Biology subjects

Gardner, P. P.

Publications and source records attributed to Gardner, P. P..

17 recordsLinked to original sources

bioRxiv2026

What Makes a Good Vaccine Antigen Target? Defining Key Features and Predicting Candidates in the Staphylococcus aureus Proteome

The rapid advancement of computational methods is transforming vaccine development by enabling faster, data-driven identification of promising antigens. In this study, we applied an in-silico pipeline to assess a broad set of sequence, structure, localisation, and immunology-derived features and determine which most effectively discriminate antigens from non-antigens in bacteria. Using these insights, we identified bacterial proteins with high potential as vaccine antigens. Applied to Staphylococcus aureus, this approach prioritized 304 candidate antigens, highlighting SSLs, nutrient acquisition factors, and cell wall-associated enzymes. While these findings demonstrate the potential of bioinformatics-guided antigen discovery, experimental validation remains essential. This work underscores the growing role of integrated computational and machine-learning approaches in accelerating next-generation vaccine design.

bioinformatics↗

bioRxiv2026

Genomic indicators of gene function: A systematic assessment of the human genome

Proteins and non-coding RNAs are functional products of the genome that are central for crucial cellular processes. With recent technological advances, researchers can sequence genomes in the thousands and probe numerous genomic activities of many species and conditions. Such studies have identified thousands of potential proteins, RNAs and associated activities. However there are conflicting interpretations of the results and therefore which regions of the genome are "functional". Here we investigate the relative strengths of associations between coding and non-coding gene functionality and genomic features, by comparing reliably annotated functional genes to non-genic regions of the genome. We find that the strongest and most consistent association between functional genes and genomic features are transcriptional activity and evolutionary conservation. We also evaluated sequence-based statistics, genomic repeats, epigenetic and population variation data. Other features strongly associated with function include histone marks, chromatin accessibility, genomic copy-number, and sequence alignment statistics such as coding potential and covariation. We also identify potential issues with SNP annotations in short non-coding RNAs, as some highly conserved ncRNAs have significantly higher than expected SNP densities. Our results demonstrate the importance of evolutionary conservation and transcription activity for indicating protein-coding and non-coding gene function. Both should be taken into consideration when differentiating between functional sequences and biological or experimental noise.

genomics↗

bioRxiv2024

Assessing the robustness of human ncRNA notation at HGNC

The HUGO Gene Nomenclature Committee (HGNC) is the only worldwide authority that assigns standardised nomenclature to human genes (1). All studies related to the human genome and genes worldwide should adhere to HGNC-approved gene names and symbols, emphasizing the importance of precise classification and naming. Recent studies have revealed the functional and clinical relevance of RNU2-2P, which is linked to neurodevelopmental disorders and cancer (2-4), underscoring the need to reassess the classification of pseudogenes and functional non-coding RNA genes. In this study, we explore the conservation and expression of genes from 15 small ncRNA families, including U1, U2, U4, U5, U6, U4ATAC, U6ATAC, U11, U12, Vault tRNA (VTRNA), Y RNA, tRNA, 7SL, U7, and 7SK, to identify non-coding RNA-derived pseudogenes that are under strong negative selection in the human genome. Our findings highlight three highly conserved and expressed pseudogenes--RNU2-2P, RNU1-27P, and RNU1-28P--that are likely misclassified, as existing evidence suggests they may play a role in disease research. This warrants a reevaluation of their status as pseudogenes. Additionally, we identified RNU5F-1, a functional copy of RNU5, which is lowly conserved and expressed, yet its classification as a functional gene raises questions about its potential role. Furthermore, other pseudogenes and functional ncRNAs that could also be misclassified were identified, suggesting the necessity for further experimental and clinical examination.

genomics↗

bioRxiv2024

A Bioinformatician, Computer Scientist, and Geneticist lead bioinformatic tool development - which one is better?

The development of accurate bioinformatic software tools is crucial for the effective analysis of complex biological data. This study examines the relationship between the academic department affiliations of authors and the accuracy of the bioinformatic tools they develop. By analyzing a corpus of previously benchmarked bioinformatic software tools, we mapped bioinformatic tools to the academic fields of the corresponding authors and evaluated tool accuracy by field. Our results suggest that "Medical Informatics" outperforms all other fields in bioinformatic software accuracy, with a mean proportion of wins in accuracy rankings exceeding the null expectation. In contrast, tools developed by authors affiliated with "Bioinformatics" and "Engineering" fields tend to be less accurate. However, after correcting for multiple testing, no result is statistically significant (p > 0.05). Our findings reveal no strong association between academic field and bioinformatic software accuracy. These findings suggest that the development of interdisciplinary software applications can be effectively undertaken by any department with sufficient resources and training.

bioinformatics↗

bioRxiv2024

Flawed machine-learning confounds coding sequence annotation

BackgroundDetecting protein-coding genes in nucleotide sequences is a significant challenge for understanding genome and transcriptome function, yet the reliability of bioinformatic tools for this task remains largely unverified. This is despite some tools being available for several decades, and widely used for genome and transcriptome annotation. ResultsWe perform an assessment of nucleotide sequence and alignment-based de novo protein-coding detection tools. The controls we use exclude any previous training dataset and include coding exons as a positive set and length-matched intergenic and shuffled sequences as negative sets. Our work demonstrates that several widely used tools are neither accurate nor computationally efficient for the protein-coding sequence detection problem. In fact, just three of nine tools significantly outperformed a naive scoring scheme. Furthermore, we note a high discrepancy between self-reported accuracies and the accuracy achieved in our study. Our results show that the extra dimension from conserved and variable nucleotides in alignments have a significant advantage over single sequence approaches. ConclusionsThese results highlight significant limitations in existing protein-coding annotation tools that are widely used for lncRNA annotation. This shows a need for more robust and efficient approaches to training and assessing the performance of tools for identifying protein-coding sequences. Our study paves the way for future advancements in comparative genomic approaches, and we hope will popularise more robust approaches to genome and transcriptome annotation.

bioinformatics↗

bioRxiv2022

High-throughput transposon mutagenesis in the family Enterobacteriaceae reveals core essential genes and rapid turnover of essentiality

The Enterobacteriaceae are a scientifically and medically important clade of bacteria, containing the gut commensal and model organism Escherichia coli, as well as several major human pathogens including multiple serovars of Salmonella enterica and Klebsiella pneumoniae. Essential gene sets have been determined for several members of the Enterobacteriaceae, with the E. coli Keio single-gene deletion library often regarded as a gold standard for gene essentiality studies. However, it remains unclear how gene essentiality varies between related strains and species. To investigate this, we have assembled a collection of thirteen sequenced high-density transposon mutant libraries from five genera within the Enterobacteriaceae. We first benchmark a number of gene essentiality prediction approaches, investigate the effects of transposon density on essentiality prediction, and identify biases in transposon insertion sequencing data. Based on these investigations we develop a new classifier for gene essentiality. Using this new classifier, we define a core essential genome in the Enterobacteriaceae of 201 universally essential genes, and reconstruct an ancestral essential gene set of 296 genes. Despite the presence of a large cohort of variably essential genes, surprisingly we find an absence of evidence for genus-specific essential genes. A clear example of this sporadic essentiality is given by the set of genes regulating the {sigma}E extracytoplasmic stress response, which appears to have independently become essential multiple times in the Enterobacteriaceae. Finally, we compare our essential gene sets to the natural experiment of gene loss in obligate insect endosymbionts that have emerged from within the Enterobacteriaceae. This isolates a remarkably small set of genes absolutely required for survival, and uncovers several instances of essential stress responses masked by redundancy in free-living bacteria.

microbiology↗

bioRxiv2022

LazyPair: scalable prediction of protein-protein interactions and interaction types

MotivationAlmost all cellular processes require protein-protein interactions. Common interaction types include binding, post-translational modifications, and catalysis. However, existing prediction tools do not take these interaction types into account and do not scale well on proteome-wide prediction. ResultsHere we show that a random forest classifier trained on per-residue physicochemical and biochemical properties is useful for predicting protein-protein interactions. Counterintuitively, we find that training random forests by individual interaction types improves accuracy. Furthermore, a combination of these specialised classifiers improves generalisability. We call our protein-protein interaction prediction tool LazyPair. More importantly, LazyPair outperforms the state-of-the-art in accuracy, generalisability and scalability. Availability and implementationLazyPair and the source code and data for reproducing our analysis are freely available at https://github.com/Gardner-BinfLab/PPI_Analysis_2022 and https://doi.org/10.5281/zenodo.6071630. The web server version and the source code are freely available at https://tisigner.com/lazypair/ and https://github.com/Gardner-BinfLab/TISIGNER-ReactJS, respectively.

bioinformatics↗

bioRxiv2021

Investigating bacterial ribosomal sequence variation in regards to future structural and antibiotic research.

Ribosome-targeting antibiotics comprise over half of antibiotics used in medicine, but our fundamental knowledge of their binding sites is derived primarily from ribosome structures from non-pathogenic species. These include Thermus thermophilus, Deinococcus radiodurans and Haloarcula marismortui, as well as commensal and pathogenic Escherichia coli. Advancements in electron cryomicroscopy have allowed for the determination of more ribosome structures from pathogenic bacteria, with each study highlighting species-specific differences that had not been observed in the non-pathogenic structures. These observed differences suggest that more novel ribosome structures, particularly from pathogens, are required for a more accurate understanding of the level of diversity of the entire bacterial ribosome, potentially leading to potential advancements in antibiotic research. In this study, high accuracy covariance and hidden Markov models were used to annotate ribosomal RNA and protein sequences respectively from genomic sequence, allowing us to determine the underlying ribosomal sequence diversity using phylogenetic methods. This analysis provided evidence that the current non-pathogenic ribosome structures are not sufficient representatives of some pathogenic bacteria, such as Campylobacter pylori, or of whole phyla such as Bacteroidetes. Significance StatementThe growing number of antibiotic resistance pathogenic bacteria are of critical concern to the health profession. Many of the current classes of antibiotics target the bacterial ribosome, the protein making factory for these species. However, much of our knowledge of the bacterial ribosome is based upon non-pathogenic bacteria that are highly divergent from the major pathogens of concern. We have analysed the genetic variation of the RNA and protein components of all available bacterial ribosomes. This has led us to identify the highest priority groups of bacteria that would benefit most from further analysis of their ribosome structures, from both a medical and evolutionary perspective.

biochemistry↗

bioRxiv2020

Annotating eukaryotic and toxin-specific signal peptides using Razor

MotivationSignal peptides are responsible for protein transport and secretion and are ubiquitous to all forms of life. The annotation of signal peptides is important for understanding protein translocation and toxin secretion and evolution. ResultsHere we explore the features of these signal sequences from eukaryotic proteins. Strikingly, we find that the signal peptides from secretory toxins have common features across kingdoms, supporting the idea of horizontal gene transfer or convergence of toxin genes across kingdoms. We leverage these features to build Razor, a simple yet powerful tool specialised in identifying signal peptides from toxins using the first 23 N-terminal residues. We demonstrate the usability of Razor by analysing all the sequences reviewed by UniProt. Indeed, Razor is able to identify toxins using their N-terminal sequences only. Strikingly, we also discover that many defensive proteins across kingdoms harbour a toxin-like signal peptide; some of these defensive proteins have emerged through convergent evolution, e.g. defensin and defensin-like protein families, and phospholipase families. In sum, Razor uses an approach independent of homology search to identify novel and known toxin classes across species using N-terminal residues. Availability and implementationRazor is available as a web application (https://tisigner.com/razor) and a command-line tool (https://github.com/Gardner-BinfLab/Razor).

bioinformatics↗

bioRxiv2020

Features of Functional Human Genes.

Proteins and non-coding RNAs are functional products of the genome that carry out the bulk of crucial cellular processes. With recent technological advances, researchers can sequence genomes in the thousands as well as probe for specific genomic activities of multiple species and conditions. These studies have identified thousands of potential proteins, RNAs and associated activities, however there are conflicting conclusions on the functional implications depending upon the burden of evidence researchers use, leading to diverse interpretations of which regions of the genome are "functional". Here we investigate the association between gene functionality and genomic features, by comparing established functional protein-coding and non-coding genes to non-genic regions of the genome. We find that the strongest and most consistent association between functional genes and any genomic feature is evolutionary conservation and transcriptional activity. Other strongly associated features include sequence alignment statistics, such as maximum between-site covariation. We have also identified some concerns with 1,000 Genomes Project and Genome Aggregation Database SNP densities, as short non-coding RNAs tend to have greater than expected SNP densities. Our results demonstrate the importance of evolutionary conservation and transcription for sequence functionality, which should both be taken into consideration when differentiating between functional sequences and noise.

bioinformatics↗

bioRxiv2020

Protein solubility is controlled by global structural flexibility

MotivationRecombinant protein production is a widely used technique in the biotechnology and biomedical industries, yet only a quarter of target proteins are soluble and can therefore be purified. ResultsWe have discovered that global structural flexibility, which can be modeled by normalised B-factors, accurately predicts the solubility of 12,216 recombinant proteins expressed in Escherichia coli. We have optimised B-factors, and derived a new set of values for solubility scoring that further improves prediction accuracy. We call this new predictor the Solubility-Weighted Index (SWI). Importantly, SWI outperforms many existing protein solubility prediction tools. Furthermore, we have developed SoDoPE (Soluble Domain for Protein Expression), a web interface that allows users to choose a protein region of interest for predicting and maximising both protein expression and solubility. AvailabilityThe SoDoPE web server and source code are freely available at https://tisigner.com/sodope and https://github.com/Gardner-BinfLab/TISIGNER-ReactJS, respectively. The code and data for reproducing our analysis can be found at https://github.com/Gardner-BinfLab/SoDoPE_paper2020.

bioinformatics↗

bioRxiv2019

A nucleus-forming jumbophage evades CRISPR-Cas DNA targeting but is vulnerable to type III RNA-based immunity

CRISPR-Cas systems provide bacteria with adaptive immunity against bacteriophages1. However, DNA modification2,3, the production of anti-CRISPR proteins4,5 and potentially other strategies enable phages to evade CRISPR-Cas. Here we discovered a Serratia jumbophage that evaded type I CRISPR-Cas systems, but was sensitive to type III immunity. Jumbophage infection resulted in a nucleus-like structure enclosed by a proteinaceous phage shell - a phenomenon only reported recently for distantly related Pseudomonas phages6,7. All three native CRISPR-Cas complexes in Serratia - type I-E, I-F and III-A - were spatially excluded from the phage nucleus and phage DNA was not targeted. However, the type III-A system still arrested jumbophage infection by targeting phage RNA in the cytoplasm in a process requiring Cas7, Cas10 and an accessory nuclease. Type III, but not type I, systems frequently targeted nucleus-forming jumbophages that were identified in global viral sequence datasets. These findings explain why many bacteria harbour both RNA- and DNA-targeting CRISPR-Cas systems1,8. Together, our results indicate that jumbophage nucleus-like compartments serve as a barrier to DNA-targeting, but not RNA-targeting defences, and that this phenomenon is widespread amongst jumbophages.

microbiology↗

bioRxiv2019

Highly accessible translation initiation sites are predictive of successful heterologous protein expression

Recombinant protein production is a key process in generating proteins of interest in the pharmaceutical industry and biomedical research. However, about 50% of recombinant proteins fail to be expressed in a variety of host cells. To address this problem, we modified up to the first nine codons of messenger RNAs with synonymous substitutions and showed that protein levels can be tuned. These modifications alter the accessibility of translation initiation sites. We also reveal the dynamics between accessibility, gene expression, and turnovers using a coarse-grained simulation.

bioinformatics↗

bioRxiv2018

Evolutionary, structural and functional explorations of non-coding RNA and protein genetic robustness

The reactions of functional molecules like proteins and RNAs to mutation affect both host cell viability and biomolecular evolution. These molecules are considered robust if function is maintained despite mutations. Proteins and RNAs have different structural and functional characteristics that affect their robustness, and to date, comparisons between them have been theoretical. In this work, we test the relative mutational robustness of RNA and protein pairs using three approaches: evolutionary, structural, and functional. We compare the nucleotide diversities of functional RNAs with those of matched proteins. Across different levels of conservation, we found the nucleotide-level variations between the biomolecules largely overlapped, with proteins generally supporting more variation than matched RNAs. We then directly tested the robustness of the protein and RNA pairs with in vitro and in silico mutagenesis of their respective genes. The in silico experiments showed that proteins and RNAs reacted similarly to point mutations and insertions or deletions, yet proteins are slightly more robust on average than RNAs. In vitro, mutated fluorescent RNAs retained greater levels of function than the proteins. Overall this suggests that proteins and RNAs have remarkably similar degrees of robustness, with the average protein having moderately higher robustness than RNA as a group.\n\nSignificance StatementThe ability of proteins and non-coding RNAs to maintain function despite mutations in their respective genes is known as mutational robustness. Robustness impacts how molecules maintain and change phenotypes, which has a bearing on the evolution and the origin of life as well as influencing modern biotechnology. Both protein and RNA have mechanisms that allow them to absorb DNA-level changes. Proteins have a redundant genetic code and non-coding RNAs can maintain structure and function through flexible base-pairing possibilities. The few theoretical treatments comparing protein and RNA robustness differ in their conclusions. In this experimental comparison of protein and RNA, we find that they have remarkably similar degrees of overall genetic robustness.

molecular biology↗

bioRxiv2018

Convergent regulatory evolution and the origin of flightlessness in palaeognathous birds

The relative roles of regulatory and protein evolution in the origin and loss of convergent phenotypic traits is a core question in evolutionary biology. Here we combine phylogenomic, epigenomic and developmental data to show that convergent evolution of regulatory regions, but not protein-coding genes, is associated with flightlessness in palaeognathous birds, a classic example of a convergent phenotype. Eleven new genomes, including a draft genome from an extinct moa, resolve palaeognath phylogeny and show that the incidence of independent, convergent accelerations among 284,000 conserved non-exonic elements is significantly more frequent in ratites than other bird lineages. Ratite-specific acceleration of conserved regions and measures of open chromatin across eight tissues in the developing chick identify candidate regulatory regions that may have modified or lost function in ratites. Enhancer activity assays conducted in the early developing chicken forelimb confirm that volant versions of a conserved element in the first intron of the TEAD1 gene display conserved enhancer activity, whereas an accelerated flightless version fails to drive reporter gene expression. Our results show that convergent molecular changes associated with loss of flight are largely regulatory in nature.

evolutionary biology↗

bioRxiv2017

Machine learning identifies signatures of host adaptation in the bacterial pathogen Salmonella enterica

Emerging pathogens are a major threat to public health, however understanding how pathogens adapt to new niches remains a challenge. New methods are urgently required to provide functional insights into pathogens from the massive genomic data sets now being generated from routine pathogen surveillance for epidemiological purposes. Here, we measure the burden of atypical mutations in protein coding genes across independently evolved Salmonella enterica lineages, and use these as input to train a random forest classifier to identify strains associated with extraintestinal disease. Members of the species fall along a continuum, from pathovars which cause gastrointestinal infection and low mortality, associated with a broad host-range, to those that cause invasive infection and high mortality, associated with a narrowed host range. Our random forest classifier learned to perfectly discriminate long-established gastrointestinal and invasive serovars of Salmonella. Additionally, it was able to discriminate recently emerged Salmonella Enteritidis and Typhimurium lineages associated with invasive disease in immunocompromised populations in sub-Saharan Africa, and within-host adaptation to invasive infection. We dissect the architecture of the model to identify the genes that were most informative of phenotype, revealing a common theme of degradation of metabolic pathways in extraintestinal lineages. This approach accurately identifies patterns of gene degradation and diversifying selection specific to invasive serovars that have been captured by more labour-intensive investigations, but can be readily scaled to larger analyses.

genomics↗

bioRxiv2017

Identifying accurate metagenome and amplicon software via a meta-analysis of benchmarking studies

Environmental DNA sequencing has rapidly become a widely-used technique for investigating a range of questions, particularly related to health and environmental monitoring. There has also been a proliferation of bioinformatic tools for analysing metagenomic and amplicon datasets, which makes selecting adequate tools a significant challenge. A number of benchmark studies have been undertaken; however, these can present conflicting results. We have applied a robust Z-score ranking procedure and a network meta-analysis method to identify software tools that are generally accurate for mapping DNA sequences to taxonomic hierarchies. Based upon these results we have identified some tools and computational strategies that produce robust predictions.

bioinformatics↗