Biology subjects

Tegally, H.

Publications and source records attributed to Tegally, H..

10 recordsLinked to original sources

bioRxiv2026

Anniemap: Vector Search for Viral Short Read Alignment

Background: The process of aligning sequencing reads to a reference genome is a foundational step in genomic analysis, underpinning tasks from variant detection to pathogen surveillance. In viral genomics, however, this problem becomes substantially more challenging: viral sequences are often present at low abundance within host-dominated samples and can differ markedly from available references due to rapid mutation and population heterogeneity. These characteristics reduce the effectiveness of conventional seed-and-extend aligners, which typically rely on long exact or near-exact matches to anchor alignments. Even modest sequence divergence or sequencing errors can disrupt such seeds, particularly for short reads, leading to missed alignments. The central challenge in this setting is maintaining robust alignment under high divergence without sacrificing efficiency. Results: We introduce Anniemap, a vector search based approach to viral short-read sequence alignment. Anniemap represents reads and reference sequences as binary vectors and performs approximate nearest-neighbour search using Facebook AI Similarity Search (FAISS) to efficiently identify candidate mappings. Anniemap was compared with the well-established alignment tools Bowtie2 and BWA-MEM2 across a diverse set of viral genomes and read lengths using both simulated and real sequencing data. Anniemap achieved higher sensitivity and throughput in almost all evaluated scenarios, with the most substantial improvements in sensitivity observed for highly divergent genomes, such as Hepatitis C virus (HCV) and Human Immunodeficiency Virus (HIV). Conclusions; By measuring vector similarity rather than relying on long exact seed matches, Anniemap provides greater robustness to sequencing errors and genomic mutations. This property is particularly advantageous for viral genomes, where substantial sequence divergence is common. Further work is required to efficiently extend vector-based search for read alignment beyond viral genomes.

genomics↗

bioRxiv2025

Disaggregation Regression and Multi-Model Evaluation for Predicting Dengue Risk in Africa

Dengue risk mapping is essential for estimating disease burden, and informing targeted surveillance and control efforts. Current approaches to risk mapping vary widely in their methodology, data sources, output metrics and applications. Many existing approaches focus on predicting ecological suitability and produce high-resolution risk maps based on environmental conditions, yet high-resolution incidence maps remain scarce, leaving a critical gap in guiding precise, location-specific interventions. The prediction of disease incidence or transmission intensity remains relatively uncommon in disease ecology, largely due to data limitations, reporting biases, and the inherent complexity that arises from transmission dynamics. In this study, we applied disaggregation regression modelling to downscale aggregated dengue case data from 14 countries in Central and South America, generating fine-resolution incidence estimates that we subsequently projected onto the African continent. We then compared the resulting predictions from the incidence-based risk map with three widely used approaches: vector suitability index, dengue environmental suitability index, and mechanistic transmission potential (Index P). The disaggregation model achieved relatively strong predictive accuracy within the training region (mean correlation = 0.72) and showed partial alignment with reported burden across Africa (Spearman {rho} = 0.33). Other risk maps exhibited similar or weaker correlations with reported cases in Africa, including {rho} = 0.33 for dengue environmental suitability, {rho} = 0.32 for transmission potential and {rho} = 0.23 for Aedes aegypti suitability. Disaggregation regression offers a valuable tool for translating reported case data into spatially explicit estimates of burden, bridging the gap between ecological risk and epidemiological relevance. While spatial agreement was high in parts of coastal West Africa across the different risk map approaches, notable divergences highlight the distinct assumptions underlying each framework. Authors SummaryDengue is a mosquito-borne viral disease with expanding global impact. Accurately mapping dengue risk is essential for identifying areas of high transmission and targeting interventions effectively. Most current approaches to mapping dengue risk focus on environmental suitability for the virus or its mosquito vector, rather than estimating actual disease burden. In this study, we used an incidence-based approach; disaggregation regression, to estimate dengue cases at high spatial resolution using national and regional case data from Latin America. We then applied the model to Africa, where surveillance data are limited, and compared its predictions to three other common types of dengue risk maps. Our results showed that while all approaches provided some insight into geographic risk patterns, they often highlighted different areas as priorities. Our incidence-based model captured both where dengue might occur and how intense transmission may be, helping bridge the gap between environmental and ecological suitability of transmission and real-world disease burden. This approach can support more informed decision-making in areas with limited surveillance and guide targeted control efforts.

ecology↗

bioRxiv2025

Craft: A Machine Learning Approach to Dengue Subtyping

MotivationThe dengue virus poses a major global health threat, with nearly 390 million infections annually. A recently proposed hierarchical dengue nomenclature system enhances spatial resolution by defining major and minor lineages within genotypes, aiding efforts to track viral evolution. While current subtyping tools - Genome Detective, GLUE, and NextClade - rely on computationally intensive sequence alignment and phylogenetic inference, machine learning presents a promising alternative for achieving accurate and rapid classification. ResultsWe present Craft (Chaos Random Forest), a machine learning framework for dengue subtyping. We demonstrate that Craft is capable of faster classification speeds while matching or surpassing the accuracy of existing tools. Craft achieves 99.5% accuracy on a hold-out test set and processes over 140 000 sequences per minute. Notably, Craft maintains remarkably high accuracy even when classifying sequence segments as short as 700 nucleotides. Contactdanielvanzyl@sun.ac.za Supplementary informationA supplemental table acknowledging the authors of the GISAID dengue sequences is available at Bioinformatics online.

genomics↗

bioRxiv2025

Identifying genomic surveillance gaps in Africa for the global public health response to West Nile Virus

BackgroundWest Nile Virus (WNV) is a zoonotic flavivirus of significant One Health relevance and is classified as a priority pathogen with a high-risk of causing public health emergencies of global concern. WNV is endemic to Africa; however, the availability of genomic sequences from the continent remains limited. MethodsWe review the extent of polymerase chain reaction testing and genomic sequencing of WNV conducted across Africa. Using phylogeographic methods, we map the spatiotemporal spread of the virus across the continent and globally. FindingsOur study shows that WNV has been detected in 39 African countries (including Comoros, Seychelles, and Mauritius), the Canary Islands, and Reunion Island. Publications including molecular data originate from 24 countries; however, genomic sequences are publicly available for only 16 countries. We identify regions with detected viral circulation but lacking molecular surveillance. Further, we list such regions that overlap with Key Biodiversity Areas (sites harbouring significant bird diversity) as they may host high viral circulation, and high human population density that may be susceptible to spillover. InterpretationWe recognise significant knowledge gaps on the true disease burden, molecular epidemiology, and distribution of WNV in Africa. Addressing these gaps requires an integrated One Health surveillance approach which is challenging to establish. We propose three key surveillance needs as potential starting points to improve our understanding of the virus in Africa to strengthen the global public health response to this disease. FundingRockefeller Foundation, the National Institute of Health USA, Institute of Human Virology Nigeria, Global Health EDCTP3 Joint Undertaking, the Health Emergency Preparedness and Response Umbrella Program, managed by the World Bank Group, the Medical Research Foundation, and the Wellcome Trust.

molecular biology↗

bioRxiv2024

Alignment Free Viral Sequence Classification

BackgroundThe rapid increase in nucleotide sequence data generated by next-generation sequencing (NGS) technologies demands efficient computational tools for sequence comparison. Alignment-based methods, such as BLAST, are increasingly overwhelmed by the scale of contemporary datasets due to their high computational demands for classification. This study evaluates alignment-free (AF) methods as scalable and rapid alternatives for viral sequence classification, focusing on identifying techniques that maintain high accuracy and efficiency when applied to extremely large datasets. ResultsWe employed six established AF techniques to extract feature vectors from viral genomes, which were subsequently used to train Random Forest classifiers. Our primary dataset comprises 297,186 SARS-CoV-2 nucleotide sequences, categorized into 3502 distinct lineages. Furthermore, we validated our models using dengue and HIV sequences to demonstrate robustness across different viral datasets. Our AF classifiers achieved 97.8% accuracy on the SARS-CoV-2 test set, and 99.8% and 89.1% accuracy on dengue and HIV test sets, respectively. ConclusionDespite the high-class dimensionality, we show that word-based AF methods effectively represent viral sequences. Our study highlights the practical advantages of AF techniques, including significantly faster processing compared to alignment-based methods and the ability to classify sequences using modest computational resources.

genomics↗

bioRxiv2023

SeqPanther: Sequence manipulation and mutation statistics toolset

Pathogen genomes harbor critical information necessary to support genomic investigations that inform public health interventions such as treatment, control, and eradication. To extract this information, their sequences are analysed to identify structural variations such as single nucleotide polymorphisms (SNPs) and insertions and deletions (indels) that may be associated with phenotypes of interest. Typically, this involves generating a consensus sequence from raw reads, aligning it to a reference and identifying positions where variations occur. Several pipelines exist to map raw reads and assemble whole genomes for downstream analysis. However, there is no easy to use, freely available bioinformatics quality control (QC) tool to explore mappings for both positional codons and nucleotide distributions in mapped short reads of microbial genomes. To address this problem, we have developed a fast and accurate tool to summarise read counts associated with codons, nucleotides, and indels in mapped next-generation sequencing (NGS) short reads. The tool, developed in Python, also provides a visualization of the genome sequencing depth and coverage. Furthermore, the tool can be run in single or batch mode, where several genomes need to be analysed. Our tool produces a text-based report that enables quick review or can be imported into any analytical tool for upstream analysis. Additionally, the tool also provides functionality to modify the consensus sequences by adding, masking, or restoring to wild type mutations specified by the user. AvailabilitySeqPanther is available at https://github.com/codemeleon/seqPanther, along with the necessary documentation for installation and usage.

bioinformatics↗

bioRxiv2022

Selection analysis identifies unusual clustered mutational changes in Omicron lineage BA.1 that likely impact Spike function

Among the 30 non-synonymous nucleotide substitutions in the Omicron S-gene are 13 that have only rarely been seen in other SARS-CoV-2 sequences. These mutations cluster within three functionally important regions of the S-gene at sites that will likely impact (i) interactions between subunits of the Spike trimer and the predisposition of subunits to shift from down to up configurations, (ii) interactions of Spike with ACE2 receptors, and (iii) the priming of Spike for membrane fusion. We show here that, based on both the rarity of these 13 mutations in intrapatient sequencing reads and patterns of selection at the codon sites where the mutations occur in SARS-CoV-2 and related sarbecoviruses, prior to the emergence of Omicron the mutations would have been predicted to decrease the fitness of any genomes within which they occurred. We further propose that the mutations in each of the three clusters therefore cooperatively interact to both mitigate their individual fitness costs, and adaptively alter the function of Spike. Given the evident epidemic growth advantages of Omicron over all previously known SARS-CoV-2 lineages, it is crucial to determine both how such complex and highly adaptive mutation constellations were assembled within the Omicron S-gene, and why, despite unprecedented global genomic surveillance efforts, the early stages of this assembly process went completely undetected.

evolutionary biology↗

bioRxiv2021

SARS-CoV-2 cell-to-cell spread occurs rapidly and is insensitive to antibody neutralization

Viruses increase the efficiency of close-range transmission between cells by manipulating cellular physiology and behavior, and SARS-CoV-2 uses cell fusion as one mechanism for cell-to-cell spread. Here we visualized infection using time-lapse microscopy of a human lung cell line and used live virus neutralization to determine the sensitivity of SARS-CoV-2 cell-to-cell spread to neutralizing antibodies. SARS-CoV-2 infection rapidly led to cell fusion, forming multinucleated cells with clustered nuclei which started to be detected at 6h post-infection. To compare sensitivity of cell-to-cell spread to neutralization, we infected either with cell-free virus or with single infected cells expressing on their surface the SARS-CoV-2 spike protein. We tested two variants of SARS-CoV-2: B.1.117 containing only the D614G substitution, and the escape variant B.1.351. We used the much smaller area of single infected cells relative to infection foci to exclude any input infected cells which did not lead to transmission. The monoclonal antibody and convalescent plasma we tested neutralized cell-free SARS-CoV-2, with the exception of B.1.351 virus, which was poorly neutralized with plasma from non-B.1.351 infections. In contrast, cell-to-cell spread of SARS-CoV-2 showed no sensitivity to monoclonal antibody or convalescent plasma neutralization. These observations suggest that, once cells are infected, SARS-CoV-2 may be more difficult to neutralize in cell types and anatomical compartments permissive for cell-to-cell spread.

microbiology↗

bioRxiv2021

SARS-CoV-2 501Y.V2 (B.1.351) elicits cross-reactive neutralizing antibodies

Neutralization escape by SARS-CoV-2 variants, as has been observed in the 501Y.V2 (B.1.351) variant, has impacted the efficacy of first generation COVID-19 vaccines. Here, the antibody response to the 501Y.V2 variant was examined in a cohort of patients hospitalized with COVID-19 in early 2021 - when over 90% of infections in South Africa were attributed to 501Y.V2. Robust binding and neutralizing antibody titers to the 501Y.V2 variant were detected and these binding antibodies showed high levels of cross-reactivity for the original variant, from the first wave. In contrast to an earlier study where sera from individuals infected with the original variant showed dramatically reduced potency against 501Y.V2, sera from 501Y.V2-infected patients maintained good cross-reactivity against viruses from the first wave. Furthermore, sera from 501Y.V2-infected patients also neutralized the 501Y.V3 (P.1) variant first described in Brazil, and now circulating globally. Collectively these data suggest that the antibody response in patients infected with 501Y.V2 has a broad specificity and that vaccines designed with the 501Y.V2 sequence may elicit more cross-reactive responses.

immunology↗

bioRxiv2020

Whole Genome Sequencing of SARS-CoV-2: Adapting Illumina Protocols for Quick and Accurate Outbreak Investigation During a Pandemic

The COVID-19 pandemic spread very fast around the world. A few days after the first detected case in South Africa, an infection started a large hospital outbreak in Durban, KwaZulu-Natal. Phylogenetic analysis of SARS-CoV-2 genomes can be used to trace the path of transmission within a hospital. It can also identify the source of the outbreak and provide lessons to improve infection prevention and control strategies. In this manuscript, we outline the obstacles we encountered in order to genotype SARS-CoV-2 in real-time during an urgent outbreak investigation. In this process, we encountered problems with the length of the original genotyping protocol, reagent stockout and sample degradation and storage. However, we managed to set up three different library preparation methods for sequencing in Illumina. We also managed to decrease the hands on library preparation time from twelve to three hours, which allowed us to complete the outbreak investigation in just a few weeks. We also fine-tuned a simple bioinformatics workflow for the assembly of high-quality genomes in real-time. In order to allow other laboratories to learn from our experience, we released all of the library preparation and bioinformatics protocols publicly and distributed them to other laboratories of the South African Network for Genomics Surveillance (SANGS) consortium.

genomics↗