Biology subjects

McInerney, J. O.

Publications and source records attributed to McInerney, J. O..

13 recordsLinked to original sources

bioRxiv2026

CRAIC: interactive interrogation of multiple sequence alignment uncertainty

Multiple sequence alignments (MSAs) are often treated as a solved problem. However, ambiguously or incorrectly aligned regions are the source of much error in phylogenetics and comparative genomics. CRAIC is a cross-platform desktop workbench that treats an alignment as a set of hypotheses to be stress-tested. CRAIC uses the posterior match probabilities of a three-state pair hidden Markov model (pair-HMM) to derive complementary views of alignment confidence. The five views are (i) a reliability overlay with live column and residue masking; (ii) a multi-aligner disagreement map, (iii) a local realignment sandbox, (iv) an interactive posterior explorer, and, where the true alignment is known, (v) a per-residue view of correctly predicted homologies. When present, MAFFT, MUSCLE, ProbCons, Clustal Omega, ClustalW and PRANK are integrated in the tool. In benchmarks the reliability score can locate misaligned columns, but while CRAIC can implement automatic filtering, it is primarily built for inspection and curation.

bioinformatics↗

bioRxiv2026

Synteny-aware microbial pangenome graphs reveal blueprints of genomic variation

Pangenomics quantifies the conserved and variable gene repertoire among genomes, but popular implementations ignore gene synteny. Graph-based approaches incorporate both gene homology and synteny, but become difficult to interpret due to pervasive rearrangements. Here we present network-pruning and graph-layout algorithms that enable interactive, synteny-aware quantification and visualization of gene conservation and variability. Applied to 29 genomes of the marine genus Undatipelagibacter (formerly SAR11 subclade Ia.3.VI), we find that genomic variability forms not a few hypervariable islands against a static backbone but a structured continuum, whose variable regions differ in scale, topology, function, and evolutionary character. Genome variation spans from ancient, specialized regions of hundreds of genes whose propensity to vary is conserved across genera, to single hypervariable genes shaped by epistatic co-selection with partners dispersed genome-wide, and shows that chromosomal context carries evolutionary information synteny-unaware pangenomics cannot capture, and some evolutionary processes act on entire functional subsystems throughout a pangenome.

microbiology↗

bioRxiv2026

Constructive neutral evolution explains the emergence of specialised ribosomes in diverse eukaryotes.

Throughout eukaryotic evolution, the structure of the ribosome has been highly conserved, featuring 80 common protein gene families. However, in many eukaryotes, paralogs of these proteins are present. "Specialised ribosomes" have been documented across diverse groups of eukaryotes where they play an important role in the regulation of translation of specific mRNAs. In the case of specialised ribosomes it has been documented that assembled ribosomes that contain specific paralogs can directly affect translational output. This has been proposed to contribute to the regulation of complex responses to environmental change and to coordinate cell-type specific physiology. This poses the question of whether ribosome specialisation principally emerges under an adaptive or neutral model of evolution. Using gene tree-species tree reconciliation, we test competing hypotheses regarding the evolutionary drivers of ribosome specialisation. We determine that examples of specialisation tend to emerge by independent duplication of the same ribosomal proteins in different lineages. We show that pathways to specialisation through paralog formation have arisen independent of: (i) paralog location within the 3D ribosome complex, and (ii) positive selection in these paralogs. We determine that the generalisable model of best fit for the evolution of paralog-mediated eukaryotic ribosomal specialisation is one of constructive neutral evolution. In lineages with small effective population sizes and increased complexity, the emergence and retention of ribosomal protein paralogs has provided the raw material for ratcheting and the emergence of translational regulation at the level of the ribosome.

evolutionary biology↗

bioRxiv2026

Species-specific transformer models of bacterial gene order and content for genomic surveillance tasks

Transformer models enable functionally meaningful representation of complex biological data, such as nucleotide or protein sequences. Existing foundation transformer models are trained on large multi-domain corpuses of unlabelled DNA or protein data, showing unmatched task generalisation. However, these foundation models are often outperformed on domain-specific tasks by models trained on taxonomically-constrained data, such as prokaryote gene annotation. By extension, species-specific transformer models hold promise for targeted analyses, given sufficient training data are available. Epidemiological analysis of bacterial pathogens exemplifies the use case of species-specific transformers, due to the wealth of genome data available, coupled with pathogen-specific analyses carried out during routine and outbreak surveillance. Here, we trained a transformer model, PanBART, on the gene content and gene order of two important and biologically distinct bacterial pathogens, Escherichia coli and Streptococcus pneumoniae, benchmarking against state-of-the-art non-transformer approaches for genomic epidemiology. We show PanBART learns representations of population structure in an unsupervised manner, and can be used to accurately assign genomes to biologically-meaningful sequence clusters. PanBART is also able to identify emergent lineages, differentiating them from pre-existing lineages, and can accurately predict genomes likely to uptake genes involved in antibiotic resistance before a transfer event has occurred. Finally, PanBART can be used to conduct co-selection analysis to identify pairs of genes likely to be evolving together. Our work demonstrates that species-specific transformer models can be employed in many critical public health scenarios. We lay the groundwork for wider application of such models in epidemiological analysis, and provide scenarios where such models excel.

bioinformatics↗

bioRxiv2025

Dissecting Phylogenetic Support: Unified Decay Indices, AU Tests, and Branch-Site Specific Visualizations.

Phylogenetic inference relies on robust measures of branch support to assess the reliability of evolutionary relationships. While bootstrap resampling and Bayesian probabilities have become the predominant support metrics in molecular phylogenetics, decay indices (also known as Bremer support) provide an alternative perspective by quantifying the amount of evidence required to collapse specific clades. We present panDecay, a unified software framework that extends the classical parsimony-based decay index concept to maximum likelihood (ML) and Bayesian inference approaches. panDecay automates the calculation of ML-based decay indices using log-likelihood differences, performs Approximately Unbiased (AU) statistical tests, and computes Bayesian decay indices through marginal likelihood comparisons using stepping-stone sampling. The software integrates with established phylogenetic tools (PAUP* and MrBayes) and provides comprehensive output including site-specific support analysis, multiple annotated tree formats, and detailed statistical reports. panDecay supports diverse data types (DNA, protein, and discrete morphological characters), offers flexible model specification, and provides parallel processing capabilities for computationally intensive Bayesian analyses. By unifying decay index calculations across parsimony, likelihood, and Bayesian frameworks within a single command-line interface, panDecay enables researchers to obtain complementary support metrics that provide deeper insights into phylogenetic signal strength and distribution across sequence alignments. The software is implemented in Python 3.8+, distributed via PyPI, and released under the MIT license.

evolutionary biology↗

bioRxiv2025

Genomic constraints shape the evolution of alternative routes to drug resistance in prokaryotes.

Antimicrobial resistance (AMR) is often modelled as the accumulation of resistance genes leading to multidrug resistance (MDR). We show that gene co-occurrence patterns in two opportunistic pathogens are consistent with fitness trade-offs that constrain which combinations of resistance mechanisms coexist. We applied a combined pangenomic and machine-learning analysis to 9,584 Escherichia coli genomes (99.2% phylogroup B2) and 7,057 Pseudomonas aeruginosa genomes. In E. coli, we identified eight cases of mutually exclusive gene pairs that independently predicted the same MDR phenotype, suggesting alternative routes to resistance whose components are typically not co-inherited. In a separate dataset of 352 strains with paired minimum inhibitory concentration (MIC) data, these dissociated combinations co-occurred more often in resistant than susceptible strains, consistent with the constraints being conditional on antibiotic selection. 33 gene pairs showed opposing association patterns between the two species, with combinations significantly associated in one species and significantly dissociated in the other (e.g. associated in E. coli and dissociated in P. aeruginosa, or vice versa). This indicates that genomic context modifies the contribution of individual genes to resistance phenotypes, and offers one explanation for the observation that 106 ARGs are present in >95% of strains yet do not predict resistance phenotype on their own. The findings are consistent with resistance evolution being shaped by fitness trade-offs and suggest that the dissociation patterns we identify could be targets for follow-up experimental work on resistance-associated fitness costs. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=128 SRC="FIGDIR/small/671315v2_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@7a717corg.highwire.dtl.DTLVardef@b08e64org.highwire.dtl.DTLVardef@154820forg.highwire.dtl.DTLVardef@126b9ee_HPS_FORMAT_FIGEXP M_FIG C_FIG IMPACT STATEMENTMultidrug resistance threatens global health; however, resistance acquisition remains poorly understood. We show that genomes cannot simply accumulate all available resistance mechanisms. Instead, species-specific grammatical rules render certain gene combinations typically incompatible. Analysing 9,584 Escherichia coli genomes, we identify eight mutually exclusive routes to resistance, demonstrating that resistance is more constrained than previously thought. Strikingly, 33 gene pairs that cooperate in E. coli actively disassociate in Pseudomonas aeruginosa, revealing that genomic context determines resistance outcomes. This explains why 106 resistance genes present in all strains fail to confer universal resistance, challenging current diagnostics. Our findings transform our understanding of resistance evolution from an unlimited accumulation model to a grammatically constrained pathways model, presenting new therapeutic strategies that exploit evolutionary dead ends.

bioinformatics↗

bioRxiv2025

Mammal placental phenotypes are predictable from microRNA repertoires.

Similar placental morphologies evolved multiple times independently in the history of mammal evolution1,2. Yet the genetic architecture that repeatedly guides distinct mammal lineages towards similar complex placental phenotypes has remained elusive. MicroRNAs (miRNAs), despite their diversity in mammals3-7 and known roles as developmental regulators8-10, remain under-examined as drivers of morphological innovation. We identified presence-absence patterns for 429 miRNA gene families across 398 mammalian genomes and discovered that placental phenotype is highly predictable from genomic miRNA repertoires (classification accuracy 74.5-95.8%). We identified 42 miRNA gene families significantly associated with placentation type, whose gene targets are enriched for developmental processes. Notably, convergent placental morphologies consistently involve identical miRNA families, revealing that evolution of this trait is constrained to predictable genetic pathways. We demonstrate that MIR-11986, uniquely associated with cotyledonary placentation, has tissue-specific expression in key reproductive tissues. MiRNA-mediated regulation therefore constrains placental morphological diversification into reproducible programs, offering insights into how genetic architecture shapes the predictability of convergent evolution. This striking pattern reveals a fundamental principle of evolution: that the miRNA regulatory networks available to control and guide complex placental morphological innovation are constrained and predictable.

genomics↗

bioRxiv2023

Bursts of novel composite gene families at major nodes in animal evolution

A molecular level perspective on how novel phenotypes evolve is contingent on our understanding of how genomes evolve through time, and of particular interest is how novel elements emerge or are lost. Mechanisms of protein evolution such as gene duplication have been well established. Studies of gene fusion events show they often generate novel functions and adaptive benefits. Identifying gene fusion and fission events on a genome scale allows us to establish the mode and tempo of emergence of composite genes across the animal tree of life, and allows us to test the repeatability of evolution in terms of determining how often composite genes can arise independently. Here we show that [~]5% of all animal gene families are composite, and their phylogenetic distribution suggests an abrupt, rather than gradual, emergence during animal evolution. We find that gene fusion occurs at a higher rate than fission (73.3% vs 25.4%) in animal composite genes, but many gene fusions (79% of the 73.3%) have more complex patterns including subsequent fission or loss. We demonstrate that nodes such as Bilateria, Euteleostomi, and Eutheria, have significantly higher rates of accumulation of composite genes. We observe that in general deuterostomes have a greater amount of composite genes as compared to protostomes. Intriguingly, up to 41% of composite gene families have evolved independently in different clades showing that the same solutions to protein innovation have evolved time and again in animals. Significance statementNew genes emerge and are lost from genomes over time. Mechanisms that can produce new genes include, but are not limited to, gene duplication, retrotransposition, de novo gene genesis, and gene fusion/fission. In this work, we show that new genes formed by fusing distinct homologous gene families together comprise a significant portion of the animal proteome. Their pattern of emergence through time is not gradual throughout the animal phylogeny - it is intensified on nodes of major transition in animal phylogeny. Interestingly, we see that evolution replays the tape frequently in these genes with 41% of gene fusion/fission events occurring independently throughout animal evolution.

evolutionary biology↗

bioRxiv2022

High Frequency of Dynamic Rearrangements in CRISPR Loci

AO_SCPLOWBSTRACTC_SCPLOWCRISPR-Cas immunization of prokaryotes proceeds by the acquisition of short fragments of invading DNA and integrating them into specific positions within the host genome in a process called adaptation. Adaptation is thought to be polarised, which suggests that CRISPR array spacer order reflects the recentness of the infection. The detailed processes through which CRISPR loci arise, and how they evolve are not completely clear. In this study, we collected 12,461 prokaryotic genomes, and using a combination of four different approaches and a series of conservative filters, we identified CRISPR arrays in 82.7% of Archaea and 40.6% of Bacteria. To understand spacer evolution in these CRISPR loci we firstly tracked point mutations in CRISPR repeats, and secondly, we carried out a comparative analysis of arrays that share multiple similar spacers. Both results indicate that CRISPR arrays are frequently dynamically rearranged. These findings are at odds with a model that suggests that spacer order is likely to reflect the recentness of infection. We conclude that the order of spacers in a CRISPR array, as well as the spacer content of the array, is likely to arise from a combination of events, such as insertion in the middle of the array, recombination within or between arrays, or Horizontal transfer of all or part of an array. We suggest these rearrangements are favoured by natural selection in complex and dynamic environments.

evolutionary biology↗

bioRxiv2021

Gene-gene relationships in an Escherichia coli accessory genome are linked to function and mobility

The pangenome contains all genes encoded by a species, with the core genome present in all strains and the accessory genome in only a subset. Coincident gene relationships are expected within the accessory genome, where the presence or absence of one gene is influenced by the presence or absence of another. Here, we analysed the accessory genome of an Escherichia coli pangenome consisting of 400 genomes from 20 sequence types to identify genes that display significant co-occurrence or avoidance patterns with one another. We present a complex network of genes that are either found together or that avoid one another more often than would be expected by chance, and show that these relationships vary by lineage. We demonstrate that genes co-occur by function, and that several highly connected gene relationships are linked to mobile genetic elements. We find that genes are more likely to co-occur with, rather than avoid, another gene, suggesting that cooperation is more common than conflict in the accessory genome. This work furthers our understanding of the dynamic nature of prokaryote pangenomes and implicates both function and mobility as drivers of gene relationships. Data summaryAll Supplementary Data files and the Python scripts used in the analyses are available at doi.org/10.17639/nott.7103. Impact statementThe pangenome of a species encompasses the core genes encoded by all genomes, as well as the accessory genes found in only a subset. Much remains to be understood about the relationships and interactions between accessory genes; in particular, what drives pairs of genes to appear together in the same genome, or what prevents them from being in the same genome together, more often than expected by chance. How these co-occurrence and avoidance relationships develop, and what effect they have on the dynamics and evolution of the pangenome as a whole, is largely unknown. Here, we present a springboard for understanding prokaryote pangenome evolution by uncovering significant gene relationships in a model Escherichia coli pangenome. We identify mobile genetic elements and the sharing of common function as possible driving forces behind the co-occurrence of accessory genes. Furthermore, this work offers an extensive dataset from which gene relationships could be identified for any gene of interest in this E. coli accessory genome, providing a rich resource for the community.

evolutionary biology↗

bioRxiv2020

Evidence for selection in a prokaryote pangenome

A pangenome is the complete set of genes (core and accessory) present in a phylogenetic clade. We hypothesize that a pangenomes accessory gene content is structured and maintained by selection. To test this hypothesis, we interrogated the genomes of 40 Pseudomonas genomes for statistically significant coincident (i.e. co-occurring/avoiding) gene patterns. We found that 86.7% of common accessory genes are involved in [≥]1 coincident relationship. Further, genes that co-occur and/or avoid each other - but are not vertically or horizontally co-inherited - are more likely to share Gene Ontology categories, are more likely to be simultaneously transcribed, and are more likely to produce interacting proteins, than would be expected by chance. These results are not due to coincident genes being adjacent to one another on the chromosome. Together, these findings suggest that the accessory genome is structured into interacting sets of genes co-selected to function together within a given strain. Given the simi larity of the Pseudomonas pangenome with open pangenomes of other prokaryotic species, we speculate that these results are generalizable.

microbiology↗

bioRxiv2019

Coinfinder: Detecting Significant Associations and Dissociations in Pangenomes

2.The accessory genes of prokaryote and eukaryote pangenomes accumulate by horizontal gene transfer, differential gene loss, and the effects of selection and drift. We have developed Coinfinder, a software program that assesses whether sets of homologous genes (gene families) in pangenomes associate or dissociate with each other (i.e. are "coincident") more often than would be expected by chance. Coinfinder employs a user-supplied phylogenetic tree in order to assess the lineage-dependence (i.e. the phylogenetic distribution) of each accessory gene, allowing Coinfinder to focus on coincident gene pairs whose joint presence is not simply because they happened to appear in the same clade, but rather that they tend to appear together more often than expected across the phylogeny. Coinfinder is implemented in C++, Python3, and R and is freely available under the GPU license from https://github.com/fwhelan/coinfinder. 3. Impact statementCoinfinder identifies genes that co-occur (associate) or avoid (dissociate) with each other across the accessory genomes of a pangenome of interest. Genes that associate or dissociate more often than expected by chance, suggests that those genes have a connection (attraction or repulsion) that is interesting to explore. Identification of these groups of genes will further the fields understanding of the importance of accessory genes. Coinfinder is a freely available, open-source software which can identify gene patterns locally on a personal computer in a matter of hours. 4. Data summaryO_LICoinfinder is freely available at https://github.com/fwhelan/coinfinder. C_LIO_LIA list of the Identifiers of the genomes used within as well as all input/output files are available at https://github.com/fwhelan/coinfinder-manuscript. C_LI The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files.

microbiology↗

bioRxiv2019

Reconstructing and Analysing The Genome of The Last Eukaryote Common Ancestor to Better Understand the Transition from FECA to LECA.

It is still a matter of debate whether the First Eukaryote Common Ancestor (FECA) arose from the merger of an archaeal host with an alphaproteobacterium, or was a proto-eukaryote with significant eukaryotic characteristics way before endosymbiosis occurred. The Last Eukaryote Common Ancestor (LECA) as its descendant is thought to be an entity that possessed functional and cellular complexity comparable to modern organisms. The precise nature and physiology of both of these organisms has been a long-standing, unanswered question in evolutionary and cell biology. Recently, a much broader diversity of eukaryotic genomes has become available and this means we can reconstruct early eukaryote evolution with a greater deal of precision. Here, we reconstruct a hypothetical genome for LECA from modern eukaryote genomes. The constituent genes were mapped onto 454 pathways from the KEGG database covering cellular, genetic, and metabolic processes across six model species to provide functional insights into its capabilities. We reconstruct a LECA that was a facultatively anaerobic, single-celled organism, similar to a modern Protist possessing complex predatory and sexual behaviour. We go on to examine how much of these capabilities arose along the FECA-to-LECA transition period. We see a at least 1,554 genes gained by FECA during this evolutionary period with extensive remodelling of pathways relating to lipid metabolism, cellular processes, genetic information processing, protein processing, and signalling. We extracted the BRITE classifications for the genes from the KEGG database, which arose during the transition from FECA-to-LECA and examine the types of genes that saw the most gains and what novel classifications were introduced. Two-thirds of our reconstructed LECA genome appears to be prokaryote in origin and the remaining third consists of genes with functional classifications that originate from prokaryote homologs in our LECA genome. Signal transduction and Post Translational Modification elements stand out as the primary novel classes of genes developed during this period. These results suggest that largely the eukaryote common ancestors achieved the defining characteristics of modern eukaryotes by primarily expanding on prokaryote biology and gene families.

evolutionary biology↗