NET-Seq

RNA-Seq

RNA-Seq describes the abundance and sequence of RNA transcripts. The method, first published by several groups in 2008^1-5 has effectively displaced older methods such as serial analysis of gene expression (SAGE) and massively parallel signature sequencing (MPSS). With over 8000 references in PubMed, RNA-Seq is by far the most abundantly cited NGS method.⁶ All RNA-Seq methods are based on the use of a reverse transcriptase to convert the RNA to cDNA. Two basic variations use either random primers or oligo(dT) primers for this reaction. Oligo(dT) primers are highly 3' biased and mostly suitable for mRNA abundance (expression) analysis. Random primers also result in some bias, which can be reduced by fragmentation of the input RNA.⁷

Additional refinements, such as the use of Moloney murine leukemia virus reverse transcriptase (MMLV-RT) and template-switching oligonucleotides produce a higher yield of full-length transcripts for gene annotation and splice-variant detection. These methods include those based on switch mechanism at the 5' end of RNA templates (Smart), such as Smart-Seq and Smart-seq2 ; cap-analysis gene expression (CAGE), such as NanoCAGE and CAGEscan ; RNA-Seq from single nuclei (snRNA-Seq) ; and sequencing of fixed and recovered intact single-cell RNA (FRISCR).

Pros:

• Highly suitable for discovering novel exons, genes, and splice isoforms
• High dynamic range in expression analysis, compared to microarrays

Cons:

• Primer bias
• Reverse transcriptase may introduce sequencing errors

• 1. Wilhelm B. T., Marguerat S., Watt S., et al. Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution. Nature. 2008;453:1239-1243
• 2. Sultan M., Schulz M. H., Richard H., et al. A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science. 2008;321:956-960
• 3. Marioni J. C., Mason C. E., Mane S. M., Stephens M. and Gilad Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 2008;18:1509-1517
• 4. Mortazavi A., Williams B. A., McCue K., Schaeffer L. and Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5:621-628
• 5. Nagalakshmi U., Wang Z., Waern K., et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science. 2008;320:1344-1349
• 6. Based on a PubMed search for RNA-Seq on 11/11/2016
• 7. Mortazavi A., Williams B. A., McCue K., Schaeffer L. and Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5:621-628

1. Conesa A., Madrigal P., Tarazona S., Gomez-Cabrero D., Cervera A., et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 2016;17:13

1. Pareek C. S., Smoczynski R., Kadarmideen H. N., et al. Single Nucleotide Polymorphism Discovery in Bovine Pituitary Gland Using RNA-Seq Technology. PLoS One. 2016;11:e0161370
2. Bennett C. G., Riemondy K., Chapnick D. A., et al. Genome-wide analysis of Musashi-2 targets reveals novel functions in governing epithelial cell migration. Nucleic Acids Res. 2016;44:3788-3800
3. Suarez-Vega A., Gutierrez-Gil B., Klopp C., Tosser-Klopp G. and Arranz J. J. Comprehensive RNA-Seq profiling to evaluate lactating sheep mammary gland transcriptome. Sci Data. 2016;3:160051
4. Davila J. I., Fadra N. M., Wang X., et al. Impact of RNA degradation on fusion detection by RNA-seq. BMC Genomics. 2016;17:814
5. Chen M. J., Chen L. K., Lai Y. S., et al. Integrating RNA-seq and ChIP-seq data to characterize long non-coding RNAs in Drosophila melanogaster. BMC Genomics. 2016;17:220
6. Arnold W. K., Savage C. R., Brissette C. A., Seshu J., Livny J. and Stevenson B. RNA-Seq of Borrelia burgdorferi in Multiple Phases of Growth Reveals Insights into the Dynamics of Gene Expression, Transcriptome Architecture, and Noncoding RNAs. PLoS One. 2016;11:e0164165
7. Joshi R. K., Megha S., Rahman M. H., Basu U. and Kav N. N. A global study of transcriptome dynamics in canola (Brassica napus L.) responsive to Sclerotinia sclerotiorum infection using RNA-Seq. Gene. 2016;590:57-67
8. Seo M., Caetano-Anolles K., Rodriguez-Zas S., et al. Comprehensive identification of sexually dimorphic genes in diverse cattle tissues using RNA-seq. BMC Genomics. 2016;17:81
9. Lukoszek R., Feist P. and Ignatova Z. Insights into the adaptive response of Arabidopsis thaliana to prolonged thermal stress by ribosomal profiling and RNA-Seq. BMC Plant Biol. 2016;16:221
10. Zhang Q., Lai M. M., Lou Y. Y., Guo B. H., Wang H. Y. and Zheng X. Q. Transcriptome altered by latent human cytomegalovirus infection on THP-1 cells using RNA-seq. Gene. 2016;594:144-150
11. Choi S. Y., Park B., Choi I. G., et al. Transcriptome landscape of Synechococcus elongatus PCC 7942 for nitrogen starvation responses using RNA-seq. Sci Rep. 2016;6:30584
12. Zhang Z. X., Zhao S. N., Liu G. F., et al. Discovery of putative capsaicin biosynthetic genes by RNA-Seq and digital gene expression analysis of pepper. Sci Rep. 2016;6:34121
13. Weissbein U., Schachter M., Egli D. and Benvenisty N. Analysis of chromosomal aberrations and recombination by allelic bias in RNA-Seq. Nat Commun. 2016;7:12144
14. Chakraborty S., Britton M., Martinez-Garcia P. J. and Dandekar A. M. Deep RNA-Seq profile reveals biodiversity, plant-microbe interactions and a large family of NBS-LRR resistance genes in walnut (Juglans regia) tissues. AMB Express. 2016;6:12