Evidence map›Paper›PMID 35583675›Full record

ArticleGigaScience2022

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

Simone Marini, Marco Oliva, Ilya B Slizovskiy, Rishabh A Das, Noelle Robertson Noyes, Tamer Kahveci, Christina Boucher, Mattia Prosperi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in GigaScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. TheFrontiers in microbiology · 2023
    Article
  5. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Simone MariniDepartment of Computer and Information Science and Engineering, University of Florida, 2004 Mowry Road Gainesville, FL 32610, USA.ORCID 0000-0002-5704-3533
Marco OlivaDepartment of Computer and Information Science and Engineering, University of Florida, 432 Newell Dr, Gainesville, FL 32611, USA.ORCID 0000-0003-0525-3114
Ilya B SlizovskiyDepartment of Veterinary Population Medicine, University of Minnesota, 1365 Gortner Avenue 225, St. Paul, MN 55108, USA.ORCID 0000-0002-0758-5510
Rishabh A DasDepartment of Computer and Information Science and Engineering, University of Florida, 2004 Mowry Road Gainesville, FL 32610, USA.
Noelle Robertson NoyesDepartment of Veterinary Population Medicine, University of Minnesota, 1365 Gortner Avenue 225, St. Paul, MN 55108, USA.ORCID 0000-0001-6149-1008
Tamer KahveciDepartment of Computer and Information Science and Engineering, University of Florida, 432 Newell Dr, Gainesville, FL 32611, USA.
Christina BoucherDepartment of Computer and Information Science and Engineering, University of Florida, 432 Newell Dr, Gainesville, FL 32611, USA.ORCID 0000-0001-9509-9725
Mattia ProsperiDepartment of Computer and Information Science and Engineering, University of Florida, 2004 Mowry Road Gainesville, FL 32610, USA.ORCID 0000-0002-9021-5595

Funding

Developing Computational Methods for Surveillance of Antimicrobial Resistant AgentsR01AI141810 · NIAID · UNIVERSITY OF FLORIDA · PI BOUCHER, CHRISTINA, PROSPERI, MATTIA · 2019 to 2023
$2.1M
NIAID NIH HHS R01 AI141810
6 · The paper itself

Abstract

backgroundAntimicrobial 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.

resultsWe 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.

conclusionsAMR-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.

Indexed as

Anti-Bacterial AgentsMetagenomicsDrug Resistance, BacterialHigh-Throughput Nucleotide SequencingMetagenomeAnti-Bacterial Agentsantimicrobial resistancefunctional metagenomicsmachine learningmatrix factorizationshort reads

Identifiers

PMID35583675
PMCPMC9116207

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.