Biology subjects

Beracochea, M.

Publications and source records attributed to Beracochea, M..

10 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

FAIRyMAGs - a series of FAIR Galaxy workflows for the generation of metagenome assembled genomes

Advances in whole-genome sequencing (WGS) technologies have enabled large-scale recovery of metagenome-assembled genomes (MAGs), providing unprecedented insights into microbial diversity across diverse environments. However, the reconstruction of MAGs remains computationally demanding and methodologically complex, requiring the integration of multiple tools for quality control, assembly, binning, refinement, and annotation. Existing workflows often rely on scripting-based implementations, constrain user-driven modification and stepwise execution, and require advanced expertise in high-performance computing (HPC) system administration, thereby limiting accessibility, reproducibility, and adaptability. Here, we present FAIRyMAGs, a Findable, Accessible, Interoperable, and Reusable (FAIR)-compliant, modular pipeline implemented within the Galaxy platform for the generation and analysis of MAGs. FAIRyMAGs consists of six interconnected workflows covering all major steps of MAG reconstruction, including read preprocessing, host and contaminant removal, assembly, binning, dereplication, and downstream taxonomic and functional annotation. The workflows are accompanied by extensive training material, including tutorials, a learning pathway, FAQs, test datasets and video walk-throughs by domain experts, supporting community adaptation. By leveraging Galaxys graphical interface and federated infrastructure, FAIRyMAGs enables users to execute complex analyses on public or private compute resources without requiring local installation or workflow programming expertise. The modular design further supports flexible adaptation, iterative optimization, and seamless integration of new tools contributed by the community. To demonstrate applicability, FAIRyMAGs was applied to four real-world microbiome datasets spanning various host-associated and environmental systems. These analyses revealed substantial variability in MAG recovery, community complexity, and clustering structure, underscoring the importance of flexible workflows adaptable to dataset-specific characteristics. Overall, FAIRyMAGs provides an accessible, extensible, and reproducible framework for genome-resolved metagenomics, reducing technical barriers and enabling methodological innovation through community-driven development within the adaptable Galaxy ecosystem.

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↗

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↗

bioRxiv2024

The OMG dataset: An Open MetaGenomic corpus for mixed-modality genomic language modeling

Biological language model performance depends heavily on pretraining data quality, diversity, and size. While metagenomic datasets feature enormous biological diversity, their utilization as pretraining data has been limited due to challenges in data accessibility, quality filtering and deduplication. Here, we present the Open MetaGenomic (OMG) corpus, a genomic pretraining dataset totalling 3.1T base pairs and 3.3B protein coding sequences, obtained by combining two largest metagenomic dataset repositories (JGIs IMG and EMBLs MGnify). We first document the composition of the dataset and describe the quality filtering steps taken to remove poor quality data. We make the OMG corpus available as a mixed-modality genomic sequence dataset that represents multi-gene encoding genomic sequences with translated amino acids for protein coding sequences, and nucleic acids for intergenic sequences. We train the first mixed-modality genomic language model (gLM2) that leverages genomic context information to learn robust functional representations, as well as coevolutionary signals in protein-protein interfaces and genomic regulatory syntax. Furthermore, we show that deduplication in embedding space can be used to balance the corpus, demonstrating improved performance on downstream tasks. The OMG dataset is publicly hosted on the Hugging Face Hub at https://huggingface.co/datasets/tattabio/OMG and gLM2 is available at https://huggingface.co/tattabio/gLM2_650M.

systems biology↗

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↗

bioRxiv2022

VIRify: an integrated detection, annotation and taxonomic classification pipeline using virus-specific protein profile hidden Markov models

The study of viral communities has revealed the enormous diversity and impact these biological entities have on a range of different ecosystems. These observations have sparked widespread interest in developing computational strategies that support the comprehensive characterization of viral communities based on sequencing data. Here we introduce VIRify, a new computational pipeline designed to provide a user-friendly and accurate functional and taxonomic characterization of viral communities. VIRify identifies viral contigs and prophages from metagenomic assemblies and annotates them using a collection of viral profile hidden Markov models (HMMs). These include our manually-curated profile HMMs, which serve as specific taxonomic markers for a wide range of prokaryotic and eukaryotic viral taxa and are thus used to reliably classify viral contigs. We tested VIRify on assemblies from two microbial mock communities and a large metagenomics study. The results showed that VIRify was able to identify sequences from both prokaryotic and eukaryotic viruses, and provided taxonomic classifications from the genus to the family rank with an accuracy of at least 95.5%. In addition, VIRify allowed the detection and taxonomic classification of a range of prokaryotic and eukaryotic viruses present in 243 marine metagenomic assemblies. Overall, we demonstrate that VIRify is a novel and powerful resource that offers an enhanced capability to detect a broad range of viral contigs and taxonomically classify them.

bioinformatics↗

bioRxiv2019

A unified sequence catalogue of over 280,000 genomes obtained from the human gut microbiome

Comprehensive reference data is essential for accurate taxonomic and functional characterization of the human gut microbiome. Here we present the Unified Human Gastrointestinal Genome (UHGG) collection, a resource combining 286,997 genomes representing 4,644 prokaryotic species from the human gut. These genomes contain over 625 million protein sequences used to generate the Unified Human Gastrointestinal Protein (UHGP) catalogue, a collection that more than doubles the number of gut protein clusters over the Integrated Gene Catalogue. We find that a large portion of the human gut microbiome remains to be fully explored, with over 70% of the UHGG species lacking cultured representatives, and 40% of the UHGP missing meaningful functional annotations. Intra-species genomic variation analyses revealed a large reservoir of accessory genes and single-nucleotide variants, many of which were specific to individual human populations. These freely available genomic resources should greatly facilitate investigations into the human gut microbiome.

microbiology↗