Biology subjects

van der Spuy, G.

Publications and source records attributed to van der Spuy, G..

3 recordsLinked to original sources

bioRxiv2025

MTBseq-nf: Enabling Scalable Tuberculosis Genomics "Big Data" Analysis through a User-Friendly Nextflow Wrapper for MTBseq pipeline

The MTBseq pipeline, published in 2018, was designed to address bioinformatics challenges in tu- berculosis research using whole-genome sequencing data. It was the first publicly available pipeline on GitHub to perform full analysis of whole-genome sequencing (WGS) data for Mycobacterium tuberculosis encompassing quality control through mapping, variant calling for lineage classifica- tion, drug resistance prediction, and phylogenetic inference. However, the pipelines architecture is not optimal for analyses on high-performance computing or cloud computing environments, which often involve large datasets. To optimize the pipeline, we created MTBseq-nf, a Nextflow wrapper which offers shorter execution times through parallelization along with multiple other key improvements. The MTBseq-nf wrapper, as opposed to the linear, batched analysis of samples in the TBfull step of MTBseq pipeline, can execute multiple instances of the same step in parallel and therefore makes full use of the provided computational resources. For evaluation of scalability and reproducibility, we used 90 M. tuberculosis genomes (European Nucleotide Archive - ENA- accession PRJEB7727) for the benchmarking analysis on a dedicated computational server. In our experiments the execution time of MTBseq-nf parallel analysis mode is at least twice as fast as the standard MTBseq pipeline for more than 20 samples. Furthermore, the MTBseq-nf wrapper facilitates reproducibility using the nf-core, bioconda, and biocontainers projects for platform independence. The proposed MTBseq-nf wrapper pipeline is, user-friendly, optimized for hardware efficiency, scalable for larger datasets, and exhibits improved reproducibility.

bioinformatics↗

bioRxiv2022

GWAS in the Southern African context

Researchers would generally adjust for the possible confounding effect of population structure by considering global ancestry proportions or top principle components. Alternatively, researchers would conduct admixture mapping to increase the power to detect variants with an ancestry effect. This is sufficient in simple admixture scenarios, however, populations from southern Africa can be complex multi-way admixed populations. Duan et al. (2018) first described local ancestry adjusted allelic (LAAA) analysis as a robust method for discovering association signals, while producing minimal false-positives. Their simulation study, however, was limited to a two-way admixed population. Realizing that their findings might not translate to other admixture scenarios, we simulated a three- and five-way admixed population to compare the LAAA model to other models commonly used in GWAS. We found that, given our admixture scenarios, the LAAA model identifies the most causal variants in most of the phenotypes we tested across both the three-way and five-way admixed populations. The LAAA model also produced a high number of false-positives which was potentially caused by the ancestry effect size that we assumed. Considering the extent to which the various models tested differed in their results and considering that the source of a given association is unknown, we recommend that researchers use multiple GWAS models when analysing populations with complex ancestry.

genetics↗

bioRxiv2021

The Recombination Landscape of the Khoe-San - the Upper Limits of Recombination Divergence in Humans

Recombination maps are important resources for epidemiological and evolutionary analyses, however, there are currently no recombination maps representing any African population outside of those with West African ancestry. We inferred the demographic history for the Nama, an indigenous Khoe-San population of southern Africa, and derived a novel, population-specific recombination map from the whole genome sequencing of 54 Nama individuals. We hypothesized that there are no publicly available recombination maps representative of the Nama, considering the deep population divergence and subsequent isolation of the Khoe-San from other African groups. We showed that the recombination landscape of the Nama does not cluster with any continental groups with publicly available representative recombination maps. Finally, we used selection scans as an example of how fine-scale differences between the Nama recombination map and the combined Phase II HapMap recombination map can impact the outcome of selection scans.

bioinformatics↗