Track resistance genes from source to spread — across environments, hosts, and ecosystems.
Antimicrobial resistance does not respect the boundaries between clinics, farms, and ecosystems. Neither does our analysis. We provide comprehensive resistome profiling and AMR gene characterization for research groups, public health teams, and industry partners — across environmental, clinical, agricultural, and aquaculture samples. We do not just identify which resistance genes are present. We trace how they move between organisms and environments, and deliver the computational evidence you need for surveillance, risk assessment, and publication.
AMR gene detection and quantification from metagenomic, genomic, or metatranscriptomic data using curated databases. We report gene families, resistance mechanisms, drug classes, and relative abundance — with proper normalization and statistical testing for differential abundance between conditions.
Characterization of the full resistance gene repertoire in your samples. Alpha and beta resistome diversity, resistome richness, evenness, and composition shifts across experimental groups, time points, or geographic locations. Includes antibiotic resistance bacteria (ARB) detection and pathogen identification where relevant. We treat the resistome as an ecological entity, not just a gene list.
Every detected gene is classified by mechanism: antibiotic inactivation, target modification, efflux pump activity, target protection, or target replacement. This mechanistic breakdown tells you not just what the community can resist, but how it resists. Understanding the mechanism is critical for assessing clinical relevance and designing intervention strategies.
Identification of plasmid-borne resistance genes from assembled contigs or long-read data. Plasmid reconstruction, incompatibility group typing, and assessment of which resistance genes sit on mobile versus chromosomal elements. This directly addresses the transferability risk of detected resistance.
Detection and characterization of integrons (class 1, 2, 3), transposable elements, and insertion sequences flanking resistance genes. We map the genetic context around AMR genes: what is upstream, what is downstream, and what mobile machinery could move them.
Assessment of HGT signatures: conjugation machinery, phage-mediated transfer markers, and genomic islands carrying resistance cassettes. We evaluate not just whether resistance genes are present, but how likely they are to spread to new hosts.
Linking resistance genes to their microbial hosts using MAG-based or contig-based approaches. We answer the critical question: which organisms are carrying which resistance genes? Knowing that a resistance gene exists is less useful than knowing which species is harboring and potentially spreading it.
See also: Shotgun Metagenomics and MAG Reconstruction →For studies spanning multiple environments (wastewater, soil, clinical isolates, animal gut), we apply source-tracking approaches to evaluate the likely origin and directionality of resistance gene flow. Which environment is the reservoir? Where is the transmission occurring?
Where data quality permits, strain-level resolution of AMR carriage, distinguishing resistant and susceptible strains of the same species within a community. Additionally, antibiotic resistance bacteria (ARB) detection and pathogen identification using curated databases. Critical for outbreak investigation, surveillance studies, and pathogen tracking.
How do resistance genes change over time? We track abundance, diversity, and composition across treatment regimens, seasonal cycles, intervention trials, and environmental perturbation events — identifying which genes are transient, which persist, and which respond to specific pressures.
Pre- and post-treatment resistome comparison for antibiotic treatment studies, disinfection interventions, or process modifications. We quantify the effect of interventions on resistome composition and identify genes that persist, emerge, or are eliminated.
Joint analysis of resistome and microbiome shifts: do resistance genes track specific taxa? Does community disruption open ecological niches for resistant organisms? We connect resistome dynamics to the underlying microbial ecology driving them.
Systematic comparison of resistome profiles across One Health compartments (human clinical samples, animal gut/feces, agricultural soil, water/wastewater, and food products) within a unified analytical framework. We identify shared resistance genes and gene variants across compartments, providing evidence for cross-compartment transmission.
Baseline and monitoring-level resistome characterization for wastewater treatment plants, river systems, agricultural runoff, aquaculture facilities, and other environmental matrices. We provide the data layer needed for AMR surveillance programs and environmental risk assessments.
For studies bridging clinical and environmental sampling, we evaluate whether resistance genes or resistant organisms detected in environmental samples match those found in clinical isolates, providing evidence-level support for environmental transmission pathways.
Every project gets a tailored analysis package — built around your question, sample type, and One Health context. Every deliverable goes directly into your manuscript, surveillance report, or technical documentation.
A comprehensive, narrated report covering all detected resistance genes, their mechanisms, host associations, mobile element context, and ecological interpretation. For academic clients, structured for direct insertion into manuscripts. For public health and industry clients, formatted as a surveillance or risk assessment report with executive summary.
High-resolution figures: resistome composition barplots, heatmaps, diversity plots, network diagrams, gene context maps, and cross-compartment comparison panels. Consistent styling at journal resolution (300+ dpi, PDF/SVG/PNG).
Complete gene-level output: gene name, accession, resistance mechanism, drug class, percent identity, coverage, host assignment, and genomic context (plasmid/chromosome, flanking MGEs). Sortable, filterable, ready for supplementary data submission.
Processed abundance matrices and database versions used.
Ready-to-use methods section citing all databases, tools, versions, and parameters, including appropriate references for CARD, ResFinder, AMRFinderPlus, and any other resources used.
Multi-database AMR detection, mobile element characterization, and ecological analysis, all integrated within reproducible, version-controlled pipelines.
Six structured steps from study design through final delivery, with a specific focus on making raw resistance gene data interpretable in the ecological and public health context that matters for your work.
You share your question, study design, sample types, and the One Health context. We review your sampling strategy, sequencing approach, and metadata capture — and tell you whether they are sufficient to answer your question. For surveillance projects, we advise on sampling frequency, spatial coverage, and detection sensitivity.
Quality assessment, adapter trimming, quality filtering, and host decontamination. For resistome work, we also assess sequencing depth against AMR gene detection sensitivity. Undersequenced samples miss low-abundance resistance genes — we flag this before a single analysis runs.
AMR gene detection across multiple curated databases, with mechanism classification, drug class assignment, and confidence scoring. We do not rely on a single database — cross-referencing CARD, ResFinder, and AMRFinderPlus reduces false positives and catches what any one database would miss.
Host attribution, mobile element characterization, co-occurrence analysis, and cross-compartment comparison. This is where raw gene lists become biological insight — not just which resistance genes are present, but who carries them, how they might transfer, and what drives their distribution.
We do not deliver a gene table and leave you to interpret it. Results come as a coherent narrative — what the resistome looks like, how it compares across conditions, what the public health implications are, and what follow-up would strengthen the evidence.
You receive the full package. We stay available through publication, grant reporting, or surveillance report submission — reviewer responses and additional analyses included.
Four tiers matched to the scope and depth of your resistome question, ranging from a baseline snapshot to full One Health surveillance analysis. All packages include annotated gene tables, reproducible scripts, and post-delivery support. Pricing provided upon consultation.
Best for: A lab or student needing baseline resistome characterization from a small set of metagenomic samples.
Best for: Research groups wanting to understand not just which AMR genes are present, but who carries them and how mobile they are.
Best for: Multi-compartment studies, surveillance programs, or projects requiring cross-system AMR tracking.
Best for: Public health agencies, environmental monitoring programs, or research centers running long-term AMR surveillance.
Tracking resistance genes across a watershed? Characterizing AMR in clinical isolates? Building a One Health surveillance dataset? Send us your samples and your question — we will design the right analytical strategy.
Discuss Your ProjectTypical response time: 48 hours. We work with environmental, clinical, agricultural, and aquaculture samples from any sequencing platform.