Purpose

Alternative splicing is an essential mechanism in eukaryotes (plants, animals and fungi), which has greatly increased the biodiversity of proteins encoded by the genome (our genetic instruction book). This mechanism allows a single gene in the genome to copy (transcribe) multiple messenger Ribonucleic Acids (mRNAs) to form detailed records (transcripts) by alternatively splicing out (removing) the non-coding regions of the gene (Figure 1). This process leads to different proteins (variants) that may have different functions and properties important for the cell’s biology. Ribonucleic Acid sequencing or RNA-seq (a technique to reveal the presence and quantity of RNA in a biological sample) has been widely used to study the abundance of such protein changes which can be caused by different stimuli such as drought, high salinity and heat stress.

Figure 1: Schema of alternative splicing: A gene includes coding sequences (exons) and non-coding sequences (introns). The gene expression (the process by which information from a gene is used in the synthesis of a functional gene) mechanism alternatively splices out (removes) the introns to synthesis (generate) different transcripts (detailed records), which are sub-sequences (derived by deleting some of the sections of the original sequence without changing the order) of the gene. This process (expression of transcripts) can be quantified by counting the number of fragmented sequences (reads) of RNA-seq that could be aligned to the corresponding sequences. The gene expression is estimated by the sum of all the transcript expression.

Many RNA-seq analysis programs are unable to handle complex experimental designs of multivariate studies (such as time-series and developmental-series data), are error prone, require dedicated bioinformaticians and currently take months to obtain the results of the analysis. Despite the importance of alternative splicing in regulation of gene expression and protein diversity at the transcript level, most RNA-seq analysis pipelines only focus on gene expression changes, thus ignoring an essential level of information relevant to the expression reprogramming in response to stimuli. RNA-seq, despite its potential for accurate and rapid expression analysis at the transcript level, is often a source of frustration for biologists due to lack of easy-to-use and accessible tools to analyse the data.

In response, we have created a 3D RNA-seq App that integrates state-of-the-art, highly rated differential analysis tools and adopts the best practice for RNA-seq analysis. It employs various user-controllable steps of data pre-processing to optimize the analysis and visualizes the intermediate and final results through graphics and tables (Figure 2). The App also generates a publication-quality report at the end of the analysis with all the methods and results embedded.

Figure 2: 3D RNA-seq App web interface

Through support from the SEFARI Gateway Responsive Opportunity Fund, we then developed a 3D RNA-seq workshop, which was designed and delivered to train participants in the effective use of the 3D RNA-seq App. By using participant’s own experiments (or publicly available datasets) the workshop could demonstrate application to their own work and empower researchers in being able to perform complex differential expression while producing experimental results in a fast, robust and reproducible way.