Results for “phylodynamics and diversification studies”

PubMed2025

Phylogenetic Methods Meet Deep Learning.

Deep learning (DL) has been widely used in various scientific fields, but its integration into phylogenetics has been slower, primarily due to the complex nature of phylogenetic data. The studies that apply DL to sequencing data often limit analyses to four-taxon trees. Many of these studies serve as "proof of principle" and perform similarly to traditional phylogeny reconstruction methods. New ways of using training data, such as encoding with compact bijective ladderized vectors or transformers, enable the handling of much larger trees and genomic data sets. This short perspective focuses on the application of DL in phylogenetics, introducing prevalent DL architectures. We highlight potential problems in the field by discussing the risks of using simulation-based training data and emphasize the importance of reproducibility and robustness in computational estimates. Finally, we explore promising research areas, including the combination of phylogenetics and population genetics in DL, the analysis of neighbor dependencies, and the potential to significantly reduce computational cost compared to traditional methods. This perspective illustrates the potential of DL in complementing traditional phylogeny reconstruction methods and aiding the advancement of phylogenetic analysis, especially in performing computationally demanding tasks such as model selection or estimating branch support values.

Humans↗

PubMed2026

Spatiotemporal patterns of Rift Valley fever virus in Africa: a retrospective genomic epidemiology and phylodynamic modelling study.

BACKGROUND: Rift Valley fever virus (RVFV) is a mosquito-borne zoonotic pathogen causing outbreaks in humans and ruminants across Africa and the Arabian Peninsula. Originally restricted to the Great Rift Valley, RVFV has expanded geographically, prompting its classification by WHO as a pathogen of pandemic potential. We investigated the evolutionary and spatial dynamics of RVFV across Africa. METHODS: We used genomic data generated at the International Livestock Research Institute Nairobi genomic laboratory (BioProject PRJNA1106221) and combined with publicly available datasets retrieved from the National Center for Biotechnology (NCBI) GenBank nucleotide database. In retrieving RVFV genome sequences from the NCBI GenBank, we applied the search terms "Rift Valley fever virus segment L AND 6404[SLEN]", "Rift Valley fever virus segment M AND 3885[SLEN]", and "Rift Valley fever virus segment S AND 1520:1690[SLEN]" for L (Large), M (Medium), and S (Small) segments, respectively. For sequences without additional spatiotemporal information, we searched PubMed to extract the associated sequence metadata. We performed molecular clock analysis, phylogenetic inference, phylodynamic modelling (continuous phylogeographic reconstruction), and landscape phylogeography on the three RVFV genome segments (L, M, and S). We aimed to assess evolutionary rates, dispersal patterns, and environmental drivers. Focus was placed on lineage C, the most widely distributed variant. FINDINGS: The global dataset used in this study consisted of large (n=236), medium (n=237), and small (n=247), which were further filtered to exclude potential reassortants and vaccine strains. Genome sequences retrieved from NCBI GenBank database comprised large (n=180), medium (n=184), and small (n=202). The genome sequences from retrospective human and livestock isolates comprised large (n=56), medium (n=53), and small (n=45) collected in Burundi (2018), Kenya (2007, 2018, 2019, 2021, and 2022), and Rwanda (2018 and 2022). Our dataset revealed that RVFV exhibited low overall genetic diversity. Lineage C, however, showed evidence of active evolution, with substitution rates ranging from 3·58 × 10-4 to 9·76 × 10-4 substitutions per site per year. This lineage probably originated in Zimbabwe in the mid-1970s and has since expanded across eastern and southern Africa. Phylogeographic reconstructions revealed rapid spread, with diffusion coefficients exceeding 50 000 km2 per year. INTERPRETATION: Lineage C appears capable of establishing endemic transmission in new regions, with ongoing diversification observed during interepidemic periods. These observations reinforce the value of continuous genomic surveillance, particularly during cryptic transmission phases when adaptive mutations might emerge. Although further evidence is needed, observed trends in climate variability and land-use change point to the potential benefit of targeted surveillance in settings that could be at increased risk, including urban centres and wetlands. FUNDING: This work was supported by the German Federal Ministry for Economic Cooperation and Development, the Rockefeller Foundation, and the Africa Centres for Disease Control and Prevention.

Rift Valley fever virus↗