Advancements in next-generation sequencing have significantly improved transcriptome analysis, utilizing various techniques like RNA-Seq to profile gene expression and discover non-coding RNA. Despite limitations of older methods such as microarrays and real-time PCR, RNA-Seq provides a robust framework for analyzing large datasets, with applications in understanding cellular differentiation, embryonic development, and drug safety. New techniques like single-cell RNA-Seq enable detailed gene expression studies, further expanding the field's utility in research and biomarker discovery.

1. Transcriptome Analysis & Applications
Recent advancement in next generation sequencing strategies has lead us to great advancement of
transcriptome studies. Various techniques for transcriptome analysis are available and widely used
since development of microarray technology and sequencing of the human genome.
mRNA level measurements using microarray techniques or real-time PCR techniques became
quickly popular, however, both these techniques had their limitations:
 Microarray approach had sensitivity issue and is vulnerable to errors such as
contaminations which hampers downstream analysis progress and result accuracy.
 Real-time PCR techniques are extremely expensive and high risk of contamination.
High throughput next generation sequencing and robust transcriptome analysis help with gene
expression profiling, gene annotation or discovery of non-coding RNA. Illumina short reads are
popular for gene expression and transcriptome assembly studies while long reads from Pacbio and
Oxford Nanopore have been used to study gene structure and isoform analysis.
RNA Seq or transcriptome analysis comprises of four major steps:
1. Experimental design (Number of replicates)
2. RNA seq library preparation to convert mRNA into cDNA library
3. Sequencing cDNA library
4. Bioinformatic RNA seq data analysis

2. Standard workflow of a RNA-seq based transcriptome analysis.
Int. J. Mol. Sci. 2018, 19(1), 245; https://doi.org/10.3390/ijms19010245
Transcriptome Analysis Workflow
RNA-Seq techniques generate hundreds of millions of short RNA reads using next-generation
sequencing (NGS). These RNA reads can be mapped to reference genomes to investigate changes
of gene expression but improved procedures for mining large RNA-Seq datasets to extract valuable
biological knowledge are needed.
Analysis phase can be divided into 4 phases:
Phase 1: Raw Data QC
Phase 2: Alignment (or de Novo assembly if no reference genome available)
Phase 3: Post Alignment QC
Phase 4: Differential Expression determination
Phase 5: Annotation/ Pathway discovery

3. Transcriptome analysis (RNA Seq Pipeline) workflow for processing raw reads for various
applications.
Int. J. Mol. Sci. 2018, 19(1), 245; https://doi.org/10.3390/ijms19010245
Transcriptome Analysis Applications:
 Transcriptomes of stem cells and cancer cells are of particular interest to researchers
who seek to understand the processes of cellular differentiation and carcinogenesis.
 Transcriptome analysis of human oocytes and embryos is used to understand the
molecular mechanisms and signaling pathways controlling early embryonic
development, and could theoretically be a powerful tool in making proper embryo
selection in in vitro fertilisation.
 Transcriptomics is an emerging and continually growing field in biomarker discovery for
use in assessing the safety of drugs or chemical risk assessment.
 Transcriptome analysis may also be used to infer phylogenetic relationships among
individuals.
 In recent years, single cell RNA seq techniques like 10x Genomics and BD Rhapsody
have enabled transcriptome analysis in a single cell. A number of cancer studies are
now focusing on mapping gene expression changes in tumor cells against normal tissue.
 Gene Ontology (GO) and Pathyway Analysis are tertiary transcriptome analysis
techniques that provide a system biology angel for genes regulation across tissues or
conditions.
 De Novo assembly of RNA seq reads can be performed to generate a reference
transcriptome for species that have too large and complex genomes.
Source: https://www.1010genome.com/transcriptome-analysis-applications/