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Record W4410122355 · doi:10.1186/s13073-025-01480-2

Simultaneous detection of pathogens and antimicrobial resistance genes with the open source, cloud-based, CZ ID platform

2025· article· en· W4410122355 on OpenAlexafffund
Dan Lu, Katrina Kalantar, Abigail Glascock, Victoria Chu, Estella Sanchez Guerrero, Nina Bernick, Xochitl Butcher, Kirsty Ewing, Elizabeth Fahsbender, Olivia Holmes, Erin Hoops, Ann E. Jones, Ryan G. Lim, Suzette McCanny, Lucia Reynoso, Karyna Rosario, Jennifer Tang, Peter M. Mourani, Amy J. Pickering, Amogelang R. Raphenya, Brian Alcock, Andrew G. McArthur, Charles Langelier

Bibliographic record

VenueGenome Medicine · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsMcMaster University
FundersNational Heart, Lung, and Blood InstituteCanadian Institutes of Health ResearchNational Institutes of HealthChan Zuckerberg InitiativeNational Institute of Allergy and Infectious DiseasesMcMaster University
KeywordsResistomeMetagenomicsAntibiotic resistanceWorkflowComputational biologyBiodefenseGenomeBioinformaticsBiologyGeneComputer scienceGeneticsMicrobiologyAntibioticsDatabaseMobile genetic elements

Abstract

fetched live from OpenAlex

BACKGROUND: Antimicrobial resistant (AMR) pathogens represent urgent threats to human health, and their surveillance is of paramount importance. Metagenomic next-generation sequencing (mNGS) has revolutionized such efforts, but remains challenging due to the lack of open-access bioinformatics tools capable of simultaneously analyzing both microbial and AMR gene sequences. RESULTS: To address this need, we developed the Chan Zuckerberg ID (CZ ID) AMR module, an open-access, cloud-based workflow designed to integrate detection of both microbes and AMR genes in mNGS and single-isolate whole-genome sequencing (WGS) data. It leverages the Comprehensive Antibiotic Resistance Database and associated Resistance Gene Identifier software, and works synergistically with the CZ ID short-read mNGS module to enable broad detection of both microbes and AMR genes from Illumina data. We highlight diverse applications of the AMR module through analysis of both publicly available and newly generated mNGS and single-isolate WGS data from four clinical cohort studies and an environmental surveillance project. Through genomic investigations of bacterial sepsis and pneumonia cases, hospital outbreaks, and wastewater surveillance data, we gain a deeper understanding of infectious agents and their resistomes, highlighting the value of integrating microbial identification and AMR profiling for both research and public health. We leverage additional functionalities of the CZ ID mNGS platform to couple resistome profiling with the assessment of phylogenetic relationships between nosocomial pathogens, and further demonstrate the potential to capture the longitudinal dynamics of pathogen and AMR genes in hospital acquired bacterial infections. CONCLUSIONS: In sum, the new AMR module advances the capabilities of the open-access CZ ID microbial bioinformatics platform by integrating pathogen detection and AMR profiling from mNGS and single-isolate WGS data. Its development represents an important step toward democratizing pathogen genomic analysis and supporting collaborative efforts to combat the growing threat of AMR.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.230
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2025
Admission routes2
Has abstractyes

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