**5. Future perspective**

RNASeq technology is proving to be a valuable tool to study known and novel transcripts of an organism by providing more insights into the role of gene expression in development, differential expression between different conditions, changes in gene expression in disease progression, alternative splicing events, RNA editing, fusion transcripts, allele-specific expression, etc. This technology is revolutionizing the field of plant and animal transcriptome, where many of the species lack reference genome because of genome size and complexity. Metatranscriptomic-NGS technology employed to study microbial transcriptome is another emerging area of research in which construction of transcriptome assembly has led to simul‐ taneous identification of thousands of transcripts from the microbial community of the human gastrointestinal tract [156], and the marine [157, 158] and soil [159]. Because of the fact that gene expression levels vary significantly from one cell to another, researchers are now moving toward single-cell transcriptomics, in which cell-to-cell variability on a genome-wide scale can be profiled. Hence, transcriptome of single cell can be probed more efficiently as compared to cell population where average transcript abundance of population is seen [160, 161]. A recent study by Sasagawa et al. developed the method Quartz-Seq for individual cell isolation followed by RNA sequencing and distinguished mouse embryonic stem cells from primitive endoderm based upon transcriptome profile as well as cell-to-cell stochastic variation [162]. Another recently developed method, RaceID, is very useful in identifying rare cell types in healthy and diseased tissues using mRNA sequencing [163]. Tissue-specific RNASeq is another emerging area of research that can reveal tissue-specific requirement of RNA expression. A recent study done on 13 different cell types discovered many tissue-specific and novel miRNAs, which suggests that the repertoire of human miRNA is more extensive than our current knowledge [164]. RNASeq is used as a powerful tool for clinical application as well. A recent study developed exome capture RNASeq protocol for degraded clinical formalin-fixed samples, which has shown to work successfully on prostate cancer samples suggesting that capture transcriptome study can be used beyond cell lines and in the clinical setting [165].

Moreover, there are several publicly available RNASeq data repositories such as ENCODE (https://www.**encode**project.org/), TCGA (www.cancergenome.nih.gov), and The Geuvadis Project (http://www.geuvadis.org/), which provide enormous amount of data to researchers to conduct genome-wide analyses beyond traditional gene expression and profiling analysis. Mining data from public repositories will provide new insights into the transcriptome and hence enable researchers to gain more information on gene regulation, which has been previously neglected.

Sequencing method and experimental protocols are also continuously improving to reduce the challenges associated with the technology. Platforms such as PacBio can produce a fulllength transcript in a single read, which can eventually eliminate the transcript assembly step of the data analysis.

Additionally, to cater to the high volume of data and the demand for high-end computational resources for the transcriptome assembly, many assemblers have started supporting parallel data processing, which has significantly reduced the time required for the assembly (reviewed in [66]). Cloud computing is another lucrative approach for parallel computing, which is scalable and can be used as per the user requirement [166].
