Welcome to Stellenbosch University

​Division of Molecular ​​Biology and Human Genetics

​​South African Tuberculosis Bioinformatics Initiative


Ashley Ehlers

​​Ph.D. candidate Bioinformatics and Computational Biology

Ashley's PhD project focuses on building and grokking a machine learning framework for predictive modelling of diseases using large omics data sets. The analysis of omics data remains complex and fraught with potential for misinterpretation. Ashley is particularly interested in the design of best practices for an end-to-end optimal performing and updated machine learning framework for suitable biosignature identification using gene expression profiling and proteomics data sets. These biosignatures are not only useful in identifying the gene or protein sets for predictive modelling but can also shed light on the biological processes involved which improves our understanding of the domain.

Kimberly Coetzer

​​Ph.D. candidate Bioinformatics and Computational Biology

Kimberly is currently in her first year of PhD under the supervision of Professor Gerard Tromp and Professor Gian Van der Spuy. Kimberly is interested in integrative research involving multi-omics data and how the findings might be used to better understand genetic disorders. Her research will focus on the proteogenomics of genetic disorders, specifically amyotrophic lateral sclerosis (ALS). The aim of this PhD is to develop a containerized tool for reproducible proteogenomic data analysis using WGS, RNA-Seq and proteomics data. The results will be used to gain biological insight into ALS by analyzing disease mechanisms and gene functions. Kimberly plans to use the skills gained through her PhD to pursue a career as a full-time bioinformatician.

Jesse Asimeng​

​​Ph.D. candidate Molecular Biology

Jesse is currently working on elucidating the biological contribution of five isolated cell populations (T helper cells, cytotoxic T cells, monocytes, neutrophils and B cells) in tuberculosis treatment response using RNA-sequencing transcriptomics data from peripheral blood of tuberculosis patients. For his MSc project, Jesse worked on using current best practices and approaches such as development of a computational pipeline, container environments, unit testing as well as the development of an R package to enhance the reproducible and robust processing and analysis of multiplexed ELISA (Luminex) data. Jesse is looking forward to using the skills and expertise he develops to improve bioinformatics capacity in Africa.​

Kwame Ahiavi

​​Ph.D. candidate Molecular Biology

The focus of Kwame's work is the implementation of a pipeline for analysing single-cell RNA sequencing data (scRNA-seq), which he developed as part of his MSc project. scRNA-seq is a cutting-edge technique for quantifying gene expression from individual cells, generating novel insights from biomedical research data. The pipeline analyses scRNA-seq data in an automated and reproducible fashion. In the future, Kwame aims to contribute to the implementation of bioinformatics techniques to answer biomedical research questions and contribute to building the capacity of junior scientists via bioinformatics teaching and research in Ghana, his home country, and beyond Sub-Saharan Africa.​

Abhinav Sharma

​​Ph.D. candidate Molecular Biology

Abhinav holds a masters degree in computer science and has extensive experience in developing software for bioinfromatics. His PhD project is focused on creating a new generation of MTb-specific and sequencing technology-independent tools for analyzing WGS data using modern machine learning techniques (i) for genome alignment (ii) for variant calling and (iii) to combine heterogenous machine learning models for resistance prediction, to achieve comparable performance against the state-of-the-art tools.

Tiego Mohlaba

​​M.Sc. candidate Bioinformatics and Computational Biology

Tiego's MSc project is titled 'Single-cell RNA sequencing pipeline: preprocessing, quality control and identification of outliers'. Her future goals are to complete this degree and pursue a PhD in the same field with the intention of contributing to the scientific community.

Ruvarashe Joylyne Madzime

​​M.Sc. candidate Molecular Biology

Ruvarashe is currently pursuing a Master's degree working on a containerised pipeline for the reproducible and standardized analysis of metagenomic and transcriptomic microbiome sequencing data, under the supervision of Prof. Gerard Tromp and Dr. Tomasz Sanko. The pipeline is intended to work on Linux-based compute clusters such as the CHPC. Upon completion of her Master's degree, Ruvarashe would like to apply the skills gained in analysing and interpreting molecular omics data and the development and improvement of data analysis pipelines. She also aims to take on PhD studies to further advance her skills as a bioinformatics researcher.

Tammara Poa Siang Koh

​​M.Sc. candidate Bioinformatics and Computational Biology

Tammara's MSc focuses on protein stability and the effect of mutations in the amino acid sequence with respect to misfolding as well as conformation stability. Her project is titled, A computational framework for parallelised protein folding simulation and the investigation of protein stability. The idea is to make use of a threading algorithm, energy functions, as well as molecular dynamics simulations to investigate the structural stability of the Prion protein and the mutations that could affect it. Tammara hopes that her research provides more insight into the mechanisms of protein folding, the folding pathways, as well as the possibility of misfolding due to mutations in the amino acid sequence. In the near future Tammara would like to be a constructive member of the scientific community, by utilising the skill sets obtained from computer science and creating resources that assist in various flavours of research.

Tiqvah Potgieter

​​​​M.Sc. candidate Bioinformatics and Computational Biology

Tiqvah's MSc project focuses on comparing different parameters in various publicly available single-cell  RNA-sequencing datasets. The aim is to evaluate how these parameters impact downstream cluster​ing and cell-type annotation results. Single-cell RNA-sequencing is a powerful technique that allows researchers to study gene expression at a single-cell level, providing the ability to identify specific cell types within a sample rather than globally, as with bulk RNA-sequencing. The project acknowledges the exciting capabilities of single-cell RNA-sequencing technology. However, it also recognises the challenges associated with pre-processing and downstream analysis of RNA-seq data, which are currently not standardised. By exploring different processing parameters, Tiqvah aims to contribute to the development of more robust and reliable methods for analysing scRNA-seq data.​