Transcriptomics

Abstract

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.