• Jyotika RajawatEmail author


Transcriptomics can be considered as Integromics, whereby combining data from various omic branches results in crisp information. Transcriptomics provides the most informative base to start a research work, and with advent of new high-throughput techniques, it has become very easy and fast to generate a pool of data and information. Transcriptome constitutes all transcripts present in a cell including mRNA, miRNA, noncoding RNAs, and small RNAs. Transcriptomics identifies the quantity of RNA and transcriptional structure and quantifies the differential expression levels of transcripts spatially and temporally during various developmental stages and under varying physiological conditions. It gives the information on diversity, noncoding RNAs, and the arrangement of transcriptional units in coding regions. Transcriptomic analysis began with a primitive technique called EST, i.e., expressed sequence tags, followed by another technique called SAGE, i.e., serial analysis of gene expression, based on Sanger sequencing. EST and SAGE were laborious and determined a small set of transcripts in a random fashion, yielding half information on transcriptome. The 1990s marks the revolutionary decade in transcriptomics with the introduction of technological innovation of contemporary technique called microarray. Microarray analyzes large mammalian transcriptome rapidly and has been useful in drug development and clinical research by analyzing thousands of genes from multiple samples. The major drawback of the technique is the analysis of only known sequences and hence cannot detect novel transcripts. The latest in transcriptome analysis is RNA-Seq based on deep sequencing technology which can record up to 109 transcripts. It identifies the gene and the temporal activity of genes in a genome. In situ RNA-Seq is an advanced form which gives an overview of an individual cell in a fixed tissue. RNA-Seq is thus an advanced technique providing detailed information of complex transcriptome. Further, the chapter discusses the advantages and limitations of transcriptome analysis tools.

Similarly, the information of the expressed genes from a microbial community is termed as metatranscriptomics. Metatranscriptomics provides functional profile of microbiome under varying physiological conditions. The data generated is useful in enrichment analysis and phylogenetic analysis of microbes. Several bioinformatic pipelines are now designed or are in process for analysis of metatranscriptome dataset. Metatranscriptomics will provide information on microbial flora in human beings which can be exploited for designing targeted therapy for microbial dysbiosis. The last section of the chapter discusses the application of transcriptomics particularly in diagnosis and profiling a disease. Another application includes identifying environment-responsive genes or pathways, host-pathogen interactions, and annotating gene functions.


EST SAGE Microarray RNA-Seq Gene annotation Cancer 


  1. Aguiar-Pulido, V., Huang, W., Suarez-Ulloa, V., Cickovski, T., Mathee, K., & Narasimhan, G. (2016). Metagenomics, metatranscriptomics, and metabolomics approaches for microbiome analysis. Evolutionary Bioinformatics, 12(S1), 5–16.Google Scholar
  2. Araujo, F. A., Barh, D., Silva, A., Guimaraes, L., & Ramos, R. T. J. (2018). GO FEAT: A rapid web-based functional annotation tool for genomic and transcriptomic data. Scientific Reports, 8, 1794.CrossRefGoogle Scholar
  3. Bikel, S., Valdez-Lara, A., Cornejo-Granados, F., Rico, K., Canizales-Quinteros, S., Soberón, X., Pozo-Yauner, L. D., & Ochoa-Leyva, A. (2015). Combining metagenomics, metatranscriptomics and viromics to explore novel microbial interactions: Towards a systems-level understanding of human microbiome. Computational and Structural Biotechnology Journal, 13, 390–401.CrossRefGoogle Scholar
  4. Byron, S. A., Van Keuren-Jensen, K. R., Engelthaler, D. M., Carpten, J. D., & Craig, D. W. (2016). Translating RNA sequencing into clinical diagnostics: Opportunities and challenges. Nature Reviews. Genetics, 17, 257–271.CrossRefGoogle Scholar
  5. Cai, Z., Wang, L., Song, X., Tagore, S., Li, X., Wang, H., et al. (2018). Adaptive transcriptome profiling of subterranean Zokor, Myospalax baileyi, to high- altitude stresses in Tibet. Scientific Reports, 8, 4671.CrossRefGoogle Scholar
  6. Czypionka, T., Krugman, T., Altmuller, J., Blaustein, L., Steinfartz, S., Templeton, A. R., & Nolte, A. W. (2015). Ecological transcriptomics – A non-lethal sampling approach for endangered fire salamanders. Methods in Ecology and Evolution, 6, 1417–1425.CrossRefGoogle Scholar
  7. Derome, N., Duchesne, P., & Bernatchez, L. (2006). Parallelism in gene transcription among sympatric lake whitefish ecotypes (Coregonus clupeaformis Mitchill). Molecular Ecology, 15, 1239–1250.CrossRefGoogle Scholar
  8. Dhanasekaran, S. M., Barrette, T. R., Ghosh, D., Shah, R., Varambally, S., Kurachi, K., et al. (2001). Delineation of prognostic biomarkers in prostate cancer. Nature, 412, 822–826.CrossRefGoogle Scholar
  9. Durmuş, S., Cakir, T., Ozgur, A., & Guthke, R. (2015). A review on computational systems biology of pathogen-host interactions. Frontiers in Microbiology, 6, 235.PubMedPubMedCentralGoogle Scholar
  10. Eng, C. H. L., Shah, S., Thomassie, J., & Cai, L. (2017). Profiling the transcriptome with RNA SPOTs. Nature Methods, 14(12), 1153–1155.CrossRefGoogle Scholar
  11. Garcia-Sanchez, S., Aubert, S., Iraqui, I., Janbon, G., Ghigo, J. M., & d’Enfert, C. (2004). Candida albicans biofilms: A developmental state associated with specific and stable gene expression patterns. Eukaryotic Cell, 3, 536–545.CrossRefGoogle Scholar
  12. Garg, R., Shankar, R., Thakkar, B., Kudapa, H., Krishnamurthy, L., Mantri, N., et al. (2016). Transcriptome analyses reveal genotype- and developmental stage-specific molecular responses to drought and salinity stresses in chickpea. Scientific Reports, 6, 19228.CrossRefGoogle Scholar
  13. Gilbert, J. A., & Hughes, M. (2011). Gene expression profiling: metatranscriptomics. Methods in Molecular Biology, 733, 195–205.CrossRefGoogle Scholar
  14. Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., et al. (1999). Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, 286, 531–537.CrossRefGoogle Scholar
  15. Govindarajan, R., Duraiyan, J., & Palanisamy, M. (2012). Microarray and its applications. Journal of Pharmacy and Bioallied Sciences, 4(6), 310–312.CrossRefGoogle Scholar
  16. Hobbs, M., Pavasovic, A., King, A. G., Prentis, P. J., Eldridge, M. D., Chen, Z., et al. (2014). A transcriptome resource for the koala (Phascolarctos cinereus): Insights into koala retrovirus transcription and sequence diversity. BMC Genomics, 15, 786.CrossRefGoogle Scholar
  17. Hoheisel, J. D. (2006). Microarray technology: Beyond transcript profiling and genotype analysis. Nature Reviews, 7, 200–210.PubMedGoogle Scholar
  18. Howe, G. T., Yu, J., Knaus, B., Cronn, R., Kolpak, S., Dolan, P., et al. (2013). A SNP resource for Douglas-fir: De novo transcriptome assembly and SNP detection and validation. BMC Genomics, 14, 137.CrossRefGoogle Scholar
  19. Hrdlickova, R., Toloue, M., & Tian, B. (2017). RNA-Seq methods for transcriptome analysis. Wiley Interdiscip Rev RNA, 8(1), e1364.CrossRefGoogle Scholar
  20. Jiang, Y., Xiong, X., Danska, J., & Parkinson, J. (2016). Metatranscriptomic analysis of diverse microbial communities reveals core metabolic pathways and microbiome specific functionality. Microbiome, 4, 2.CrossRefGoogle Scholar
  21. Kanter, I., & Kalisky, T. (2015). Single cell transcriptomics: Methods and applications. Frontiers in Oncology, 5, 53.CrossRefGoogle Scholar
  22. Li, B., & Dewey, C. N. (2011). Rsem: Accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics, 12(1), 323.CrossRefGoogle Scholar
  23. Lin, Z., Chen, M., Dong, X., Zheng, X., Huang, H., Xu, X., & Chen, J. (2017). Transcriptome profiling of Galaxea fascicularis and its endosymbiont Symbiodinium reveals chronic eutrophication tolerance pathways and metabolic mutualism between partners. Scientific Reports, 7, 42100.CrossRefGoogle Scholar
  24. Lowe, R., Shirley, N., Bleackley, M., Dolan, S., & Shafee, T. (2017). Transcriptomics technologies. Plos Computational Biology, 13, e1005457.CrossRefGoogle Scholar
  25. Macgregor, P. F., & Squire, J. A. (2002). Application of microarrays to the analysis of gene expression in cancer. Clinical Chemistry, 48(8), 1170–1177.PubMedGoogle Scholar
  26. McGrath, L. L., Vollmer, S. V., Kaluziak, S. T., & Ayers, J. (2016). De novo transcriptome assembly for the lobster Homarus americanus and characterization of differential gene expression across nervous system tissues. BMC Genomics, 17, 63.CrossRefGoogle Scholar
  27. Moncada, R., Chiodin, M., Devlin, J. C., Baron, M., Hajdu, C. H., Simeone, D., & Yanai, I. (2018 Jan 1). Building a tumor atlas: integrating single-cell RNA-Seq data with spatial transcriptomics in pancreatic ductal adenocarcinoma. bioRxiv, 254375.Google Scholar
  28. Moreno, J. C., Pauws, E., van Kampen, A. H., Jedlicková, M., de Vijlder, J. J., & Ris-Stalpers, C. (2001). Cloning of tissue-specific genes using SAGE and a novel computational substraction approach. Genomic, 75, 70–76.CrossRefGoogle Scholar
  29. Salem, M., Paneru, B., Al-Tobasei, R., Abdouni, F., Thorgaard, G. H., Rexroad, C. E., & Yao, J. (2015). Transcriptome assembly, gene annotation and tissue gene expression atlas of the rainbow trout. PLoS One, 10(3), e0121778.CrossRefGoogle Scholar
  30. Sun, M., Li, Y. T., Liu, Y., Lee, S. C., & Wang, L. (2015). Transcriptome assembly and expression profiling of molecular responses to cadmium toxicity in hepatopancreas of the freshwater crab Sinopotamon henanense. Scientific Reports, 6, 19405.CrossRefGoogle Scholar
  31. Velculescu, V. E., Zhang, L., Vogelstein, B., & Kinzler, K. W. (1995). Serial analysis of gene expression. Science, 270(5235), 484–487.CrossRefGoogle Scholar
  32. Wang, Z., Gerstein, M., & Snyder, M. (2009). RNA-Seq: A revolutionary tool for transcriptomics. Nature Reviews. Genetics, 10(1), 57–63.CrossRefGoogle Scholar
  33. Westermann, A. J., Gorski, S. A., & Vogel, J. (2012). Dual RNA-seq of pathogen and host. Nature Reviews. Microbiology, 10, 618–630.CrossRefGoogle Scholar
  34. Westreich, S. T., et al. (2016). SAMSA: A comprehensive metatranscriptome analysis pipeline. BMC Bioinformatics, 17(1), 399.CrossRefGoogle Scholar
  35. Westreich, S. T., Treiber, M. L., Mills, D. A., Korf, I., & Lemay, D. G. (2018). SAMSA2: A standalone metatranscriptome analysis pipeline. BMC Bioinformatics, 19(1), 175.Google Scholar
  36. Wolf, J. B. W. (2013). Principles of transcriptome analysis and gene expression quantification: An RNA-seq tutorial. Molecular Ecology Resources, 13, 559–572.CrossRefGoogle Scholar
  37. Yamamato, M., Wakatsuki, T., Hada, A., & Ryo, A. (2001). Use of serial analysis of gene expression (SAGE) technology. Journal of Immunological Methods, 250, 45–66.CrossRefGoogle Scholar
  38. Yee, J. C., Gerdtzen, Z. P., & Hu, W. S. (2008). Comparative transcriptome analysis to unveil genes affecting recombinant protein productivity in mammalian cells. Biotechnology and Bioengineering, 102, 246–263.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Molecular and Human genetics laboratory, Department of ZoologyUniversity of LucknowLucknowIndia

Personalised recommendations