Improved annotation of antibiotic resistance determinants reveals microbial resistomes cluster by ecology

By Molly K. Gibson, Kevin J. Forsberg, Gautam Dantas. The ISME Journal, 2014

614 citations recorded by the source corpus.

Subfields: Molecular Biology, Molecular Medicine, Pollution

View Paper: https://www.semanticscholar.org/paper/70ca8cbf7790b0a312ad917dc2d8d557b88c8157

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Peer Bork
2. Ivica Letunic
3. Curtis Huttenhower
4. Jaime Huerta-Cepas
5. Nicola Segata
6. Kristoffer Forslund
7. Aron Marchler-Bauer
8. Roxanne A. Yamashita

In Molecular Medicine

1. Molly K. Gibson
2. Gautam Dantas

In Pollution

1. Curtis Huttenhower
2. Nicola Segata
3. Robert G. Beiko
4. Gautam Dantas

Citations in the corpus

• High-Specificity Targeted Functional Profiling in Microbial Communities with ShortBRED (2015, methodology): “We identified centroid sequences (appropriate for the more shallow 454 sequencing in [12]) and ShortBRED markers (for Illumina sequences from 10–12) for 849 AR protein families derived from the ARDB and independent curation [16].”
• proGenomes: a resource for consistent functional and taxonomic annotations of prokaryotic genomes (2016, methodology): “For proteins with no CARD resistance gene annotation, the best ResFams HMM hit above threshold was retained.”
• CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database (2019, methodology): “Others re-package the content of other AMR databases to provide an alternative database (17), tool (18) or statistical model (19).”
• RefSeq: an update on prokaryotic genome annotation and curation (2017, methodology): “Resources used during this biocuration effort included ResFams (18), CARD (19), ResFinder (20), among others.”
• Validating the AMRFinder Tool and Resistance Gene Database by Using Antimicrobial Resistance Genotype-Phenotype Correlations in a Collection of Isolates (2019, methodology): “First, protein annotation and similarity comparisons against both reference proteins and using HMMs with appropriate cutoffs can aid in determining if the gene is in frame and of correct length, whereas a nucleotide approach can miss nonsense mutations.”
• American Gut: an Open Platform for Citizen Science Microbiome Research (2018, neutral): “2 (110, 111), to identify putative antibiotic resistance genes.”
• Antibiotic-Resistance Genes in Waste Water. (2017, methodology): “Also, the HMM (Hidden Markov Model)-based database, Resfams [24], and updated version of the CARD database [28], contain sets of verified genes.”
• MEGARes: an antimicrobial resistance database for high throughput sequencing (2016, methodology): “read metagenomic data can result in false positive AMR gene identifications (43), the AmrPlusPlus pipeline can filter out genes with a gene fraction below a user-defined threshold using a custom C++ program called ResistomeAnalyzer (source code available at https://github.”
• Dissemination of Antimicrobial Resistance in Microbial Ecosystems through Horizontal Gene Transfer (2016, background): “Recent metagenomic studies have also uncovered that ARGs predominantly cluster by ecology, implying that the resistome in soils, and wastewater treatment plants differ significantly from that of human pathogens (Gibson et al., 2015; Munck et al., 2015).”