Biology subjects

Smith, V. A.

Publications and source records attributed to Smith, V. A..

3 recordsLinked to original sources

bioRxiv2026

Metabarcode and transcriptome datasets of Pinus sylvestris to assess fungal phyllosphere and disease dynamics.

Understanding how host-microbiome interactions influence tree disease is critical for understanding forest resilience. Here, we present foliar microbiome ITS2 metabarcoding transcriptomic datasets from Pinus sylvestris to investigate susceptibility to Dothistroma needle blight (DNB), a globally important foliar disease caused by Dothistroma septosporum. We hypothesised that host genotype shapes foliar microbial communities and their interactions, thereby influencing disease outcomes. Samples were collected from a progeny-provenance field trial in the south of Scotland representing a broad spectrum of disease susceptibilities. The dataset comprises ITS2 metabarcoding samples from 200 genotypes across three timepoints and RNAseq samples from 48 genotypes across two timepoints. Sampling captured key stages of pathogen exposure and disease progression. Both standardised and bespoke protocols were used for nucleotide extraction, sequencing, and quality control, including multiple negative and positive controls. These datasets, available in the European Nucleotide Archive (project accession PRJEB88228), enable analysis of temporal dynamics in foliar fungal communities, host-microbiome transcriptional responses, and genotype-dependent variation in disease susceptibility.

bioinformatics↗

bioRxiv2026

Dynamic Bayesian networks for neural information flow:evaluation of continuous and discrete scoring metrics

Neural information flow describes the movement of activity between neurons or brain areas. Advances in experimental methods have allowed production of large amounts of observational data related to neuronal activity from the single-neuron to population level. Most current methods for analysing these data are based on pairwise comparison of activity, and fall short of reliably extracting neural information flow network structure. Dynamic Bayesian networks may overcome some of these limitations. Here we evaluate the performance of a range of Bayesian network scoring metrics against the performance of multivariate Granger causality and LASSO regression for their ability to learn the connectivity underlying simulated single-neuron and neuronal population data. We find that discrete dynamic Bayesian networks are the best performing method for single-neuron data, and perform consistently for neural-population data. Continuous dynamic Bayesian networks have a tenancy to learn overly dense structures for both data types, but may have utility in scoping studies on single-neuron data. Multivariate Granger causality is the most robust method for learning structure of neural information flow between neural-populations, but performs poorly on single-neuron data. Significance testing within multivariate Granger causality produces variable results between data types. Overall, this work highlights how the analysis of neural information flow can vary depending on they type and structure of underlying data, and promotes discrete dynamic Bayesian networks as a useful and consistent tool for neural information flow analysis.

neuroscience↗

bioRxiv2025

Bootstrap-based criteria for identifying differences between learned Bayesian networks

Bayesian networks provide a powerful framework for learning dependencies from data, and they are widely used to probe structure in biological systems. Biological systems are governed by complex networks of interactions, and uncovering these interactions and comparing them across conditions is central to understanding biological mechanisms. However, when comparing Bayesian networks, it can be difficult to determine whether observed differences are substantial enough to reflect genuine differences in the underlying systems generating the data. Here, we address this by developing bootstrap-based criteria for identifying such differences and demonstrate their performance using simulated data from synthetic Bayesian networks. Both edge-level and whole-network connectivity comparisons reliably identified when underlying networks differed, even when this involved only 5% of edges, while distinguishing these differences from sampling variation. However, even with large datasets, the criteria were unable to recover specific edge differences. Thus distinguishing that networks differed was possible, but not the specific ways they differed. These criteria establish a framework for more robust and standardised Bayesian network comparisons, with broad potential for real-world applications.

systems biology↗