Biology subjects

Dimonaco, N. J.

Publications and source records attributed to Dimonaco, N. J..

11 recordsLinked to original sources

bioRxiv2026

GeneFior: A back to basics and transparent multi-tool approach tosequence detection

The detection of sequences of interest, such as antimicrobial resistance genes, directly from genomic and metagenomic sequencing data has become routine, enabled by curated reference databases and rapid in silico sequence search tools. Yet most workflows depend on prior assembly, an inherently lossy process in which a substantial proportion of reads fail to assemble or are collapsed into consensus sequences, causing low-abundance variants and nucleotide-level diversity to be systematically obscured. The tools used to interrogate the resulting assemblies compound this further, clustering reference sequences at arbitrary identity thresholds, imposing hidden parameter defaults, and reducing intermediate alignment evidence to summarised outputs that cannot be critically evaluated or reproduced. Here we present GeneFior, a transparent, multitool workflow integrating BLAST, DIAMOND, Bowtie2, BWA, and Minimap2 to search both DNA and protein sequences against any user-supplied reference database. By enforcing genecentric identity and coverage thresholds at both the read and gene level, GeneFior reduces false positives while retaining sensitivity to genuine, low-abundance variants, including those differing at single-nucleotide resolution. Crucially, by exposing all alignment parameters, preserving intermediate outputs, and generating cross-tool consensus detection matrices, GeneFior makes the influence of tool choice, database selection, and parameter configuration on reported gene profiles directly observable and reproducible.

bioinformatics↗

bioRxiv2025

Uncovering the Genomic Landscape of Mycobacterium bovis in Wales

Bovine tuberculosis (bTB), caused by the bacterium Mycobacterium bovis, is one of the most pressing animal health issues in Wales today. It negatively impacts cattle health, affects profitability and trade, and can decimate years of genetic improvement towards desirable production traits. It also places a significant burden on the health and well-being of affected farming families. Eradication of bTB requires the understanding of local transmission pathways to target effective disease control interventions. Traditional genotyping methods, such as spoligotyping and VNTR analysis, which have previously been used in Wales to understand the epidemiology of M. bovis, have lacked the discriminatory power to accurately identify local transmission pathways. Recently, whole-genome sequencing has replaced traditional genotyping methods for all M. bovis strains isolated from infected animals in Wales. In this study, we characterised the genomic diversity of M. bovis across Wales by analysing the genome sequence of all 379 M. bovis isolates obtained from culture-positive animals in Wales in 2021. Analyses uncovered three main clusters that are geographically distinct. A further three clusters containing fewer isolates were also geographically separated, two of which had particularly large SNP distances compared to the majority of other Welsh isolates, suggesting independent introductions of M. bovis strains that are not endemic to Wales. Fine-scale and epidemiologically relevant genetic structuring was identified within the six main clusters, indicating region-specific evolution, which can drive local disease dynamics. Finally, A number of SNPs in coding genes were identified that have the potential for important advantageous physiological consequences that may impact host-pathogen interactions and necessitate further investigation.

microbiology↗

bioRxiv2025

Struvite fertiliser offers a sustainable solution for recycling of phosphorus for crop growth without altering the soil resistome.

BackgroundThe reserves of rock phosphate, used to produce fertilisers, are being exhausted by the ever-increasing pressure to meet the food demands of a growing world population. Sustainable alternatives are urgently needed, and struvite, a renewable phosphorus fertiliser recovered from waste streams such as animal manure, offers a promising solution. However, other manure-derived fertilisers have been associated with the promotion of antimicrobial resistance (AMR) in soil, raising concerns about whether struvite might pose similar risks. Despite this, the impact of struvite on the soil resistome remains poorly understood. MethodsThis study assesses the AMR risk associated with Xiamen struvite (XIA), produced from piggery wastewater, compared with two commercial fertilisers: Crystal Green (CG) and Triple Super Phosphate (TSP), and an untreated control soil. Following a three-month fertilisation period, rhizospheric soils were analysed using mass spectrometry to investigate antimicrobial residues in the soil, and shotgun metagenomics and culture-based assays, and bacterial isolate characterisation to identify bacterial AMR linked to the treatments. Notably, this is the first study to apply culturing methods to investigate struvites impact on AMR. Results and conclusionsWhile XIA struvite soil fertilisation led to an increase in total culturable bacterial load, (CFU/g), there was no evidence of antimicrobial residues detected in soil, as confirmed by mass spectrometry. Metagenomic profiling further revealed no significant alterations to antimicrobial resistance genes (ARGs) across all treatments, including CG, TSP, and untreated controls. Culture-based analysis supported these findings, with tetracycline-resistant bacteria undetected and only minimal resistance observed to erythromycin, florfenicol, and neomycin. Although resistance levels to amoxicillin, streptomycin, and trimethoprim were higher in XIA-treated soils, this appeared to reflect a broader increase in microbial load rather than selective enrichment of resistant strains. These data suggest that piggery-derived struvite does not impose a clear AMR risk in soil and performs similarly to conventional fertilisers. While it appears to increase total bacterial load, there is no evidence of selective enrichment for AMR bacteria. However, variability in antimicrobial and ARG content across studies highlights the need for standardised production specifications for struvites to ensure safety and consistency. This study provides the most comprehensive assessment to date of struvites AMR implications, supporting its potential as a safe, sustainable fertiliser and contributing valuable insights to inform regulatory guidance.

microbiology↗

bioRxiv2025

PyamilySeq: Exposing the fragility of conventional gene (re)clustering and pangenomic inference methods

Pangenomics has become a central framework for exploring microbial diversity and evolution, enabling researchers to distinguish genes that define shared biological function from those that drive adaptation. However, this relies on clustering genes by sequence similarity, a process that is far less deterministic than often assumed. This study introduces PyamilySeq, a transparent and flexible toolkit designed to diagnose and quantify hidden biases within gene clustering and pangenome inference methodologies. Using PyamilySeq, we can see how clustering thresholds (often hardcoded and poorly documented) and paralog handling can substantially alter gene family composition. Surprisingly, even parameters unrelated to clustering, such as decimal precision (0.8 vs. 0.80), output selection, and even CPU and memory allocation, can alter gene family assignments, challenging the assumption that identical clustering thresholds yield consistent results. Furthermore, tools often fail to report biologically meaningful or representative sequences for gene families, undermining downstream analyses. These findings reveal systematic fragilities in gene clustering and pangenome construction and highlight that pangenomics is not merely a data-driven task but a methodological one, where transparency, reproducibility, and interpretability are as critical as biological insight. This work calls for a re-evaluation of how pangenomes are constructed and compared, and advocates for methodologies that make their assumptions explicit and their results verifiable.

bioinformatics↗

bioRxiv2025

Dietary inclusion of Asparagopsis taxiformis significantly reduces methane emissions in dairy ruminants by mechanistically altering vitamin B12 coenzyme production and other methanogenesis precursor pathways.

Ruminant products are consumed widely on a global level due to their high protein and micronutrient density. However, ruminant production is a major source of greenhouse gas emissions, particularly with respect to methane (CH), with ruminants contributing 33% of all anthropogenic CH emissions. CH is produced due to the natural fermentative processes undertaken by the complex rumen microbiome, primarily via the utilisation of hydrogen by rumen archaea to form CH. Previous studies have shown that feeding the red seaweed Asparagopsis taxiformis (ASP) to ruminants can reduce CHemissions from beef cattle by up to 80% (Roque et al., 2021). Nevertheless, the mechanism of action of this seaweed in terms of effects on the rumen microbiome is largely unknown, which is the main focus of this study. Six Nordic Red cows at 122 {+/-} 13.7 (mean {+/-} SD) days in milk were divided into 3 blocks by milk yield in Latin square design and fed grass silage and a commercial concentrate (60:40) either with or without 0.5% ASP on an organic matter basis. Rumen fluid was collected 19 days into each experimental period, with a holistic approach using both assembly and read mapping based approaches to interrogate taxonomic, functional, and ecological shifts applied to metagenomic data. We show that ASP reduces methane production not only through direct inhibition of methanogens but also by disrupting cobamide-dependent metabolic pathways and redirecting carbon flow toward pyruvate and propionate rather than acetate and methane. For the first time, specific enzymes involved in vitamin B12 (cobamide) biosynthesis are identified as suppressed by ASP, and microbial taxa contributing to these functional changes are elucidated showing both niche displacement and resilience within the rumen microbiome. These findings offer new mechanistic insight into how red seaweed supplementation modulates the rumen microbiome, supporting its potential role in sustainable ruminant production.

genomics↗

bioRxiv2024

Lineage-specific microbial protein prediction enables large-scale exploration of protein ecology within the human gut

Microbes use a range of genetic codes and gene structures, yet these are ignored during metagenomic analysis. This causes spurious protein predictions, preventing functional assignment which limits our understanding of ecosystems. To resolve this, we developed a lineage-specific gene prediction approach that uses the correct genetic code based on the taxonomic assignment of genetic fragments, removes partial predictions, and optimises prediction of small proteins. Applied to 9,634 metagenomes and 3,594 genomes from the human gut, this approach increased the landscape of captured expressed microbial proteins by 78.9%, including previously hidden functional groups. Optimised small protein prediction captured 3,772,658 small protein clusters, many with antimicrobial activity. Integration of the protein sequences and sample metadata into a tool, InvestiGUT, enables association of protein prevalence with host parameters. Accurate prediction of proteins is critical for understanding the functionality of microbiomes, hence this work will enhance understanding mechanistic interactions between microbes and hosts.

bioinformatics↗

bioRxiv2023

Accessory genes define species-specific pathways to antibiotic resistance.

BackgroundThe rise of antimicrobial resistance (AMR) is a growing concern globally and a deeper understanding of AMR gene carriage vs usage is vital for future responses to reduce the spread of AMR. Identification of AMR phenotype by laboratory-based assays are often hindered by difficulties in establishing cultures. This issue could be resolved by rapid computational assessment of an organisms genome, however, AMR gene finder tools are not intended to infer AMR phenotype which is likely to be a product of multiple gene interactions. MethodsTo understand the importance of multi-gene interactions to the relationship between AMR genotype and AMR phenotype, we applied machine learning approaches to 16,950 genomes from microbial isolates representing 28 different genera with 1.2 million corresponding laboratory-determined MICs for 23 different antibiotics. We then elucidated the genomic paths to phenotypic antimicrobial resistance with the aim of allowing for the development of rapid determination of AMR phenotype from genomes or even whole microbiomes. ResultsThe application of machine learning models resulted in a >1.5-fold increase in average prediction accuracy of AMR phenotype across the 23 antibiotic models. Interpretation of these models revealed 528 distinct genomic pathways to phenotypic resistance, many of which were species-specific and involved genes which have not previously been associated with AMR phenotype. This is the first study to demonstrate the utility of machine learning models in the prediction of AMR phenotype for a wide range of clinically relevant organisms and antibiotics. This could be applied as a rapid and affordable alternative to culture-based techniques, estimating taxonomy in addition to AMR phenotype, and providing real-time monitoring of multi-drug resistant pathogens. Availability and implementationO_ST_ABSContactC_ST_ABSldillon05@qub.ac.uk View supplementary information at this linkhttps://osf.io/cj4bq/?view_only=c0ee87b7609543b688953089be4c376f See Code Availability for scripts used.

bioinformatics↗

bioRxiv2022

FrameRate: learning the coding potential of unassembled metagenomic reads

MotivationMetagenomic assembly is a slow and computationally intensive process and despite needing iterative rounds for improvement and completeness the resulting assembly often fails to incorporate many of the input sequencing reads. This is further complicated when there is reduced read-depth and/or artefacts which result in chimeric assemblies both of which are especially prominent in the assembly of metagenomic datasets. Many of these limitations could potentially be overcome by exploiting the information content stored in the reads directly and thus eliminating the need for assembly in a number of situations. ResultsWe explored the prediction of coding potential of DNA reads by training a machine learning model on existing protein sequences. Named FrameRate, this model can predict the coding frame(s) from unassembled DNA sequencing reads directly, thus greatly reducing the computational resources required for genome assembly and similarity-based inference to pre-computed databases. Using the eggNOG-mapper function annotation tool, the predicted coding frames from FrameRate were functionally verified by comparing to the results from full-length protein sequences reconstructed with an established metagenome assembly and gene prediction pipeline from the same metagenomic sample. FrameRate captured equivalent functional profiles from the coding frames while reducing the required storage and time resources significantly. FrameRate was also able to annotate reads that were not represented in the assembly, capturing this missing information. As an ultra-fast read-level assembly-free coding profiler, FrameRate enables rapid characterisation of almost every sequencing read directly, whether it can be assembled or not, and thus circumvent many of the problems caused by contemporary assembly workflows. Availabilityhttps://github.com/NickJD/FrameRate Contactliuwei.wang@fu-berlin.de and nicholas@dimonaco.co.uk

bioinformatics↗

bioRxiv2022

StORF-Reporter: Finding Genes between Genes

Large regions of prokaryotic genomes are currently without any annotation, in part due to well-established limitations of annotation tools. For example, it is routine for annotation tools to misreport or completely omit genes using alternative start codons. Therefore, we present StORF-Reporter, a tool that takes an annotated genome and returns missing CDS genes from unannotated regions. StORF-Reporter consists of two parts. The first begins with the extraction of unannotated regions from an annotated genome. Next, Stop-ORFs (StORFs) are identified in these unannotated regions. StORFs are open reading frames that are delimited by stop codons and thus can capture those genes most often missing in genome annotations. We show that this methodology recovers genes missing from canonical genome annotations. We inspected the results of the genomes of model organisms, the pangenome of Escherichia coli, and a further 6,223 prokaryotic genomes of 179 genera from the Ensembl Bacteria database. StORF-Reporter was able to extend the core, soft-core and accessory gene-collections, identify novel gene families and extend families into additional genera. The high levels of sequence conservation observed between genera suggest that many of these StORF sequences are likely to be functional genes that must now be added to the canonical annotations.

bioinformatics↗

bioRxiv2021

No one tool to rule them all: Prokaryotic gene prediction tool performance is highly dependent on the organism of study

MotivationThe biases in Open Reading Frame (ORF) prediction tools, which have been based on historic genomic annotations from model organisms, impact our understanding of novel genomes and metagenomes. This hinders the discovery of new genomic information as it results in predictions being biased towards existing knowledge. To date users have lacked a systematic and replicable approach to identify the strengths and weaknesses of any ORF prediction tool and allow them to choose the right tool for their analysis. ResultsWe present an evaluation framework (ORForise) based on a comprehensive set of 12 primary and 60 secondary metrics that facilitate the assessment of the performance of ORF prediction tools. This makes it possible to identify which performs better for specific use-cases. We use this to assess 15 ab initio and model-based tools representing those most widely used (historically and currently) to generate the knowledge in genomic databases. We find that the performance of any tool is dependent on the genome being analysed, and no individual tool ranked as the most accurate across all genomes or metrics analysed. Even the top-ranked tools produced conflicting gene collections which could not be resolved by aggregation. The ORForise evaluation framework provides users with a replicable, data-led approach to make informed tool choices for novel genome annotations and for refining historical annotations. Availabilityhttps://github.com/NickJD/ORForise Contactnicholas@dimonaco.co.uk Supplementary informationSupplementary data are available at bioRxiv online.

bioinformatics↗

bioRxiv2020

Hacking the diversity of SARS-CoV-2 and SARS-like coronaviruses in human, bat and pangolin populations

In 2019, a novel coronavirus, SARS-CoV-2/nCoV-19, emerged in Wuhan, China, and has been responsible for the current COVID-19 pandemic. The evolutionary origins of the virus remain elusive and understanding its complex mutational signatures could guide vaccine design and development. As part of the international "CoronaHack" in April 2020 (https://www.coronahack.co.uk/), we employed a collection of contemporary methodologies to compare the genomic sequences of coronaviruses isolated from human (SARS-CoV-2;n=163), bat (bat-CoV;n=215) and pangolin (pangolin-CoV;n=7) available in public repositories. Following de novo gene annotation prediction, analyses of gene-gene similarity network, codon usage bias and variant discovery were undertaken. Strong host-associated divergences were noted in ORF3a, ORF6, ORF7a, ORF8 and S, and in codon usage bias profiles. Lastly, we have characterised several high impact variants (inframe insertion/deletion or stop gain) in bat-CoV and pangolin-CoV populations, some of which are found in the same amino acid position and may be highlighting loci of potential functional relevance.

genomics↗