Biology subjects

Finn, R. D.

Publications and source records attributed to Finn, R. D..

At least 19 recordsLinked to original sources

bioRxiv2026

MAP: a comprehensive pipeline for mobilome annotation and cargo gene characterisation in prokaryotic (meta)genomic assemblies

Mobile genetic elements (MGEs) drive horizontal gene transfer in prokaryotes, disseminating antimicrobial resistance genes (ARGs), virulence factors (VFs) and biosynthetic gene clusters (BGCs). Given their importance, there is a pressing need for a single, open source tool that annotates the MGE repertoire together with its functional cargo. We present MAP (Mobilome Annotation Pipeline), a Nextflow pipeline that predicts plasmids, viral sequences, prophages, integrons, insertion sequences, transposons, integrative and conjugative elements, and non-autonomous compositional outliers, removes redundant predictions, and labels genes within MGE boundaries. MAP outputs a GFF3 formatted file, a FASTA file of MGE sequences, and a combined report placing ARGs, VFs, toxins and BGCs in their mobilome context, enabling the identification of composite elements such as ARG-carrying integrons within plasmids. We demonstrate its use on genomes from the MGnify soil genome catalogue.

bioinformatics↗

bioRxiv2026

Biological meaning in protein embedding space is resolution-dependent

Protein language model embeddings are increasingly used to organise biological sequences, yet how biological meaning is encoded within embedding neighbourhoods remains poorly understood. Using two independent hierarchical enzyme systems, carbohydrate-active enzymes and peptidases, we investigated how biological interpretation changes across embedding organisations aligned to different levels of biological hierarchy. Different embedding organisations give rise to distinct neighbourhood semantics. When aligned to membership-boundary resolution, embeddings robustly separated artefacts and unrelated proteins from members of the target category. However, embeddings aligned to functional-grouping resolution maintained compositional neighbourhood structure for multi-domain proteins spanning more than one functional or catalytic group. Finally, embeddings aligned to local-family resolution recovered compact family-like neighbourhoods, including families withheld from training, while weakening broader membership-boundary and functional-grouping relationships. Moreover, embeddings optimised toward the same level of biological organisation retain different biological relationships depending on optimisation trajectory employed. Together, our results show that proximity in protein embedding space has no fixed biological interpretation. Instead, biological meaning emerges across embedding resolutions through selective preservation of different forms of biological organisation.

bioinformatics↗

bioRxiv2026

Machine learning-based prediction of memory requirements for metagenomic assembly in high-performance computing environments

Metagenomic assembly can be a computationally intensive step in microbiome analysis, with memory requirements that vary widely depending on input data characteristics. In workflow systems like Galaxy and large-scale platforms like MGnify, which run thousands of heterogeneous jobs, inaccurate memory allocation drives job failures and costly retries when underestimated, and reduces throughput when overestimated. Current approaches rely primarily on heuristic rules based on input file size or sample metadata, which often fail to generalize across diverse datasets. In this study, we present a machine learning-based framework for predicting memory requirements of metagenomic assembly using metaSPAdes. We analyzed 300 assembly jobs from diverse biomes and evaluated 18 predictive models using combinations of input file size, biome classification, and sequence-derived k-mer features. K-mer profiles were computed from raw sequencing data and summarized into statistical descriptors capturing sequence complexity and diversity. Model performance was assessed using both conventional regression metrics and a production-oriented cost function that accounts for retry policies and resource waste in high-performance computing environments. Our results show that machine learning models can outperform commonly used heuristics. In particular, models incorporating biome information achieved the best performance and can be tuned to favor conservative predictions that reduce job failure rates. Simpler models based solely on input file size also performed competitively, offering a practical alternative for systems with limited feature availability. When evaluated under realistic workload distributions, predictive approaches reduced total memory waste by several million gigabyte-hours per 1,000 jobs compared to static allocation strategies. These findings demonstrate that data-driven resource prediction can substantially improve efficiency in metagenomic workflows. The proposed framework is adaptable to different computational environments and provides a foundation for integrating predictive resource allocation into large-scale bioinformatics platforms beyond Galaxy.

microbiology↗

bioRxiv2026

Transposable Element Diversification and the Evolution of Peltigerales Lichen Symbionts

Lichens are composite organisms formed through the symbiotic association between fungi, algae and/or bacteria. Multiple independent origins of the lichenized lifestyle have been reported in both fungal and algal lineages, but the molecular mechanisms and evolution underpinning these symbiotic relationships remain largely unknown. In this study, we performed long-read metagenomic sequencing on 11 Peltigerales lichen species to characterize the genomic content of lichen symbionts via metagenome assembled genomes (MAGs). Peltigerales genomes generated in this work represent the largest Lecanoromycetes genome sequenced to date, driven by high transposable element content. Transposable elements (TEs) are known to drive genome evolution in other symbioses but have been underexplored in lichen symbionts due technological limitations. Transcriptomics revealed that many genes associated with adaptations to the lichenized lifestyle are associated with TEs suggesting that they may play a key role in the evolution of lichenization.

genomics↗

bioRxiv2026

Shared antimicrobial resistance gene families across gut microbiomes in indigenous Orang Asli communities of Peninsular Malaysia

Indigenous groups across the world have been underrepresented in the ongoing efforts to map human microbiome diversity. This study investigates the gut microbiome and resistome diversities of Indigenous Orang Asli communities in Malaysia with different lifestyles and degree of urbanisation. Healthy adult participants (>18 years old) were recruited from three Indigenous communities - Temuan (urban), Temiar (semi-urban), and Jahai (rural hunter-gatherer), as well as urban Malays. Stool samples were collected on dry ice and analysed using shotgun metagenomic sequencing. Microbial alpha diversity (Shannon) decreased but antibiotic resistance genes (ARGs) diversity increased as the degree of urbanisation in the groups increased (P < 0.05). The groups contributed to 13% of the variation observed in the microbial composition (PERMANOVA, P = 0.001), and 14.5% in resistome composition (PERMANOVA, P = 0.001). Romboutsia timonensis was significantly depleted in Jahai compared to Malay (FDR = 0.04). Shared ARGs conferring resistance to beta-lactams (cfxA), tetracyclines (tet), and macrolides (erm) were observed across all groups, irrespective of geographical location, ethnicity and lifestyle. This study provides initial characterisation of the gut microbiome and resistome of three Indigenous Orang Asli communities in Malaysia. Our findings offer foundational evidence of antimicrobial resistance patterns and underscores the need for broader inclusion of underrepresented populations in national surveillance and stewardship efforts. Impact statementOur study generated one of the first integrated microbiome-resistome analyses across an urbanisation gradient of Malaysian gut microbiome. Shotgun metagenomic profiling of hard-to-access Indigenous cohorts enabled taxonomic classification for most reads, supporting high-resolution microbial community and ARG analysis. Increasing urbanisation was associated with reduced microbial alpha diversity alongside increased ARG diversity, with population grouping explaining an insightful proportion of both microbiome and resistome variation. Core resistance determinants to beta-lactams (cfxA), tetracyclines (tet), and macrolides (erm) were consistently detected across lifestyles and geographies. These results establish a baseline framework for urbanisation-linked microbial shifts in the under-studied Malaysian gut microbiome and provide actionable direction for investigating national AMR drivers, surveillance design, and targeted stewardship strategies in underrepresented populations. Data summary and code availabilitySupporting data are provided in the supplementary data files. Specific accession numbers of the sequence data are provided as Supplementary Material. Codes produced for this analysis can be found at https://zenodo.org/records/18208358.

genomics↗

bioRxiv2025

Rapid and Consistent Genome Clustering for Navigating Bacterial Diversity with Millions of MAGs and Isolates

Bacterial genome and metagenome databases collectively contain over 5 million high-quality assemblies. However, the redundancy of these databases and the limited scalability of existing tools create bottlenecks for fully comprehensive, tree-of-life-scale genomic analyses. A fundamental task is to first break this data into smaller chunks, guided by their genome similarity. However, alignment-based comparative methods struggle to handle more than a few tens of thousands of genomes at a time, making the global organisation computationally complex and expensive. Here, we present gemsparcl (https://github.com/johannahelene/gemsparcl), a tool that clusters bacterial genomes into genomic cohesive units (GCUs), at approximately species-level resolution, over 500 times faster than existing methods. As part of developing gemsparcl, we developed sketchlib.rust, a one-permutation MinHash approach that implements an auxiliary inverted index to further accelerate all-versus-all comparisons. We added a statistical correction for incomplete metagenome-assembled genomes (MAGs) to enable accurate distance estimation and network-based edge quality filtering. After genome completeness quality control, we clustered 5.6 million high-quality bacterial genomes (2.88 million isolates and 2.77 million MAGs) into 92,954 GCUs in [~]14 hours using 48 CPU threads and less than 16.5 GB of memory. Using taxonomic validation of the GCUs, the method achieves very high (99.76%) cluster purity (meaning only one species label occurs per GCU). We demonstrate that the clustering also highlights cases where taxonomic naming can be potentially harmonised or improved. Furthermore, we identify the most frequently reconstructed MAGs that lack a corresponding isolate genome and are thus priorities for culturing. The enhanced speed of gemsparcl enables routine database updates to incorporate the latest genomes. It also makes reference-free microbiome analysis across millions of genomes computationally tractable for the first time.

bioinformatics↗

bioRxiv2025

Quadrupling the protein family space with global metagenomics

The known universe of protein families represents only a small fraction of natures molecular diversity. From 40.3 billion sequences across 40,446 metagenomes, 9,540 metatranscriptomes, and 539 million proteins from 167,415 reference genomes, we identified 608,258 previously uncharacterized protein families with [≥]100 members and 6.5 million families with [≥]25 members, none matching known Pfam domains or reference proteins. This effort doubles the known repertoire of large families and quadruples that of smaller families. Integration of AlphaFold2-based structural predictions with gene-neighborhood and taxonomic analyses enables the characterization of previously unannotated proteins, revealing candidates for both novel and known biological functions in understudied microbial lineages and biomes. This expanded repertoire provides insights into microbial adaptation and broadens the molecular toolkit available for biotechnology, highlighting the power of global metagenomics to uncover hidden protein diversity.

bioinformatics↗

bioRxiv2025

Tangerine: a Starship-like element in the genomes of Xanthoria lichen-forming fungi

Lichens are symbiotic associations between filamentous fungi and photosynthetic micro-organisms, such as green algae and/or cyanobacteria, that result in a single anatomically-complex structure that can thrive in environments inhospitable to most organisms, including arctic tundra, high mountains, and deserts. Recent evidence suggests that lichens may be even more complex than previously appreciated, containing multiple microbial constituents, but how genomes of the principal fungal symbiont (which provides the majority of biomass in lichen tissue) have been shaped during evolution is largely unexplored. Recently, giant transposable elements called Starships have been found in many genomes of filamentous fungi, but to which extent they occur in lichen-forming fungi is not known. In this report, we describe a Starship element from the lichen fungus Xanthoria parietina. This element, named Tangerine, contains several genes that have signatures of horizontal gene transfer from non-lichen-forming fungi, most likely from black yeasts of the Chaetothyriales, that are often lichen-associated. Repetitive sequences carried by Tangerine, and found in other sites in Xanthoria genomes, are affected by repeat-induced point mutation (RIP), a mechanism of genome defense against transposable elements, consistent with fungal sexual reproduction which always precedes new lichen formation by Xanthoria. Tangerines "captain" belongs to a newly defined family of tyrosine recombinases specific to lichen-forming Lecanoromycetes. Several other captain clades have signatures of horizontal gene transfer between distantly related lichen-forming fungi and non-mycobiont lichen-associated fungi. We speculate that Starships may play a significant, yet hitherto unrecognized role, in lichen genome evolution and provide a roadmap for further investigation.

genomics↗

bioRxiv2025

RNAcentral in 2026: Genes and literature integration

RNAcentral was founded in 2014 to serve as a comprehensive database of non-coding RNA sequences. It began by providing a single unified interface to more specialised resources, and now contains 45 million sequences. It has grown beyond providing a single interface to many specialised resources and now provides several services and analyses. These include secondary structure prediction with R2DT, sequence search, and analysis with Rfam. Since its last publication in 2021, RNAcentral has developed two major features. First, literature integration with the development of LitScan and LitSumm. LitScan automatically identifies and links relevant publications to RNA entries, while LitSumm uses natural language processing to generate functional summaries from the literature. Together, these tools address the critical challenge of connecting sequence data with scattered functional knowledge across thousands of publications. Secondly, RNAcentral has created gene level entries. Gene level entries represent a large structural change to RNAcentral. While RNAcentral previously organized data exclusively at the sequence level, we now group related transcripts into gene-centric views. This allows researchers to explore all isoforms, splice variants, and related sequences for a gene in a unified interface, better reflecting biological organization and facilitating comparative analyses. RNAcentral is freely available at: https://rnacentral.org.

bioinformatics↗

bioRxiv2025

nf-core/proteinfamilies: A scalable pipeline for the generation of protein families

The growth of metagenomics-derived amino acid sequence data has transformed our understanding of protein function, microbial diversity and evolutionary relationships. However, the vast majority of these proteins remain functionally uncharacterized. Grouping the millions of such uncharacterised sequences with the few experimentally characterised ones allows the transfer of annotations, while the inspection of conserved residues with multiple sequence alignments can provide clues to function, even in the absence of existing functional information. To address the challenges associated with this data surge and the need to group sequences, we present a scalable, open-source, parametrizable Nextflow pipeline (nf-core/proteinfamilies) that generates protein nascent families or assigns new proteins to existing families. The computational benchmarks demonstrated that resource usage can scale approximately linearly with input size, while the biological benchmarks showed that the generated protein families closely resemble manually curated families found in widely used databases.

bioinformatics↗

bioRxiv2025

PIMENTO: A PrIMEr infereNce TOolkit to facilitate large-scale calling of amplicon sequence variants

The identification of amplicon sequence variants from DNA metabarcoding data is a common method for revealing the taxonomic makeup of environmental samples, and for allowing comparative studies between similar datasets. A significant hurdle to the large-scale calling of amplicon sequence variants from publicly available nucleotide datasets is the heterogeneous presence of primer sequences in reads, the removal of which is a necessary pre-processing step for this form of analysis. Furthermore, as the details of the experimental primers are rarely captured in the metadata associated with the sequence records, there is a need for a method that can automatically infer the presence and identity of primers in sequencing data. In this work, we introduce PIMENTO, a Python package which uses a dual-strategy approach for identifying primers that are present in sequencing reads to enable their removal, and therefore facilitate amplicon sequence variant calling at scale.

bioinformatics↗

bioRxiv2025

Biome-specific genome catalogues reveal functional potential of shallow sequencing

The use of 16S rRNA metabarcoding for functional prediction is limited by several biases. Shallow shotgun sequencing is a cost-effective and taxonomically high-resolution alternative to 16S rRNA metabarcoding, but the low sequencing depth limits functional inference. Our BioSIFTR tool maps shallow shotgun sequencing reads against single-biome databases and extrapolates into their precalculated functional profiles. We used three datasets from red junglefowl, mice, and human gut, containing matched deep shotgun metagenomic and 16S rRNA metabarcoding data for taxonomic and functional benchmarking. An additional human gut deep shotgun sequencing data set was subsampled to 1 M reads and analysed with BioSIFTR to replicate previously obtained results. BioSIFTR taxonomic and functional profiles closely agree with the results of the full deep sequencing data in all biomes. We also replicated differences in the human gut microbiome between high and low trimethylamine N-oxide producing participants, using only < 2 % of the original deep sequencing data. The BioSIFTR tool is a powerful approach which approximates the functional information of a deep-sequenced metagenome while using only a fraction of the data. Shallow shotgun sequencing combined with BioSIFTR could be a stand-in replacement for 16S rRNA metabarcoding with an increased taxonomic and functional resolution, and lower bias. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC="FIGDIR/small/659887v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@8ca365org.highwire.dtl.DTLVardef@13b466dorg.highwire.dtl.DTLVardef@8cad19org.highwire.dtl.DTLVardef@1b853fa_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Elucidating the mechanisms of action and evolutionary history of phage anti-defence proteins

Phages and bacteria are locked in a molecular arms race, with phage anti-defence proteins (ADPs) enabling them to evade bacterial immune systems. To streamline access to information on ADPs, we developed the Encyclopaedia of Viral Anti-DefencE Systems (EVADES), an online resource containing sequences, structures, protein family annotations, and mechanisms of action (MoA) for 268 ADPs. Through computational structural analysis we predicted MoAs for 21 uncharacterised ADPs. We demonstrate the utility of EVADES by exploring different characteristics of ADPs: (i) DNA mimic ADPs exhibit broad-spectrum activity against bacterial defence systems; (ii) protein sharing across defence systems enables multi-defence inhibitory activity of ADPs; and (iii) eukaryotic dsDNA viruses encode ADP homologs, suggesting conserved immune evasion strategies across domains of life. These results highlight the broader relevance of phage ADPs in understanding interactions between prokaryotic or eukaryotic viruses and their hosts. EVADES is freely accessible at https://www.ebi.ac.uk/finn-srv/evades. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=132 SRC="FIGDIR/small/658234v3_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@18460corg.highwire.dtl.DTLVardef@19ef231org.highwire.dtl.DTLVardef@2e91a5org.highwire.dtl.DTLVardef@15c2547_HPS_FORMAT_FIGEXP M_FIG C_FIG O_LIThe EVADES database aggregates knowledge on phage anti-defence proteins (ADPs), facilitating hypothesis generation. C_LIO_LIMechanisms of action are proposed for 21 uncharacterised ADPs. C_LIO_LIADPs that function as DNA mimics can block diverse bacterial defence systems. C_LIO_LIHomology of targets of ADPs underpin their broad counter-defence capacities and enable prediction of further targets. C_LIO_LIEukaryotic double-stranded DNA viruses employ functional homologs of ADPs to evade host immune responses. C_LI

microbiology↗

bioRxiv2025

Seasonal biogeochemical variations in a modern microbialite reef under early Earth-like conditions

Microbialites are organosedimentary structures dating to the Precambrian that serve as archives of Earths environmental evolution. Today, they persist in only a few environments markedly different from those in which they first arose. Here, we report a modern microbialite reef in Laguna Pozo Bravo (Puna region, Argentina), exposed to high radiation, low oxygen pressure, and volcanic inputs reminiscent of early Earth. Through physicochemical, mineralogical, spectroscopic, electron microscopy, and metagenomic analyses, we identified diverse microbial communities with metabolic capacities that induce mineralisation. Seasonal environmental fluctuations drive cyclical changes in community composition, producing potential mineralisation patterns. Our findings suggest that carbon fixation and the metabolic drivers of alkalinity in microbialites evolved over time. Moreover, the variability in prokaryotic compositions among modern microbialites demonstrates that carbonate precipitation is governed by metabolic potential rather than taxonomy, reinforcing their role as dynamic records of environmental conditions.

microbiology↗

bioRxiv2024

CODARFE: Unlocking the prediction of continuous environmental variables based on microbiome

Despite the surge in data acquisition, there is a limited availability of tools capable of effectively analyzing microbiome data that identify correlations between taxonomic compositions and continuous environmental factors. Furthermore, existing tools also do not predict the environmental factors in new samples, underscoring the pressing need for innovative solutions to enhance our understanding of microbiome dynamics and fulfill the prediction gap. Here, we introduce CODARFE, a novel tool for sparse compositional microbiome-predictors selection and prediction of continuous environmental factors. We tested CODARFE against four state-of-the-art tools in two experiments. First, CODARFE outperformed predictor selection in 21 out of 24 databases in terms of correlation. Second, among all the tools, CODARFE achieved the highest number of previously identified bacteria linked to environmental factors for human data--that is, at least 7% more. We also tested CODARFE in a cross-study, using the same biome but under different external effects (e.g., ginseng field and cattle for arable soil, and HIV and crohns disease for human gut), using a model trained on one dataset to predict environmental factors on another dataset, achieving 11% of mean absolute percentage error. Finally, CODARFE is available in five formats, including a Windows version with a graphical interface, to installable source code for Linux servers and an embedded Jupyter notebook available at MGnify - https://github.com/alerpaschoal/CODARFE.

bioinformatics↗

bioRxiv2024

mettannotator: a comprehensive and scalable Nextflow annotation pipeline for prokaryotic assemblies

SummaryIn recent years there has been a surge in prokaryotic genome assemblies, coming from both isolated organisms and environmental samples. These assemblies often include novel species that are poorly represented in reference databases creating a need for a tool that can annotate both well-described and novel taxa, and can run at scale. Here, we present mettannotator - a comprehensive Nextflow pipeline for prokaryotic genome annotation that identifies coding and non-coding regions, predicts protein functions, including antimicrobial resistance, and delineates gene clusters. The pipeline summarises the results of these tools in a GFF (General Feature Format) file that can be easily utilised in downstream analysis or visualised using common genome browsers. Mettannotator has been tested on both novel and known taxa, and used across Ensembl Bacteria and MGnify. Here, we show how it works on 29 prokaryotic phyla, both isolate and metagenome-assembled genomes, and present metrics on its performance in comparison to other tools. Availability and implementationThe pipeline is written in Nextflow and Python and published under an open source Apache 2.0 licence. Instructions and source code can be accessed at https://github.com/EBI-Metagenomics/mettannotator. The pipeline is also available on WorkflowHub: https://workflowhub.eu/workflows/1069.

bioinformatics↗

bioRxiv2024

CELEBRIMBOR: Pangenomes from metagenomes

SummaryMetagenome Assembled Genomes (MAGs) are often incomplete, with sequences missing due to errors in assembly or low coverage. Incomplete MAGs present a particular challenge for identification of shared genes within a microbial population, known as core genes, as a core gene missing in only a few assemblies will result in it being mischaracterized at a lower frequency. Here, we present CELEBRIMBOR, a snakemake pangenome analysis pipeline which uses a measure of genome completeness to automatically adjust the frequency threshold at which core genes are identified, enabling accurate core gene identification in MAGs. Availability and implementationCELEBRIMBOR is published under open source Apache 2.0 licence at https://github.com/bacpop/CELEBRIMBOR and is available as a Docker container. Supplementary material is available in the online version of the article.

bioinformatics↗

bioRxiv2024

Specialization in Molecular Functions and Secondary Metabolite Production Potential in Cyanobacterial Symbionts

Cyanobacteria are globally occurring photosynthetic bacteria notable for their contribution to primary production and their production of toxins which have detrimental impacts on ecosystems. Beyond this, cyanobacteria can form mutualistic symbiotic relationships with a diverse set of eukaryotes, ranging from land plants to fungi. Nevertheless, not all cyanobacteria are found in symbiotic associations suggesting symbiotic cyanobacteria have evolved specializations that facilitate host-interactions. Photosynthetic capabilities, nitrogen fixation, and the production of complex biochemicals are key functions provided by host-associated cyanobacterial symbionts. To explore if additional specializations are associated with such lifestyles in cyanobacteria, we have conducted comparative phylogenomics of molecular functions and of biosynthetic gene clusters (BGCs) in 977 cyanobacterial genomes. Cyanobacteria with host-associated and symbiotic lifestyles were concentrated in the family Nostocaceae, where eight monophyletic clades correspond to specific host taxa. In agreement with previous studies, symbionts are likely to provide fixed nitrogen to their eukaryotic partners. Additionally, our analyses identified chitin metabolising pathways in cyanobacteria associated with specific host groups, while obligate symbionts had fewer BGCs. The conservation of molecular functions and BGCs between closely related symbiotic and free-living cyanobacteria suggests that there is the potential for additional cyanobacteria to form symbiotic relationships than is currently known.

genomics↗