Biology subjects

Sergeant, M.

Publications and source records attributed to Sergeant, M..

7 recordsLinked to original sources

bioRxiv2026

The chromosome level genome of the Blueberry Stem Gall Wasp, Hemadas nubilipennis (Hymenoptera: Ormyridae) on lowbush blueberry (Vaccinium angustifolium) reveals repeat-driven expansion

Gall-inducing wasps are emerging models for studying plantinsect coevolution, host manipulation, host plant adaptation, and speciation, yet chromosome-level resources remain scarce for most lineages. The blueberry stem gall wasp (BSGW), Hemadas nubilipennis (Hymenoptera: Ormyridae), is native to North America where it induces galls on both lowbush (Vaccinium angustifolium) and highbush blueberries (V. corymbosum). Recently, BSGW has reached outbreak densities in cultivated highbush production. Given that (a) the biology has been characterized primarily from natural lowbush-associated populations, (b) the absence of genomic resources limits comparative analyses, and (c) populations on cultivated highbush represent a recent host shift, we generated the first chromosome-level genome from wild lowbush blueberry. The BSGW genome consists of five chromosome-scale scaffolds totaling 1.08 Gb (N50 = 218 Mb), the second largest known in Chalcidoidea. Comparative analysis reveals that genome size variation is driven primarily by transposable element proliferation (R = 0.96, p < 0.001), with BSGW exhibiting a high proportion of unclassified TEs. Gene-body methylation is conserved, enriched in exons of broadly expressed core genes, and correlates with gene density. The mitochondrial genome (18,697 bp) exhibits extensive gene rearrangement, and COI sequences reveal 4.35.4% divergence from geographically distant populations, suggesting a complex of cryptic species. Additionally, we assemble a near-complete genome of the endosymbiont Wolbachia pipientis (Supergroup A), which encodes PifA and PifB effectors potentially linked to parthenogenesis. These resources establish a foundation for population genomics, taxonomic revision, and applied management, while providing insights into genome architecture, epigenetics, and symbiont interactions.

genomics↗

bioRxiv2025

ChatMDV: Democratising Bioinformatics Analysis Using Large Language Models

BackgroundThe rapid advancement in single-cell, spatial omics, imaging, and genomic technologies requires robust analytical and visualisation platforms capable of managing complex biological data. Tools such as Multi-Dimensional Viewer (MDV) offer comprehensive interfaces for data exploration, but still require manual configuration and computational expertise to generate visualisation outputs, limiting accessibility for many users. ResultsWe present ChatMDV, a natural language interface integrated with MDV that allows users to generate high-quality interactive visualisations through natural language commands. ChatMDV employs a retrieval-augmented generation (RAG) pipeline combined with large language models (LLMs) to translate user queries into reproducible Python code and interactive output. This approach enables exploratory and targeted analysis in diverse biological domains. We demonstrate ChatMDVs capabilities using three datasets of increasing complexity: the Peripheral Blood Mononuclear Cells 3K (PBMC3K) dataset, the lung cancer atlas dataset hosted at the Human Cell Atlas and the longitudinal TAURUS study single-cell RNA-sequencing (scRNA-seq) dataset. ConclusionsBy bridging the gap between natural language processing and bioinformatics visualisation, ChatMDV reduces technical barriers, enhances reproducibility, and supports more inclusive scientific inquiry. Its modular design and adherence to FAIR (Findability, Accessibility, Interoperability, and Reuse) principles make it a scalable and adaptable framework for accelerating biological data analysis. Key PointsO_LIChatMDV enables users to create interactive visualisations from biological datasets using natural language. C_LIO_LIThe system combines large language models with MDVs graphical platform to simplify data exploration. C_LIO_LIIt supports reproducibility, adaptability, and FAIR data practices, making it suitable for a wide range of users and use cases. C_LI

bioinformatics↗

bioRxiv2024

Deciphering cis-regulatory elements using REgulamentary

With the boom in Genome-Wide Association Studies (GWAS), it has become apparent that many disease-associated genetic variants lie in the non-coding regions of the genome. In order to prioritise these variants and disentangle their functional significance, it is important to be able to accurately classify cis-regulatory elements within these non-coding regions of the genome. Historically, the classification of cis-regulatory elements relied purely on the presence of characteristic histone marks, with recent advancements in their classification using more sophisticated Hidden Markov Model (HMM)-based approaches. The limitation of the HMM-based approaches is that the output of these models is an arbitrary chromatin state, which then requires the user to manually assign these states to a particular class of cis-regulatory elements. Here we present a new tool, REgulamentary, which enables de novo genome-wide annotation of cis-regulatory elements in a cell-type specific manner. We benchmarked REgulamentary against GenoSTAN, the most popular existing published chromatin annotation and regulatory element identification tool, to demonstrate the advancements REgulamentary can provide in assigning chromatin states. Finally, as an example of REgulamentarys utility in solving complex disease trait loci, we applied REgulamentary to published GWAS data to demonstrate how this tool can be used to prioritise likely causal variants.

bioinformatics↗

bioRxiv2022

Breathlessness in a virtual world: An experimental paradigm testing how discrepancy between VR visual gradients and pedal resistance during stationary cycling affects breathlessness perception.

IntroductionThe sensation of breathlessness is often attributed to perturbations in cardio-pulmonary physiology, leading to changes in afferent signals. New evidence suggests that these signals are interpreted in the light of prior "expectations". A misalignment between afferent signals and expectations may underly unexplained breathlessness. Using a novel immersive virtual reality (VR) exercise paradigm, we investigated whether manipulating an individuals expectation of effort (determined by a virtual hill gradient) may alter their perception of breathlessness, independent from actual effort (the physical effort of cycling). MethodsNineteen healthy volunteers completed a single experimental session where they exercised on a cycle ergometer while wearing a VR headset. We created an immersive virtual cycle ride where participants climbed up 100 m hills with virtual gradients of 4%, 6%, 8%, 10% and 12%. Each virtual hill gradient was completed twice: once with a 4% cycling ergometer resistance and once with a 6% resistance, allowing us to dissociate expected effort (virtual hill gradient) from actual effort (physical effort of pedalling). At the end of each hill, participants reported their perceived breathlessness. Linear mixed effects models were used to examine the independent contribution of actual effort and expected effort to ratings of breathlessness (0-10 scale). ResultsExpectation of effort (effect estimate {+/-} std. error, 0.63 {+/-} 0.11, p<0.001) and actual effort (0.81 {+/-} 0.21, p<0.001) independently explained subjective ratings of breathlessness, with comparable contributions of 19% and 18%, respectively. Additionally, we found that effort expectation accounted for 6% of participants physical effort of pedalling and was a significant, independent predictor (0.09 {+/-} 0.03; p=0.001). ConclusionsAn individuals expectation of effort is equally important for forming perceptions of breathlessness as the actual effort required to cycle. A new VR paradigm enables this to be experimentally studied and could be used to re-align breathlessness and enhance training programmes.

neuroscience↗

bioRxiv2020

Multi Locus View : An Extensible Web Based Tool for the Analysis of Genomic Data

MotivationTracking and understanding data quality, analysis and reproducibility are critical concerns in the biological sciences. This is especially true in genomics where Next Generation Sequencing (NGS) based technologies such as ChIP-seq, RNA-seq and ATAC-seq are generating a flood of genome-scale data. These data-types are extremely high level and complex with single experiments capable of mapping ten to hundreds of thousands of biologically meaningful events across the genome. However, such data are usually processed with automated tools and pipelines, generating tabular outputs and static visualizations. These are difficult to interact with and require substantial bioinformatic skills to manipulate and query. Similarly, interpretation is normally made at a high level without the ability to visualise the underlying data in detail and so the complexity and quality of the real underlying biological signal is lost. Also genomics datasets require integration with other genomics datasets to be properly interpreted and this integration with multiple tracks again requires substantial bioinformatics skills and is difficult to visualise across multiple pertinent datasets. Conventional genome browsers do allow for the detailed visualisation of multiple tracks but are limited to browsing single locations and do not allow for interactions with the dataset as a whole. MLV has been developed to allow users to fluidly interact with genomics datasets at multiple scales, from complete metadata labelled and clustered populations to detailed representations of individual elements. It has inbuilt tools to integrate signals across multiple datasets and to perform dimensionality reduction and clustering analysis based on the extracted signal, allowing for the high-level analysis of complex datasets while maintaining visualisation of the fine grain structure of the data. MLVs ability to visualise clustering within the data combined with efficient tools for large-scale tagging of individual elements makes it a unique tool for the generation of annotated datasets for modern machine learning approaches. ResultsMulti Locus View (MLV) is a web based tool for the visualisation, analysis and annotation of Next Generation Sequencing data sets. The user is able to browse the raw data, cluster, and combine the data with other analysis. Intuitive filtering and visualisation then enables the user to quickly locate and annotate regions of interest. User datasets can then be shared with other users or made public for quick assessment from the academic community. MLV is publically available at https://mlv.molbiol.ox.ac.uk and the source code is available at https://github.com/Hughes-Genome-Group/mlv

bioinformatics↗

bioRxiv2020

Targeted high-resolution chromosome conformation capture at genome-wide scale.

Chromosome conformation capture (3C) provides an adaptable tool for studying diverse biological questions. Current 3C methods provide either low-resolution interaction profiles across the entire genome, or high-resolution interaction profiles at up to several hundred loci. All 3C methods are affected to varying degrees by inefficiency, bias and noise. As such, generation of reproducible high-resolution interaction profiles has not been achieved at scale. To overcome this barrier, we systematically tested and improved upon current methods. We show that isolation of 3C libraries from intact nuclei, as well as shortening and titration of enrichment oligonucleotides used in high-resolution methods reduces noise and increases on-target sequencing. We combined these technical modifications into a new method Nuclear-Titrated (NuTi) Capture-C, which provides a >3-fold increase in informative sequencing content over current Capture-C protocols. Using NuTi Capture-C we target 8,061 promoters in triplicate, demonstrating that this method generates reproducible high-resolution genome-wide 3C interaction profiles at scale.

genomics↗

bioRxiv2020

CaptureCompendium: a comprehensive toolkit for 3C analysis.

DNA folding within nuclei is a highly ordered process, with implications for gene regulation and development. An array of chromosome conformation capture (3C) methods have been developed to investigate how DNA is packaged within nuclei and to interrogate specific interactions. While these methods use different approaches to examine target loci (many-versus-all) or the entire genome (all-versus-all), they all rely on the core principle of endonuclease digestion and proximity-based ligation to re-arrange genomic order to reflect the three-dimensional nuclear conformation. This sequence reorganization creates novel chimeric DNA fragments which require specialist bioinformatic tools to analyze and visualize. Despite this need for specialist bioinformatic skills, the core biological importance of genome folding has seen widespread methodological uptake. To service the needs of experimentalists using the many-versus-all Capture-C family of methods we have developed CaptureCompendium; a toolkit of software to simplify the design, analysis and presentation of 3C experiments.

bioinformatics↗