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.
D. J. van Zyl, H. Tegally, C. Baxter et al.· bioRxiv· 0 citations
Infectious-disease risk models often rely on occurrence records that are incomplete and spatially biased by surveillance effort, diagnostic access, and outbreak history. Ecological niche modelling (ENM) can identify areas where disease occurrence is environmentally plausible, yet most approaches represent locations using only pointwise covariate values and therefore overlook the surrounding spatial context and rely on presence-only data. Here, we present a deep-learning framework for presence-only data that estimates relative disease suitability by comparing the environmental conditions surrounding reported occurrences with those sampled across the wider study area. The model processes gridded environmental patches at local, neighbourhood, and broader landscape scales, learns the contribution of each scale, and accommodates missing raster values. Using dengue virus as a global case study, we evaluate whether multiscale spatial representation improves upon point-based ENM baselines including random forest and maximum entropy (MaxEnt) under a spatially disjoint train-test design. The model achieved a Boyce index of 0.971 and an AUC of 0.976 on the held-out test set. Learned scale weights and ablation experiments indicated that neighbourhood context contributed most strongly, while local and broader-scale information provided complementary predictive signals. Compared with point-based baselines, the model identified 6-18% more environmentally suitable area across South Asia, Southeast Asia, and South America, encompassing tens of millions of residents. These findings demonstrate that multiscale spatial context can improve estimates of relative dengue suitability. More broadly, mask-aware convolutional density-ratio estimation provides a flexible framework for mapping environmentally structured pathogens from incomplete, presence-only occurrence data.
C. Foka Takamgno, J. Poongavanan, M. U. Kraemer et al.· medRxiv· 0 citations
The 2026 Bundibugyo virus outbreak emerged in a region with frequent conflict, food insecurity, rainforest and mining-related human mobility in Ituri province in the north-eastern region of the Democratic Republic of Congo (DRC). Existing ecological niche models have identified regions environmentally suitable for orthoebolavirus circulation but do not explicitly account for anthropogenic conditions that shape opportunities for interspecies contact, such as wildlife-to-human, and zoonotic spillover. Here, we update habitat suitability models for three putative orthoebolavirus reservoir bat species and for orthoebolavirus, and develop an integrated spatial spillover risk framework that combines ecological suitability with anthropogenic drivers, including human settlement, mining activity, bushmeat-related activities, forest loss, and conflict. We find that the updated model reveals previously under-predicted suitability in eastern DRC, and that the integration of anthropogenic factors with orthoebolavirus habitat suitability improves the prediction of historical zoonotic spillover locations. Boyce Index (measure of spatial predictive accuracy) increases from 0.78 to 0.97 when ecological suitability was combined with the built environment, while mining- and bushmeat-based scenarios showed the greatest enrichment of observed spillover events. We also find a temporal association of the relative contribution of habitat suitability and anthropogenic factors, with mining showing the largest and most consistent effect over the last decade, and conflict acting as a secondary amplifying factor whose apparent contribution has grown in recent periods. Together, these findings demonstrate that ecological suitability alone does not fully characterize landscapes vulnerable to Ebola zoonotic emergence and highlight the value of integrating environmental and anthropogenic information to strengthen One Health surveillance, epidemic preparedness, and targeted public health interventions in the DRC and neighboring countries.
M. Moir, H. Tegally, D. M. Moges et al.· medRxiv· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
