Welcome to Stellenbosch University

​Division of Molecular ​​Biology and Human Genetics

​​South African Tuberculosis Bioinformatics Initiative



Abhinav Sharma

PhD candidate Molecular Biology]

Abhinav's PhD project is titled "Next generation tools for analysis of Mycobacterium tuberculosis sequencing data using technology-independent techniques driven by Machine Learning" and is focused on building tools which can accommodate the current and future generations of NGS technologies. Abhinav has a background in Computer Science (Bachelor's degree) and Data Science (Master’s degree) is passionate about reproducible, open science as well as capacity building and enjoys teaching opportunities. Abhinav is looking forward to honing his skills in Data Science and Bioinformatics to contribute to the Biomedical research community.

Ncité Lima Da Camara

​​Ph.D. candidate Molecular Biology

Ncite’s PhD project titled "Tools for Analysis of Luminex Immunoassay Data: Development of a Robust Pipeline and Best Practices Recommendations" aimed to reduce the variability introduced into multiplex immunoassay data (i.e., Luminex data) prior to receiving the data and during data pre-processing by recommending best practices for Luminex data generation and developing an automated data pre-processing pipeline to standardise data pre-processing to provide reproducible results that is the foundation for good science. The future prospects of this study are more extensive testing of the pipeline and to implement a graphical user interface to make the pipeline user-friendly.

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.

Tiego Mohlaba

​​M.Sc.. candidate Bioinformatics and Computationsl 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.

Jesse Asimeng​

​​M.Sc. candidate Molecular Biology

Jesse is working on using current best practices and approaches such as containers, workflow management systems (Nextflow), unit testing as well as the development of an R package to enhance the reproducibility and robustness of multiplexed ELISA (Luminex) data processing and analysis. This work is an extension of the work by Ncité Lima DaCamara as part of her PhD. Jesse is looking forward to advancing his studies with a PhD to hone his skills and expertise in Bioinformatics and Data Science, in the near future.

Kwame Ahiavi

​​M.Sc. 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.​

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.

Philo Tchokonte-Nana

​​B.Sc. Hons. candidate Bioinformatics and Computational Biology

As part of her research project, Philo used R with the Tidymodels machine learning framework to create a machine learning model to find the most effective machine learning method for TB prediction. Her work also compared the performances of the different algorithms.  Luminex datasets of 22 host biomarkers from adults presenting with symptoms suggestive of pulmonary TB disease in 6 countries were used for the modelling. This work was supervised by Prof. Gian Van der Spuy. Philo’s immediate future goal is to obtain a Master's in bioinformatics focusing on mathematical modelling of glycolysis in breast cancer cell lines.

Tammara Poa Siang Koh

​​B.Sc. Hons. candidate Bioinformatics and Computational Biology

Tammara’s honours project focused on biological databases and the creation of a web resource where one is able to search for these databases without redundant searches or replication of existing biological data. The web resource, Biodata, serves as a single venue where bioinformatics database resources are consolidated. Information on these resources were taken from the 2015 to 2022 annual Nucleic Acid Research database issues. 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.