Abstract 2018-2019: HTPathwaySeq: RShiny user interface prototype for data exploration of high-throughput pathway analysis
HTPathwaySeq is a novel application, developed by Biogazelle, for high-throughput RNA sequencing based molecular phenotyping. It allows one to establish compound activity in early drug discovery stages in a more cost-effective manner. The data output of the pipeline consists of enriched gene sets, for up to 95 contrasts with varying condition, in numerous text-based files. The objective during this traineeship was to develop an interactive and dynamic user interface with the help of the R package Shiny. Users should be able to explore the data generated by HTPathwaySeq and interact with data analyses, such as pathway activity, compound similarity and molecular toxicity. In short, with the application it’s possible to: (i) get a general overview of the results obtained from HTPathwaySeq. (ii) Explore the enriched gene sets for individual contrasts in addition with informative descriptions and gene details. (iii) Determine Compound toxicity by visualizing enriched gene sets with known cellular responses to toxic agents. (iv) Assess molecular similarity amongst compounds by clustering contrasts based on gene set enrichment correlation. Furthermore, the application is designed in such a way that it works dynamically for different experimental designs and with a lot of interactivity to improve exploration and visualization. To conclude, The HTPathwaySeq Rshiny user interface allows for improved data explorations in order to gain more insight and increase research outcome.
Abstract traineeship advanced bachelor of bioinformatics 2017-2018: RNA Seq compendium using R/Shiny
Biogazelle is a transcriptomics service provider with a large experience in RNA sequencing technologies. Besides sequencing projects for customers, the R&D unit has generated a large catalog of sequenced samples, covering a series of tissues, sample types, diseases, and many technical settings.
The purpose of the internship is to explore this internal collection of mRNA expression data to support our customers in finding an optimal experimental setting for their liquid biopsy experiment.
In the first phase, we collected sample information, gene annotation and gene expression data from projects executed at Biogazelle. Data from all projects were normalized and homogenized to generate large data matrices (in R). In the second phase, we built a simple (web) interface (using Shiny) to query these data matrices.
The web interface allows users to select a gene of interest and display the normalized gene expression values together with sample information. Furthermore, the application is capable of visualizing gene expression levels (R) between different datasets, across different tissue types, different sample types and in multiple diseases. It also supplies the user with sample by sample QC information to make data interpretation easier.
For the user this will offer them insight in the expression of their selected gene. They will be able to determine the best course of action for future experiments in terms of selection of samples (sample types/tissue types), the type of disease they want to study, the expression levels of the chosen gene and more.