Whole Transcriptome Sequencing: Brief Introduction, Workflow, Advantages and Applications

Introduction of Whole Transcriptome Sequencing

All RNA transcripts from an organism must be taken into account, regardless of whether they are coding or non-coding to get a true picture of transcriptomes. As a result, an organism's entire RNA must be isolated. Because ribosomal RNA (rRNA) makes up as much as 98 percent of the transcriptome, it is frequently depleted to increase the number of reads covering the RNA of interest. Whole transcriptome sequencing, also known as total RNA-Seq, is the most effective way to get a complete picture of your organism's transcriptomes.

Whole transcriptome analysis aims to determine gene expression heterogeneity in cells, tissues, organs, and even the entire body by capturing both coding and non-coding RNA. This research is also significant because it establishes the groundwork for functional characterization and annotation of genes/genomes previously identified through DNA sequencing, as well as blueprints for reconstructing genetic interaction networks to better understand cellular functions, growth/development, and biological systems. It also generates molecular fingerprints of disease processes and prognoses, allowing researchers to locate potential targets for drug discovery and diagnostics, as well as investigate the relationship between host and pathogen in search of novel therapeutic and prophylactic strategies. In tendons of older humans, for example, cellular function, growth, and cycling pathways are among the most essential gene networks attributing to age-related degeneration, while enhancement of activating and repressive histone modifications was the major sex-dimorphic signature.

Workflow of Whole Transcriptome Sequencing

In summary, RNA-Seq approaches follow a similar workflow. RNA is isolated first, then reverse-transcribed into cDNA. After fragmenting the cDNA, adapters are ligated to the fragments, which are then sequenced single-end or paired-end. The reads are then aligned against a reference transcriptome or assembled from scratch to reveal a detailed profile with single-base resolution.

Total RNA-Seq varies from other transcriptome sequencing methods in that it allows both coding and non-coding RNA to be sequenced. Rather than being subjected to polyA+ selection, which targets the mRNA species, the entire RNA is frequently subjected to rRNA depletion. Reduced rRNA helps allocate more sequencing reads to transcripts of interest because it makes up a large percentage of total RNA. The remaining rRNA-depleted RNA is used to make cDNA. Strand-specific information can be provided during downstream processing if desired. The cDNA is then treated in the same way as any other RNA-Seq sample.

Advantages and Applications of Whole Transcriptome Sequencing

Total RNA-Seq examines both coding and non-coding RNA to provide a comprehensive picture of the transcriptome. Some of the benefits of whole transcriptome sequencing are as follows:

• It captures both well-known and novel characteristics.
• Allows researchers to find biomarkers in the widest range of transcripts possible.
• Allows for a more thorough understanding of phenotypes of interest.
• Profiling of the entire transcriptome over a wide dynamic range is possible.

Furthermore, genome sequencing is a useful tool for researchers who want to:

• Obtain the most comprehensive transcriptome-wide view possible.
• Exon boundaries and the untranslated region (UTR) are used to map genes.
• Examine the mechanisms of splicing and alternative splicing.
• Investigate human and non-human transcriptomes origin

References:

1. Yang IS, Kim S. Analysis of whole transcriptome sequencing data: workflow and software. Genomics & informatics. 2015 Dec;13(4):119.
2. Jiang Z, Zhou X, Li R, et al. Whole transcriptome analysis with sequencing: methods, challenges and potential solutions. Cellular and molecular life sciences. 2015 Sep;72(18).
3. Cirulli ET, Singh A, Shianna KV, et al. Screening the human exome: a comparison of whole genome and whole transcriptome sequencing. Genome biology. 2010 May;11(5).