Head of Division, Computational Biology, Department of Integrative Biomedical Sciences, & Member of the Institute of Infectious Disease and Molecular Medicine (IDM), Faculty of Health Sciences, University of Cape Town; PI H3ABioNet Bioinformatics Network
The Computational Biology (CBIO) Division is the centre of Bioinformatics activities at the University of Cape Town. It aims to perform world-class bioinformatics research and provide high quality bioinformatics education, training and services. Nicola Mulder’s main research interests lie in the areas of genomics and its application to human diseases of relevance to Africa.
The group is working on pan genome analysis of Pneumococcal genomes to determine the mechanism of carriage. We have also developed a graph-based approach for the analysis of different strains of bacterial pathogens and applied this to Mycobacterium tuberculosis.
African population genetics and diseases
The group has developed new algorithms and tools for the analysis of African genetic data. We have developed expertise in GWAS and population genetics and run an imputation service for H3Africa researchers. We also collaborate with researchers to use GWAS data to study the genetic determinants of disease. The group forms the central node for H3ABioNet, the pan African bioinformatics network for H3Africa. The network is building capacity for genomics research in Africa and develops data standards and tools for the data.
The group works with collaborators to analyse microbiome data related to various diseases. We have developed containerized workflows for this analysis as well as for the analysis of whole genome shotgun metagenomics data. We have also developed a new tool for the analysis of metaproteomic data.
Bioinformatics tools and support
In addition to research, we provide bioinformatics services to other researchers in the Institute and beyond. We have developed workflows for genomics-related data analysis, including GWAS, NGS and variant calling, RNASeq, 16S rRNA analysis, and WGS metagenomics. We also develop new algorithms and visualisation tools, including those for pan-genome analysis. We work collaboratively with researchers requiring help with general bioinformatics problems and large-scale data analysis.