iMeta

Precise identification depends on highly resolved classification. Therefore, whole genome-based strain typing schemes have been implemented for identification of the most important microbial human pathogens. However, there are no dedicated typing schemes for the majority of prokaryotic human, animal, and plant pathogens, or for non-pathogenic microbes, often limiting their identification to taxonomic classification tools that have the species as the smallest unit. To address this deficiency, we created the genomeRxiv web server which contains over 350,000 genomes and leverages the average nucleotide identity (ANI)-based Life Identification Number (LIN) approach, allowing users to identify prokaryotic genomes in real time from approximately genus-rank to near strain-level resolution. LINs are overlaid with circumscriptions of species and infraspecies classes, integrating taxonomy with strain typing. Metadata and distance trees are visualized to complement inferred phylogenetic relationships with epidemiological and ecological information. A command line tool is provided for users to upload k-mer sketches of unpublished genomes and associated metadata, facilitating epidemiological investigations without the need to share complete DNA sequences. In conclusion, genomeRxiv constitutes a much-needed resource for precise, rapid identification of the many prokaryotes not included in any pathogen-specific typing scheme.

Meta2Data is a modular, largely automated pipeline implemented in Python and Bash for the retrieval, standardization, and processing of public microbiome sequencing data. It retrieves metadata from major public repositories, including INSDC and CNCB, using user-provided keywords or BioProject/BioSample identifiers. Retrieved metadata are standardized using a synonym dictionary and then subjected to manual curation. Based on the curated metadata, Meta2Data downloads the corresponding raw sequencing data and processes them using standardized workflows based on DADA2 or VSEARCH. The pipeline supports both single- and multiple-target-region datasets and produces integrated feature tables, taxonomic assignments, and phylogenetic trees for downstream microbial community analyses. Availability and ImplementationMeta2Data is freely available at https://github.com/LinyangSun/Meta2Data and archived on Zenodo at https://doi.org/10.5281/zenodo.20670433

Recent efforts have demonstrated that systematic analysis of the millions of metagenomes in the Sequence Read Archive (SRA) enables groundbreaking biological discoveries. However, SRA-scale comparison of whole metagenomes has remained elusive. Here, we introduce the most comprehensive resource to date for experiment-to-experiment analysis of all SRA metagenomes, comprising FracMinHash sketches (470GB), hypergen sketches (19GB), and an all-pairs Jaccard similarity matrix (90GB), which we release online at https://github.com/RolandFaure/SRA-metagenome-sketches-databases. These resources enable a wide range of analyses, which we demonstrate with several applications. First, we use the FracMinHash sketches to uncover temporal trends, finding that the amount of newly discovered microbial sequence continues to grow, rather than saturate or shrink. Second, we use the hypergen sketches, which enable nearest-neighbor search, to identify co-assembly candidates and thereby improve assembly quality. Third, we use the similarity matrix to analyze all SRA metagenomes, identifying duplicated submissions and over 50K candidates for metadata correction. Together, our resources unlock systematic analysis of metagenomic data at unprecedented scale.

The human gut microbiota has been explored for the last two decades and studies have uncovered its major role in the degradation of complex glycans from the diet as well as the host tissues. Strikingly, until very recently, no similar investigation has been carried out on the human oral cavity microbiota, which is increasingly recognized has an important parameter in gastrointestinal health. Prevotellaceae are an important and under-investigated family of bacteria in both the gut and the oral cavity microbiota. The oral cavity is particularly rich in glycosaminoglycans which could provide an abundant carbon source for the inhabiting microorganisms, and play a significant role in host-pathogen interactions. Hence, we analyzed Polysaccharide Utilization Loci in oral microbiota species and we identified a glycosaminoglycan-degrading Polysaccharide Utilization Locus in Segatella oris. We produced and characterized the four enzymes, confirmed their ability to degrade glycosaminoglycans, and we focused our efforts on the biochemical and structural characterization of the first reported Prevotellaceae endosulfatase.

Viruses shape microbial metabolism through auxiliary viral genes (AVGs) that reprogram host metabolism, physiology, and gene regulation during infection. Identifying AVGs is complicated by ambiguous viral versus cellular assignment, inconsistent curation, and reliance on sequence homology that misses divergent functions. Here we present CheckAMG, which pairs annotation-based AVG prediction, curated with a per-gene viral scoring system and ontological mapping of metabolic function, with a finetuned genome-language model for annotation-independent discovery. Benchmarked against DRAM-V and VIBRANT across three ecosystems, CheckAMG produced more strongly supported auxiliary metabolic gene (AMG) calls, and uniquely among AMG tools reports auxiliary physiological (APGs) and regulatory (AReGs) genes. Applied to 1,005,980 fragmented or complete soil and human-gut viral genomes, CheckAMG identified 541,199 AVGs tracking biome-specific protein clusters and functions, including 109,107 proteins with no detectable sequence similarity to any reference database. CheckAMG thus provides a reproducible, scalable framework for identifying the genes through which viruses reprogram host biology.

Functional profiling of anaerobic digestion microbiomes is routinely performed against KEGG or MetaCyc, whose paid commercial licences block fee-for-service analysis for biogas operators, the setting where the results have the most immediate operational value. We present digestome, a curated marker-gene panel for anaerobic digestion built exclusively on sources that are free for commercial use (NCBIfam, public domain; Pfam, CC0; Rhea, CC BY), together with a scorer that reports pathway completeness, branch capability and, for hydrolysis, whether the enzyme is built for export. Benchmarked against 1,401 metagenome-assembled genomes from 134 anaerobic digesters, the panel assigned no methanogenesis route to any of 1,361 non-methanogens, and every acetoclastic call fell within Methanosarcina or Methanothrix. The result held for 3,043 species representatives from the Genome Taxonomy Database, spanning 203 phyla, which are built differently from binned metagenomes: the only background genomes given a route were two archaea carrying mcrA, and none of the 90 anaerobic methane and alkane oxidisers was given one. Specificity against non-methanogens does not show that a methanogen gets the right route: with a family-level check, 59 of 156 acetoclastic calls in that sample fell on methylotrophic genera that do not use acetate, and a lineage policy removed them, with 14 more left unconfirmed in unnamed genera. Testing whether a catalytic domain shares a polypeptide with an export module reduced the genomes credited with cellulolytic capacity from 597 to 50. A causal graph of the measurement shows that a profile reports the community together with three measurement choices (sampling, DNA extraction and sequencing depth, and reference genomes), which fixes what one profile can attribute and what a prospective validation would have to record.

Metagenomic sequencing allows for detection of any number of pathogenic viruses from a single sample without a priori knowledge of its contents. This has created a substantial opportunity to characterize pathogens in humans and their environment, but the field is held back by a lack of bioinformatics tools for taxonomic profiling and reconstructing virus genomes. The ideal profiler would simultaneously do two things: report viruses at single-read sensitivity so that ultra-low-abundance pathogens are not missed and hold near-perfect specificity so that a pathogen is never falsely reported. Here we present EsViritu, a taxonomic profiler that pairs an iterative greedy clustering algorithm and lowest-common-ancestor taxonomic refinement with a curated Virus Pathogen Database to reconstruct virus genomes and generate virome profiles directly from reads. We benchmarked EsViritu against four leading metagenomic profilers (Kraken2, Centrifuger, Metabuli, and Sylph), running each program across a full parameter sweep and against the same Virus Pathogen Database for an apples-to-apples comparison. Across synthetic positive controls, pathogen-free negative controls, and mutated mock communities, EsViritu was the most accurate tool at reconstructing community composition: it led or tied every tool on relative abundance accuracy at all taxonomic ranks and led or tied on detection across ranks. Importantly, other tools frequently report the presence of multiple related taxa when only reads for a single genome are present, but EsViritu faithfully records the correct, single taxon and genome. EsViritu achieves read-level sensitivity for sequences as low as 85% average nucleotide identity to the nearest reference and reports essentially no false positives in pathogen-free host and synthetic samples. EsViritu also returns reconstructed consensus genomes whose nucleotide identity to the nearest reference closely tracks ground truth, yielding both a profile and genomes from a single run.

Determining the biological function of bacterial proteins remains a central challenge in microbiology. We previously developed Fusion, a reference-free scheme that clustered the tens of millions of known, unique, bacterial proteins into about four hundred thousand groups of functionally similar proteins, i.e. functions. For any newly identified proteins, Fusion function assignment could be performed using function-aware sequence alignment to each function's representatives. However, Fusion functions lack human-readable descriptions of their functionality. Here, we leverage large language models (LLMs) to synthesize scattered, partial annotations of functions of individual proteins into coherent English-language descriptions for ~100,000 clusters, i.e. a quarter of all Fusion functions. From these descriptions, we extract Gene Ontology (GO) terms and Enzyme Commission (EC) numbers. We validate these against annotations inferred from UniProtKB/Swiss-Prot. These annotations immediately expand functional coverage for both genome and microbiome -level analyses. For organisms in FusionDB, we annotate 50% more proteins than alignment to Swiss-Prot alone. For other bacteria, the gain is smaller but still reaches 30% for some organisms. For microbiomes, annotation coverage increases nearly ten-fold. Our work thus enables the analysis of microbial "transparent matter," i.e. functions already described in earlier research that have not yet made it into usable annotations. Our annotated Fusion function database is freely available at https://services.bromberglab.org/fusion2ai and as a reference DB for metagenome annotation https://services.bromberglab.org/mifaser.

As climate change accelerates, the microbial communities sustaining organisms and ecosystems worldwide face an uncertain future, with some proving resilient to stress while others collapse, yet no generalizable framework exists to distinguish the two beforehand. Using amplicon sequencing data from ~29,000 microbiomes, we constructed over 440 matched network pairs spanning three of the most pressing stressors: drought, warming, and salinity. We show that keystone microbes with high connectivity, functional redundancy, and metabolic flexibility, are disproportionately likely to survive stress, and that baseline network and functional properties predict community-level structural and functional stability even as taxonomic composition shifts stochastically. These relationships held across the diverse hosts, ecosystems, and regions we tested. Because these properties can be derived from standard, low-cost sequencing collected before disturbance occurs, our findings turn baseline microbiome data into an early-warning system for identifying which communities are most vulnerable to climate stress.

Microbial metagenomes encode vast catalytic diversity, but recovering enzymes of novel, yet-undescribed functionality typically requires whole-community assembly plus a means of identifying active proteins ab initio. The first step of the task is compute-intensive and misses low-abundance sequences. The second is complicated by our inability to predict novel functions. REBEAN, our DNA language model, sidesteps the latter by assigning each sequencing read a high-level Enzyme Commission (EC) class or a non-enzyme label without alignment. Here we build REFinder, a pipeline that addresses both steps by routing REBEAN-annotated reads to assemble only the putative enzymatic reads that have identified catalytic signatures. In our evaluation of 50 microbiome metagenomes, REFinder identified 1.2 to 2.1 fold more enzymes than could be recovered via homology-based annotation of the proteins from the corresponding full assemblies. Moreover, it was as much as 6.4-fold cheaper computationally than full assembly. Across all samples, REFinder identified at least three fourths and as many as 90% of the homology-accessible enzymes identified via full assembly of the complete metagenomes. Notably, a fraction of these, 22% to 45% per EC class, carried no similarity to Swiss-Prot proteins, i.e. a set of enzymes homology cannot annotate. For roughly two fifths of the over thirteen thousand such novel oxidoreductases from two saliva samples, ESMFold predicted structures aligned with TM-score[≥]0.7 to a characterized enzyme structure in the PDB - a substantial structural similarity without sequence homology. These results illustrate that targeted, alignment-free assembly turns even well-mapped microbiomes into a source of thousands of previously invisible but credible novel enzymes, raising our expectations for exploration of environmental microbiomes.

Metagenomic sequencing enables broad pathogen detection without requiring prior assumptions about the organisms present, but the memory and computational demands of classifying large sequencing datasets against reference databases often lead analysis pipelines to rely on desktop or server-grade computing platforms. This limits practical use in disasterstricken and other resource-constrained environments, where portable sequencing may be available but reliable access to cloud or laboratory infrastructure is not. We present KENT, a portable edge-computing framework for metagenomic pathogen screening based on a cluster of NVIDIA Jetson Nano modules. KENT adapts CuCLARK-l for low-memory embedded execution and extends it with a custom software stack for distributed read classification, abundance estimation, split-run abundance merging, and report generation. Using an MPI-based controller/responder architecture, KENT supports local, offline analysis with sample-level parallelism across multiple nodes. We evaluate KENT using long read wastewater metagenomic samples with a custom pathogen database and the CAMI2 Marine long read dataset. In the wastewater setting, KENT achieves an average speedup of 3.1x over the CPU-based CLARK-l baseline while maintaining close agreement in classified read abundance estimates. On CAMI2 Marine, KENT achieves an average speedup of 2.68x and similarly preserves CLARK-l abundance profiles. Evaluation against the CAMI2 long read taxonomic ground truth yields a mean species-level precision of 58.55%, recall of 93.36%, F1 score of 71.74%, and L1 abundance distance of 0.731, closely matching CLARK-l. Among ground truth species represented in the reference database, KENT detects 99.86% on average. A controlled database-size experiment further shows little change in classification throughput across the tested database configurations, while large sequencing inputs remain constrained by the Jetson Nanos unified memory capacity. Overall, KENT demonstrates that GPU-accelerated metagenomic classification can be adapted to low-memory embedded hardware while preserving the biological profiling behavior of the CLARK-l baseline, supporting portable and offline pathogen screening in resource-constrained settings. AvailabilityKENT is available at https://github.com/BilkentCompGen/KENT

Biofilms are widely distributed in both natural and engineered systems and play a fundamental role in microbial ecology and biotechnological applications. When grown on agar substrates, biofilms often exhibit macroscopic structural features due to mechanical instabilities driven by matrix production. These wrinkles encode relevant information about biofilm growth and structural development and have even been shown to possess some functional roles. Despite their relevance, currently available approaches for wrinkle detection from images rely on manual annotation, which is time-consuming, not scalable, and strongly dependent on the annotator. In the present study, we developed an end-to-end machine learning pipeline, named AutoWrinkleID, that starts from bright field images of biofilms and performs wrinkle identification, characterisation, and quantification across multiple bacterial species and strains. The name reflects the automated identification and subsequent characterisation and quantification of biofilm wrinkles. The annotations used to generate the masks, namely images containing only the wrinkle structures, were obtained both through manual labelling and using constitutive fluorescence and motility fluorescence. Interestingly, we found that models trained using only manual annotations perform worse than those trained using motility fluorescence markers. A comparison between motility and constitutive fluorescence masks further indicates that motility based annotations consistently outperform constitutive fluorescence across all strains, strongly suggesting that motile phenotypes are tightly associated with wrinkle structures. Additional computational analysis is conducted on the same dataset to assess the robustness and limitations of the results as a function of training dataset. We also observed that Sholl analysis, originally developed for the study of neuronal dendrites and later applied to wrinkle analysis, can effectively characterize wrinkle patterns, although its applicability is strongly limited by wrinkle shape and by the quality of the masks.

Accurate species-level profiling of shotgun metagenomes becomes difficult when an organism is represented by only sparse sequence coverage. Per-read k-mer classifiers can retain sensitivity under these conditions, but short reads from conserved or shared genomic regions can generate false-positive calls among closely related references. More conservative marker-gene and genome-containment approaches generally provide stronger species-level specificity but may lose sensitivity as genomic sampling becomes sparse. We present winnow-tax, a two-stage taxonomic profiling pipeline that separates sensitive candidate nomination from genome-level confirmation. Candidate species are nominated using Sylph, Kraken2, and a branch-rescue procedure, then reads are competitively recruited to a sample-specific reference set. Species presence is evaluated using read support, observed genome breadth, and a Lander-Waterman-based breadth ratio that compares observed breadth with the breadth expected at the measured mean depth. In a controlled synthetic community of 82 genomes with human DNA background, winnow-tax maintained a stronger precision-sensitivity balance than Kraken2/Bracken, Sylph, and MetaPhlAn 4 as genome coverage decreased, with its advantage concentrated near the low-coverage detection boundary. In the CAMI III Toy Longitudinal Human Gut benchmark, winnow-tax had higher sensitivity than Sylph (0.803 versus 0.672), lower precision (0.923 versus 0.970), and a modestly but significantly higher F1 score (0.858 versus 0.793). In a clinical enteric stool cohort with culture/PCR reference testing, winnow-tax achieved the best composite performance for Salmonella, detecting 37 of 48 composite-positive samples with one false-positive call. Together, these results support a profiling strategy in which weak taxonomic signals are retained during candidate generation but require genomic evidence distributed as broadly as expected for their sequencing depth before species presence is accepted.

Zoonotic transmission of viruses has triggered major outbreaks throughout history. However, human-to-animal transmission, or reverse zoonotic transmission, also represents an underappreciated public health threat. Reverse zoonoses can establish novel animal reservoirs that fuel viral evolution and enable eventual spillback into humans, underscoring the importance of defining viral host range. Current approaches for host range prediction are limited. Experimental methods are retrospective and rely on brute-force testing in a small number of models, whereas computational approaches often lack experimental grounding. Here, we present a platform that integrates high-throughput experimental screening with deep learning to predict viral host range from sequence. We screened a yeast-displayed SARS-CoV-2 receptor-binding domain library against 15 ACE2 orthologs from diverse species, and deep sequenced the ACE2-binding and non-binding populations to generate an atlas of 8.8 million unique RBD:ACE2 interaction measurements. These data were used to train a transformer-based deep learning model that predicts RBD:ACE2 compatibility across animal species from sequence alone, and generalizes in a zero-shot manner to ACE2 orthologs not observed during training. Critically, the model pairs each prediction with a distance-based uncertainty estimate, allowing hundreds of candidate species to be triaged by both predicted host compatibility and confidence. This framework enables the rapid prioritization of animal species for SARS-CoV-2 surveillance and provides a general strategy for anticipating viral host range.

Local alignment tools struggle at querying against thousands of bacterial genomes, because the reference indexes are memory-intensive and base-level dynamic programming is slow. We present alamem (https://github.com/yunwilliamyu/alamem), an approximate local aligner that streams unprocessed reference databases and estimates average nucleotide identity (ANI) from Maximal-Exact-Match (MEM) chaining statistics. Alamem can query a bacterial genome against >85,000 prokaryotic genomes in under 30 seconds (with multithreading) using 4 GB of memory, making it over 16x faster and 21x more memory-efficient than existing local aligners using their default running modes. On top of alamem, we built alasight (https://github.com/graceoualline/alasight) for detecting signatures of horizontal gene transfer (HGT) by finding pairs of alamem hits to divergent genomes. Alasight achieves over an order of magnitude better sensitivity on data from real bovid microbiomes than existing mobile genetic element-detection software and provides depth and sparsity information supporting each putative HGT region.

Heterotrophic flagellates play a major role in nutrient recycling and shaping aquatic microbial communities. Many motile species have evolved diverse predatory strategies to exploit microbial prey, from bacteria to small eukaryotes. Excavates, deep-branching eukaryotic lineages with distinctive biflagellate morphology and conserved cytoskeletal features, exhibit varied feeding behaviours, including grazing, raptorial feeding, and filter-feeding. However, the temporal dynamics of their motility, feeding, and spatial distribution in relation to bacterial population density remain under-explored. Here, we quantify the behavioural ecology of excavates using high-resolution, single-cell tracking and quantitative analysis. Focussing on two Discoba species - the marine Jakoba libera and freshwater Reclinomonas americana - we identify different behavioural patterns: J. libera swims, forages and feeds, while R. americana swims and feeds. Prey availability differentially affects motility: J. libera increases the number of swimming cells when food is scarce, while R. americana shows the opposite trend. Spatial statistics reveal fundamental differences in their feeding and redistribution strategies. J. libera acts as an active forager, whereas R. americana behaves as a strict filter feeder. A third species, the anaerobic Kipferlia bialata (from a different clade), adopts a foraging strategy similar to J. libera. Together, these results elucidate the adaptive relationship between prey availability, feeding modes, and motility in bacteriovorous excavate flagellates, offering new insights into the ecological and evolutionary dynamics of microbial predators.

Dengue virus (DENV) is genetically diverse, consisting of four antigenically distinct serotypes and extensive sub-serotype variation that can influence epidemiology and control. As DENV continues to expand globally, there is an urgent need to characterize the dynamics of distinct DENV genetic lineages, including their persistence, establishment, and spread. Using a standardized DENV lineage nomenclature system and 37,828 publicly available DENV genomes sampled from 2000-2024, we classified sequences across nine global regions to construct a comprehensive resource of regional lineage distributions over time. We found marked regional differences in lineage diversity, with higher lineage richness in Southeast and East Asia than in the Americas. We further showed that COVID-19-related disruptions were associated with decreased regional lineage persistence and detection of new lineages during 2020-2021, followed by rebounds after 2022 and, in some regions, substantial lineage turnover. Finally, we identified a small set of globally expansive (cosmopolitan) lineages that are over-represented in available sequences, including five first-order minor lineages detected in at least seven of nine regions: 3III_B.3, 2II_F.1, 2II_A.1, 1III_A.3, and 1I_K.3. Together, these results demonstrate how standardized lineage classification enables global tracking of emerging DENV diversity and supports targeted genomic surveillance.

Gastrointestinal infections are a leading cause of death and morbidity worldwide. Antibiotics as current treatments are ineffective or not recommended due to the emergence of multidrug resistant organisms. The initial step of the pathogenesis for most of the enteric infections is the binding of the pathogen to oligosaccharide receptors on the surface of host cells, making these receptors attractive targets for novel antimicrobial strategies that do not rely on conventional antibiotics. Here, we developed an alternative therapeutic intervention based on a novel microbiome-based chemical engineering approach. We synthesized cholesteryl-peptides as flexible chemical scaffold for the presentation of carbohydrate GM1 mimic receptors in probiotic bacteria acting as a decoy for cholera toxin neutralization. The GM1-cholesteryl-peptides were stably embedded into the membranes of both gram positive and gram negative probiotics, Lactobacillus reuteri DSM and Escherichia coli Nissle 1917 respectively. The resulting GM1-coated probiotics demonstrated to quench nearly 100% of cholera toxin in vitro, functioning as decoys by preventing toxin binding to epithelial cells. The present study strongly suggests that our new and versatile biomimetic non-recombinant system could be employed to neutralize the functional effect of infections mediated by oligosaccharide receptors.

Microbiome measurements are commonly judged by how faithfully they reconstruct an underlying biological profile, using taxonomic similarity, prediction accuracy, or compositional distance. Yet faithfulness and usefulness are not the same: a distorted measurement may still support the correct intervention, while a small error can be decisive when it falls near the boundary between two treatment choices. We therefore evaluate measurement quality by whether information loss changes the decisions and outcomes that remain attainable. We treat the intervention-relevant state of the gut community as a hidden quantity seen only through an imperfect, information losing measurement, mapped by a decision rule to a graded intervention. Each decision rule is evaluated by net host benefit and ecological margin, generating a feasible set of attainable outcome pairs. We define the measurement-induced decision loss (MIDL) as the maximum net host benefit lost under imperfect measurement relative to perfect observation. For scalar affine information loss, increasing degradation is a Blackwell garbling; under post-processing closure, more informative measurement weakly expands the feasible set and cannot reduce optimal host benefit. In the binary-state model, MIDL increases linearly while an observation-dependent decision rule remains optimal and plateaus once the optimal policy becomes signal-independent. We extend the framework to finite states, state-specific loss, multi-segment stool aggregation, and ecological-margin constraints. The framework distinguishes measurement faithfulness from decision usefulness and provides a principled basis for comparing stool and richer measurements by the interventions and trade-offs they support.

Bacterial pathogens ensure their successful dissemination by employing specialized secretion systems to deliver a wide range of virulence proteins that interact with specific host proteins to modulate the response during infection. These interactions are often mediated by host-like elements, like short linear motifs, which can interfere with host interaction interfaces, enabling bacterial secreted proteins to compete and displace native partners of the targeted proteins. Several resources provide access to bacteria-host protein-protein interaction data. However, some are neither fully functional or up to date. Moreover, most of these databases lack virulence protein annotations, thus hampering hypothesis formulation based on protein interaction analyses. In this context, we have developed BactMentha, a unified resource that integrates bacterial-host protein-protein interactions for three organisms (human, mouse, and rat) with virulence protein annotations and bacterial strain clinical relevance information. BactMentha provides easy access to available experimentally identified binding regions of the interaction partners, complemented with putative interaction interfaces based on predicted structure complexes and on the detection of host-like elements on bacterial protein sequences interacting with human proteins. BactMentha currently contains 11 776 unique protein interactions between 2970 bacterial proteins, of which 684 have at least one virulence annotation, and 5088 host proteins. Experimentally identified binding regions are available for 532 bacteria-host interactions, most of which involve human proteins (440 interactions). Predicted interaction interfaces enabled a 3.7-fold increase in known and putative binding regions in the bacteria-human protein interaction data. By providing easy access to functionally annotated and clinically relevant protein interaction data, enriched with experimentally and predicted interaction interfaces, BactMentha can be instrumental in formulating novel hypotheses and speeding up wet lab research on the molecular mechanisms underlying pathogenic bacterial infections.