. Recently, RNA sequencing is used widely to identify genes associated with growth and development processes and their expression patterns in response to a variety of biological and non-biological stresses, in various organs and growth stages in different organisms. It helps scientists to determine the amounts of gene expression, differentiation of different isoforms of genes, detection of gene fusions and characterization of small RNA as well as alternative splicing events, duplicate elements, exon of genes, new transcripts, UTRs, SNPs, and somatic mutations. The RNA-seq method typically consists of providing suitable biological samples, isolation of total RNA, enrichment of non-ribosomal RNAs, conversion of RNA to cDNA, construction of a fragment library, selecting size and adding linkers and sequencing on high-throughput sequencing platform, alignment, and assembly of the reads and downstream analysis.
Full-Text [PDF 1199 kb]   (6297 Downloads)    
Article Type: Systematic Review | Subject: Agricultural Biotechnology
Received: 2018/06/6 | Accepted: 2019/03/18 | Published: 2019/12/21

1. Berglund EC, Kiialainen A, Syvänen AC. Next-generation sequencing technologies and applications for human genetic history and forensics. Investig Genet. 2011;2:23. [Link] [DOI:10.1186/2041-2223-2-23]
2. Quail MA, Smith M, Coupland P, Otto TD, Harris SR, Connor TR, et al. A tale of three next generation sequencing platforms: Comparison of Ion Torrent, Pacific Biosciences and Illumina MiSeq sequencers. BMC Genom. 2012;13(1):341. [Link] [DOI:10.1186/1471-2164-13-341]
3. Xiong M, Zhao Z, Arnold J, Yu F. Next-generation sequencing. J Biomed Biotechnol. 2010;2010:370710. [Link] [DOI:10.1155/2010/370710]
4. Liu T, Zhu S, Tang Q, Chen P, Yu Y, Tang S. De novo assembly and characterization of transcriptome using Illumina paired-end sequencing and identification of CesA gene in ramie (Boehmeria nivea L. Gaud). BMC Genom. 2013;14(1):125. [Link] [DOI:10.1186/1471-2164-14-125]
5. Morozova O, Marra MA. Applications of next-generation sequencing technologies in functional genomics. Genomics. 2008;92(5):255-64. [Link] [DOI:10.1016/j.ygeno.2008.07.001]
6. Liu J, Zhou Y, Luo C, Xiang Y, An L. De novo transcriptome sequencing of desert herbaceous Achnatherum splendens (Achnatherum) seedlings and identification of salt tolerance genes. Genes. 2016;7(4):12. [Link] [DOI:10.3390/genes7040012]
7. Mahmoudi P, Moieni A, Khayam Nekoei SM, Mardi M, Hosseini Salekdeh GH. Analysis of saffron stigma (Crocus sativus L.) transcriptome using SOAPdenovo and Trinity assembly software. Crop Biotechnol. 2014;6:35-46. [Persian] [Link]
8. Mochida K, Shinozaki K. Advances in omics and bioinformatics tools for systems analyses of plant functions. Plant Cell Physiol. 2011;52(12):2017-38. [Link] [DOI:10.1093/pcp/pcr153]
9. Mansouri M, Naghavi MR, Alizadeh H, Mohammadinejad G, Mousavi SA, Hosseini Salekdeh G. Expression profiling of genes involved in signaling process in Aegilops tauschii under salinity stress. Iran J Filed Crop Sci. 2016;47(2):205-16. [Persian] [Link]
10. Hu L, Li H, Chen L, Lou Y, Amombo E, Fu J. RNA-seq for gene identification and transcript profiling in relation to root growth of bermudagrass (Cynodon dactylon) under salinity stress. BMC Genom. 2015;16(1):575. [Link] [DOI:10.1186/s12864-015-1799-3]
11. Dang ZH, Qi Q, Zhang HR, Li HY, Wu SB, Wang YC. Identification of salt-stress-induced genes from the RNA-Seq data of Reaumuria trigyna using differential-display reverse transcription PCR. Int J Genom. 2014;2014:381501. [Link] [DOI:10.1155/2014/381501]
12. Zhou Y, Yang P, Cui F, Zhang F, Luo X, Xie J. Transcriptome analysis of salt stress responsiveness in the seedlings of Dongxiang wild rice (Oryza rufipogon Griff.). PLoS One. 2016;11(1):e0146242. [Link] [DOI:10.1371/journal.pone.0146242]
13. Mardis ER. A decade's perspective on DNA sequencing technology. Nature. 2011;470(7333):198-203. [Link] [DOI:10.1038/nature09796]
14. Griffith M, Walker JR, Spies NC, Ainscough BJ, Griffith OL. Informatics for RNA sequencing: A web resource for analysis on the cloud. PLoS Comput Biol. 2015;11(8):e1004393. [Link] [DOI:10.1371/journal.pcbi.1004393]
15. Jazayeri SM, Melgarejo Muñoz LM, Romero HM. RNA-seq: A glance at technologies and methodologies. Acta Biológica Colombiana. 2015;20(2):23-35. [Link] [DOI:10.15446/abc.v20n2.43639]
16. Schliesky S, Gowik U, Weber AP, Bräutigam A. RNA-seq assembly-are we there yet?. Front Plant Sci. 2012;3:220. [Link] [DOI:10.3389/fpls.2012.00220]
17. Soneson C, Delorenzi M. A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinform. 2013;14(1):91. [Link] [DOI:10.1186/1471-2105-14-91]
18. Konczal M, Koteja P, Stuglik MT, Radwan J, Babik W. Accuracy of allele frequency estimation using pooled RNA‐Seq. Mol Ecol Resour. 2014;14(2):381-92. [Link] [DOI:10.1111/1755-0998.12186]
19. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5(7):621-8. [Link] [DOI:10.1038/nmeth.1226]
20. Sooknanan R, Pease J, Doyle K. Novel methods for rRNA removal and directional, ligation-free RNA-seq library preparation. Nat Methods. 2010;7(10):858. [Link] [DOI:10.1038/nmeth.f.313]
21. Shokralla S, Spall JL, Gibson JF, Hajibabaei M. Next‐generation sequencing technologies for environmental DNA research. Mol Ecol. 2012;21(8):1794-805. [Link] [DOI:10.1111/j.1365-294X.2012.05538.x]
22. Steinbock LJ, Radenovic A. The emergence of nanopores in next-generation sequencing. Nanotechnology. 2015;26(7):074003. [Link] [DOI:10.1088/0957-4484/26/7/074003]
23. Pease J, Sooknanan R. A rapid, directional RNA-seq library preparation workflow for Illumina® sequencing. Nat Methods. 2012;9:i-ii. [Link] [DOI:10.1038/nmeth.f.355]
24. Chikhi R, Medvedev P. Informed and automated k-Mer size selection for genome assembly. Bioinformatics. 2014;30(1):31-7. [Link] [DOI:10.1093/bioinformatics/btt310]
25. Ramirez-Gonzalez RH, Leggett RM, Waite D, Thanki A, Drou N, Caccamo M, et al. StatsDB: Platform-agnostic storage and understanding of next generation sequencing run metrics. F1000Res. 2013;2:248. https://doi.org/10.12688/f1000research.2-248.v1 [Link] [DOI:10.12688/f1000research.2-248.v2]
26. Haas BJ, Zody MC. Advancing RNA-seq analysis. Nat Biotechnol. 2010;28(5):421-3. [Link] [DOI:10.1038/nbt0510-421]
27. Fonseca NA, Rung J, Brazma A, Marioni JC. Tools for mapping high-throughput sequencing data. Bioinformatics. 2012;28(24):3169-77. [Link] [DOI:10.1093/bioinformatics/bts605]
28. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, Van Baren MJ, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010;28(5):511-5. [Link] [DOI:10.1038/nbt.1621]
29. Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010;11(3):R25. [Link] [DOI:10.1186/gb-2010-11-3-r25]
30. Bullard JH, Purdom E, Hansen KD, Dudoit S. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinform. 2010;11(1):94. [Link] [DOI:10.1186/1471-2105-11-94]
31. Al Seesi S, Tiagueu YT, Zelikovsky A, Măndoiu II. Bootstrap-based differential gene expression analysis for RNA-Seq data with and without replicates. BMC Genom. 2014;15(Suppl 8):S2. [Link] [DOI:10.1186/1471-2164-15-S8-S2]

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