Response to: “best practices when benchmarking CATCH for the design of genome enrichment probes”

Bioinformatics · 2026-02-28

We clarify the design principles and evaluation choices underlying Syotti, a robust and scalable probe-design tool developed to support large, heterogeneous bacterial datasets with minimal parameter tuning. We highlight Syotti's ability to perform simultaneous large-scale designs and its effectiveness as a reliable alternative when existing tools such as CATCH are not well suited to the problem setting.

An atlas of the maturing nasopharyngeal microbiome in dairy calves from birth to weaning: Protocol for a microbiome-based systematic review and meta-analysis

Open MIND · 2026-01-01

Systematic review and meta-analysis about upper respiratory tract microbiome maturation in pre-weaned dairy calves.

Reducing skin microbiome exposure impacts through swine farm biosecurity

GigaScience · 2025-01-01 · 1 citations

BACKGROUND: Livestock work is unique due to worker exposure to animal-associated microbiomes within the workplace. Swine workers are a unique cohort within the US livestock labor force, as they have direct daily contact with pigs and undertake mandatory biosecurity interventions. However, investigating this occupational cohort is challenging, particularly within tightly regulated commercial swine operations. Thus, little is known about the impacts of animal exposure and biosecurity protocols on the swine worker microbiome. We obtained unique samples from US swine workers, using a longitudinal study design to investigate temporal microbiome dynamics. RESULTS: We observed a significant increase in bacterial DNA load on worker skin during the workday, with concurrent changes in the composition and abundance of microbial taxa, resistance genes, and mobile genetic elements. However, mandatory showering at the end of the workday partially returned the skin's microbiome and resistome to their original state. CONCLUSIONS: These novel results from a human cohort demonstrate that existing biosecurity practices can ameliorate work-associated microbiome impacts.

S2047 Fecal Microbiota Transplantation Therapy Alters Resistome Burden and Mobilization Potential in a Disease-Dependent Manner

The American Journal of Gastroenterology · 2025-10-01

Introduction: Antimicrobial resistance (AMR) is a global health threat that contributes to millions of deaths annually. Fecal microbiota transplantation (FMT) offers a non-antibiotic strategy to treating gastrointestinal microbiome-related disorders, especially recurrent Clostridioides difficile (rCDI), yet its impact on AMR gene (ARG) dynamics and mobile genetic elements (MGEs) remains poorly understood. Understanding these effects is crucial as FMT adoption broadens into new clinical indications beyond rCDI treatment. Methods: We analyzed 263 publicly available metagenomic samples from FMT donors and recipients across rCDI, multidrug-resistant bacterial (MDRB) infections, and melanoma, reflecting disease processes with variable disruptions to patients’ native intestinal microbiota. Resistome and mobilome profiles were characterized using AMR++ and MGE classification pipelines with longitudinal comparisons and differential abundance assessed via ANCOM-BC2 (FDR-adjusted P < 0.05). Colocalization analyses were performed using the TELCoMB pipeline to evaluate ARG mobility risk potential. Results: In rCDI samples, both ARG and MGE diversity shifted markedly across donor, pre-FMT, and post-FMT samples. Differential abundance analysis revealed several significantly altered ARG and MGE genes pre- and post-FMT (FDR-adjusted P < 0.05). Among the ARGs shared between pre- and post-FMT samples, ∼12% significantly changed in abundance, with a majority (∼67%) being enriched. In contrast to rCDI, melanoma and MDRB cohorts exhibited minimal shifts in ARG and MGE diversity post-FMT, with >97% of genes stable and <2% showing significant change. Colocalization analysis showed that FMT recipients–especially rCDI patients–harbored more unique ARG–MGE colocalizations than donors. Both pre- and post-FMT rCDI samples exhibited colocalization rates >10× higher than donors, indicating sustained or elevated resistomes amenable to horizontal gene transfer. Conclusion: Our findings, which accounted for prior antibiotic treatment, FMT preparation, and FMT route, highlight that FMTs reshape the resistome and mobilome in a disease-specific manner. These findings underscore the need for careful resistome surveillance in FMT recipients and highlight the potential of FMTs to modulate both AMR burden and gene mobility in the human gastrointestinal tract.

The gut microbiome and resistome of yellow perch (Perca flavescens) living in Minnesota lakes under varying anthropogenic pressure

One Health · 2024-11-12 · 1 citations

Anthropogenic activities can significantly impact wildlife in natural water bodies, affecting not only the host's physiology but also its microbiome. This study aimed to analyze the gut microbiome and antimicrobial resistance gene profile (i.e., the resistome) of yellow perch living in lakes subjected to different levels of anthropogenic pressure: wastewater effluent-impacted lakes and undeveloped lakes. Total DNA and RNA from gut content samples were extracted and sequenced for analysis. Results indicate that the gut resistome and microbiome of yellow perch differ between lakes, perhaps due to varying anthropogenic pressure. The resistome was predominated by macrolide resistance genes, particularly the MLS23S group, making up 53 % of resistome sequences from effluent-impacted lakes and 73 % from undeveloped lakes. The colistin resistance gene group ( mcr ) was detected in numerous samples, including variants associated with Aeromonas and the family Enterobacteriaceae . The gut microbiome across all samples was dominated by the phyla Proteobacteria, Firmicutes, and Actinobacteria, with the opportunistic pathogens Plesiomonas shigelloides and Aeromonas veronii more abundant in effluent-impacted lakes. Metagenomic analysis of wild fish samples offers valuable insights into the effects of anthropogenic pressures on microbial communities, including antimicrobial resistance genes, in water bodies.

The TELCoMB Protocol for High‐Sensitivity Detection of ARG‐MGE Colocalizations in Complex Microbial Communities

Current Protocols · 2024-10-01 · 2 citations

Understanding the genetic basis of antimicrobial resistance is crucial for developing effective mitigation strategies. One necessary step is to identify the antimicrobial resistance genes (ARGs) within a microbial population, referred to as the resistome, as well as the mobile genetic elements (MGEs) harboring ARGs. Although shotgun metagenomics has been successful in detecting ARGs and MGEs within a microbiome, it is limited by low sensitivity. Enrichment using cRNA biotinylated probes has been applied to address this limitation, enhancing the detection of rare ARGs and MGEs, especially when combined with long-read sequencing. Here, we present the TELCoMB protocol, a Snakemake workflow that elucidates resistome and mobilome composition and diversity and uncovers ARG-MGE colocalizations. The protocol supports both short- and long-read sequencing and does not require enrichment, making it versatile for various genomic data types. TELCoMB generates publication-ready figures and CSV files for comprehensive analysis, improving our understanding of antimicrobial resistance mechanisms and spread. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Installing TELCOMB Locally Alternate Protocol: Installing TELCOMB on a SLURM Cluster Basic Protocol 2: Data Preprocessing Basic Protocol 3: Calculation of Resistome Distribution and Composition Basic Protocol 4: Identification of ARG-MGE Colocalizations.

Factors impacting target-enriched long-read sequencing of resistomes and mobilomes

Genome Research · 2024-11-01 · 6 citations

We investigated the efficiency of target-enriched long-read sequencing (TELSeq) for detecting antimicrobial resistance genes (ARGs) and mobile genetic elements (MGEs) within complex matrices. We aimed to overcome limitations associated with traditional antimicrobial resistance (AMR) detection methods, including short-read shotgun metagenomics, which can lack sensitivity, specificity, and the ability to provide detailed genomic context. By combining biotinylated probe-based enrichment with long-read sequencing, we facilitated the amplification and sequencing of ARGs, eliminating the need for bioinformatic reconstruction. Our experimental design included replicates of human fecal microbiota transplant material, bovine feces, pristine prairie soil, and a mock human gut microbial community, allowing us to examine variables including genomic DNA input and probe set composition. Our findings demonstrated that TELSeq markedly improves the detection rates of ARGs and MGEs compared to traditional sequencing methods, underlining its potential for accurate AMR monitoring. A key insight from our research is the importance of incorporating mobilome profiles to better predict the transferability of ARGs within microbial communities, prompting a recommendation for the use of combined ARG-MGE probe sets for future studies. We also reveal limitations for ARG detection from low-input workflows, and describe the next steps for ongoing protocol refinement to minimize technical variability and expand utility in clinical and public health settings. This effort is part of our broader commitment to advancing methodologies that address the global challenge of AMR.

Slaughtering processes impact microbial communities and antimicrobial resistance genes of pig carcasses

The Science of The Total Environment · 2024-06-30 · 16 citations

AMR-meta: a <i>k</i>-mer and metafeature approach to classify antimicrobial resistance from high-throughput short-read metagenomics data

GigaScience · 2022-01-01 · 17 citations

BACKGROUND: Antimicrobial resistance (AMR) is a global health concern. High-throughput metagenomic sequencing of microbial samples enables profiling of AMR genes through comparison with curated AMR databases. However, the performance of current methods is often hampered by database incompleteness and the presence of homology/homoplasy with other non-AMR genes in sequenced samples. RESULTS: We present AMR-meta, a database-free and alignment-free approach, based on k-mers, which combines algebraic matrix factorization into metafeatures with regularized regression. Metafeatures capture multi-level gene diversity across the main antibiotic classes. AMR-meta takes in reads from metagenomic shotgun sequencing and outputs predictions about whether those reads contribute to resistance against specific classes of antibiotics. In addition, AMR-meta uses an augmented training strategy that joins an AMR gene database with non-AMR genes (used as negative examples). We compare AMR-meta with AMRPlusPlus, DeepARG, and Meta-MARC, further testing their ensemble via a voting system. In cross-validation, AMR-meta has a median f-score of 0.7 (interquartile range, 0.2-0.9). On semi-synthetic metagenomic data-external test-on average AMR-meta yields a 1.3-fold hit rate increase over existing methods. In terms of run-time, AMR-meta is 3 times faster than DeepARG, 30 times faster than Meta-MARC, and as fast as AMRPlusPlus. Finally, we note that differences in AMR ontologies and observed variance of all tools in classification outputs call for further development on standardization of benchmarking data and protocols. CONCLUSIONS: AMR-meta is a fast, accurate classifier that exploits non-AMR negative sets to improve sensitivity and specificity. The differences in AMR ontologies and the high variance of all tools in classification outputs call for the deployment of standard benchmarking data and protocols, to fairly compare AMR prediction tools.

Syotti: scalable bait design for DNA enrichment

Bioinformatics · 2022-04-14 · 17 citations

MOTIVATION: Bait enrichment is a protocol that is becoming increasingly ubiquitous as it has been shown to successfully amplify regions of interest in metagenomic samples. In this method, a set of synthetic probes ('baits') are designed, manufactured and applied to fragmented metagenomic DNA. The probes bind to the fragmented DNA and any unbound DNA is rinsed away, leaving the bound fragments to be amplified for sequencing. Metsky et al. demonstrated that bait-enrichment is capable of detecting a large number of human viral pathogens within metagenomic samples. RESULTS: We formalize the problem of designing baits by defining the Minimum Bait Cover problem, show that the problem is NP-hard even under very restrictive assumptions, and design an efficient heuristic that takes advantage of succinct data structures. We refer to our method as Syotti. The running time of Syotti shows linear scaling in practice, running at least an order of magnitude faster than state-of-the-art methods, including the method of Metsky et al. At the same time, our method produces bait sets that are smaller than the ones produced by the competing methods, while also leaving fewer positions uncovered. Lastly, we show that Syotti requires only 25 min to design baits for a dataset comprised of 3 billion nucleotides from 1000 related bacterial substrains, whereas the method of Metsky et al. shows clearly super-linear running time and fails to process even a subset of 17% of the data in 72 h. AVAILABILITY AND IMPLEMENTATION: https://github.com/jnalanko/syotti. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.