Biology subjects

Moulton, V.

Publications and source records attributed to Moulton, V..

6 recordsLinked to original sources

bioRxiv2026

Machine learning approaches for the identification and analysis of enterotoxin genes in Staphylococcus aureus genomes

2.Staphylococcus aureus produces a broad range of enterotoxins that act as superantigens, disrupting host immune responses and resulting in a myriad of clinical symptoms. However, large-scale analyses determining enterotoxin gene diversity, lineage structure and isolate metadata remain scarce. We analysed 15,887 S. aureus RefSeq genomes using a machine learning pipeline combining profile Hidden Markov Model-based enterotoxin gene identification, lineage typing, gene profile-based strain clustering and association rule mining using a broad range of gene and metadata features. This approach identified 35 distinct enterotoxin genes and five variant forms, including two putative novel enterotoxin genes, sel34 and sel35. HDBSCAN clustering distinguished 45 enterotoxin gene profile groups, revealing strong associations between the two major egc enterotoxin gene cluster variants (OMIWNG and OMIUNG) and Clonal Complex membership: CC5, CC22 and CC45 with OMIWNG; CC30 and CC121 with OMIUNG. Integration of isolate metadata exposed distinct geographic and temporal trends, including a recent rise in non-egc lineages derived from Asia and animal sources. These findings show that S. aureus enterotoxin diversity is structured by lineage, mobile genetic element composition and Clonal Complex association. The discovery of sel34 and sel35, together with the comprehensive overview of lineage-specific enterotoxin profiles, expands current understanding of S. aureus virulence evolution and provides a scalable analytical framework for monitoring toxin gene dynamics in clinical and environmental populations. 3. Impact StatementUnderstanding how virulence genes evolve and spread in Staphylococcus aureus is vital for predicting pathological potential and managing infection risk for this species. By analysis of over 15,000 publicly available S. aureus genomes, this study provides the most comprehensive overview to date of enterotoxin gene diversity and lineage structure. Using machine learning and large-scale genomic mining, we reveal clear evolutionary and epidemiological patterns linking enterotoxin gene clusters to specific Clonal Complexes and identify two previously unknown enterotoxin genes. These findings highlight how recombination and horizontal gene transfer shape S. aureus toxins across hosts, continents and time. The resulting analytical framework offers a scalable foundation for future genomic surveillance of virulence evolution in both clinical and environmental settings. 4. Data SummaryGenome assemblies and associated metadata for the 15,887 Staphylococcus aureus strains analysed within this study were generated elsewhere prior to this study and were here downloaded from the NCBI RefSeq database. Secondary datasets and descriptions of algorithmic approaches used to develop them are presented in the main text and within the Supplementary Files 1 and 2.

bioinformatics↗

bioRxiv2024

Squirrel: Reconstructing semi-directed phylogenetic level-1 networks from four-leaved networks or sequence alignments

With the increasing availability of genomic data, biologists aim to find more accurate descriptions of evolutionary histories influenced by secondary contact, where diverging lineages reconnect before diverging again. Such reticulate evolutionary events can be more accurately represented in phylogenetic networks than in phylogenetic trees. Since the root location of phylogenetic networks can not be inferred from biological data under several evolutionary models, we consider semi-directed (phylogenetic) networks: partially directed graphs without a root in which the directed edges represent reticulate evolutionary events. By specifying a known outgroup, the rooted topology can be recovered from such networks. We introduce the algorithm SO_SCPLOWQUIRRELC_SCPLOW (Semi-directed Quarnet-based Inference to Reconstruct Level-1 Networks) which constructs a semi-directed level-1 network from a full set of quarnets (four-leaf semi-directed networks). Our method also includes a heuristic to construct such a quarnet set directly from sequence alignments. We demonstrate SO_SCPLOWQUIRRELC_SCPLOWs performance through simulations and on real sequence data sets, the largest of which contains 29 aligned sequences close to 1.7 Mbp long. The resulting networks are obtained on a standard laptop within a few minutes. Lastly, we prove that SO_SCPLOWQUIRRELC_SCPLOW is combinatorially consistent: given a full set of quarnets coming from a triangle-free semi-directed level-1 network, it is guaranteed to reconstruct the original network. SO_SCPLOWQUIRRELC_SCPLOW is implemented in Python, has an easy-to-use graphical user-interface that takes sequence alignments or quarnets as input, and is freely available at https://github.com/nholtgrefe/squirrel.

evolutionary biology↗

bioRxiv2023

Algebraic invariants for inferring 4-leaf semi-directed phylogenetic networks

A core goal of phylogenomics is to determine the evolutionary history of a set of species from biological sequence data. Phylogenetic networks are able to describe more complex evolutionary phenomena than phylogenetic trees but are more difficult to accurately reconstruct. Recently, there has been growing interest in developing methods to infer semi-directed phylogenetic networks. As computing such networks can be computationally intensive, one approach to building such networks is to puzzle together smaller networks. Thus, it is essential to have robust methods for inferring semi-directed phylogenetic networks on small numbers of taxa. In this paper, we investigate an algebraic method for performing phylogenetic network inference from nucleotide sequence data on 4-leaf semi-directed phylogenetic networks by analysing the distribution of leaf-pattern probabilities. On simulated data, we found that we can correctly identify with high accuracy the undirected phylogenetic network for sequences of length at least 10kbp. We found that identifying the semi-directed network is more challenging and requires sequences of length approaching 10Mbp. We are also able to use our approach to identify tree-like evolution and determine the underlying tree. Finally, we employ our method on a real dataset from Xiphophorus species and use the results to build a phylogenetic network.

evolutionary biology↗

bioRxiv2020

Metagenome-assembled genomes of phytoplankton communities across the Arctic Circle

Phytoplankton communities significantly contribute to global biogeochemical cycles of elements and underpin marine food webs. Although their uncultured genetic diversity has been estimated by planetary-scale metagenome sequencing and subsequent reconstruction of metagenome-assembled genomes (MAGs), this approach has yet to be applied for eukaryote-enriched polar and non-polar phytoplankton communities. Here, we have assembled draft prokaryotic and eukaryotic MAGs from environmental DNA extracted from chlorophyll a maximum layers in the surface ocean across the Arctic Circle in the Atlantic. From 679 Gbp and estimated 50 million genes in total, we recovered 140 MAGs of medium to high quality. Although there was a strict demarcation between polar and non-polar MAGs, adjacent sampling stations in each environment on either side of the Arctic Circle had MAGs in common. Furthermore, phylogenetic placement revealed eukaryotic MAGs to be more diverse in the Arctic whereas prokaryotic MAGs were more diverse in the Atlantic south of the Arctic Circle. Approximately 60% of protein families were shared between polar and non-polar MAGs for both prokaryotes and eukaryotes. However, eukaryotic MAGs had more protein families unique to the Arctic whereas prokaryotic MAGs had more families unique to south of the Arctic circle. Thus, our study enabled us to place differences in functional plankton diversity in a genomic context to reveal that the evolution of these MAGs likely was driven by significant differences in the seascape on either side of an ecosystem boundary that separates polar from non-polar surface ocean waters in the North Atlantic.

microbiology↗

bioRxiv2019

PAREameters: computational inference of plant microRNA-mRNA targeting rules using RNA sequencing data

MicroRNAs (miRNAs) are short, non-coding RNAs that influence the translation-rate of mRNAs by directing the RNA-induced silencing complex to sequence-specific targets. In plants, this typically results in cleavage and subsequent degradation of the mRNA. This can be captured on a high-throughput scale using degradome sequencing, which supports miRNA target prediction by aligning degradation fragments to reference mRNAs enabling the identification of causal miRNA(s). The current criteria used for target prediction were inferred on experimentally validated A. thaliana interactions, which were adapted to fit that specific subset of miRNA interactions. In addition, the miRNA pathway in other organisms may have acquired specific changes, e.g. lineage-specific miRNAs or new miRNA-mRNA interactions, thus previous criteria may not be optimal. We present a new tool, PAREameters, for inferring targeting criteria from RNA sequencing datasets; the stability of inferred criteria under subsampling and the effect of input-size are discussed. We first evaluate its performance using experimentally validated miRNA-mRNA interactions in multiple A. thaliana datasets, including conserved and species-specific miRNAs. We then perform comprehensive analyses on the differences in flower miRNA-mRNA interactions in several non-model organisms and quantify the observed variations. PAREameters highlights an increase in sensitivity on most tested datasets when data-inferred criteria are used.

bioinformatics↗

bioRxiv2017

SPECTRE: a Suite of PhylogEnetiC Tools for Reticulate Evolution

SummarySplit-networks are a generalization of phylogenetic trees that have proven to be a powerful tool in phylogenetics. Various ways have been developed for computing such networks, including split-decomposition, NeighborNet, QNet and FlatNJ. Some of these approaches are implemented in the user-friendly SplitsTree software package. However, to give the user the option to adjust and extend these approaches and to facilitate their integration into analysis pipelines, there is a need for robust, open-source implementations of associated data structures and algorithms. Here we present SPECTRE, a readily available, open-source library of data structures written in Java, that comes complete with new implementations of several pre-published algorithms and a basic interactive graphical interface for visualizing planar split networks. SPECTRE also supports the use of longer running algorithms by providing command line interfaces, which can be executed on servers or in High Performance Computing (HPC) environments.\n\nAvailabilityFull source code is available under the GPLv3 license at: https://github.com/maplesond/SPECTRE\n\nSPECTREs core library is available from Maven Central at: https://mvnrepository.com/artifactuk.ac.uea.cmp.spectre/core\n\nDocumentation is available at: http://spectre-suite-of-phylogenetic-tools-for-reticulate-evolution.readthedocs.io/en/latest/\n\nContactsarah.bastkowski@earlham.ac.uk\n\nSupplementary Information (SI)Supplementary information is available at Bioinformatics online.

bioinformatics↗