ARG-ANNOT, a New Bioinformatic Tool To Discover Antibiotic Resistance Genes in Bacterial Genomes
Sushim K. Gupta,Babu Roshan Padmanabhan,Seydina M. Diene,Rafael López-Rojas,Marie Kempf,Luce Landraud,Jean-Marc Rolain +6 more
TLDR
A concise database for BLAST using a Bio-Edit interface that can detect AR genetic determinants in bacterial genomes and can rapidly and easily discover putative new AR geneticeterminants is created.Abstract:
ARG-ANNOT (Antibiotic Resistance Gene-ANNOTation) is a new bioinformatic tool that was created to detect existing and putative new antibiotic resistance (AR) genes in bacterial genomes. ARG-ANNOT uses a local BLAST program in Bio-Edit software that allows the user to analyze sequences without a Web interface. All AR genetic determinants were collected from published works and online resources; nucleotide and protein sequences were retrieved from the NCBI GenBank database. After building a database that includes 1,689 antibiotic resistance genes, the software was tested in a blind manner using 100 random sequences selected from the database to verify that the sensitivity and specificity were at 100% even when partial sequences were queried. Notably, BLAST analysis results obtained using the rmtF gene sequence (a new aminoglycoside-modifying enzyme gene sequence that is not included in the database) as a query revealed that the tool was able to link this sequence to short sequences (17 to 40 bp) found in other genes of the rmt family with significant E values. Finally, the analysis of 178 Acinetobacter baumannii and 20 Staphylococcus aureus genomes allowed the detection of a significantly higher number of AR genes than the Resfinder gene analyzer and 11 point mutations in target genes known to be associated with AR. The average time for the analysis of a genome was 3.35 ± 0.13 min. We have created a concise database for BLAST using a Bio-Edit interface that can detect AR genetic determinants in bacterial genomes and can rapidly and easily discover putative new AR genetic determinants.read more
Citations
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Journal ArticleDOI
Plasmid classification in an era of whole-genome sequencing: application in studies of antibiotic resistance epidemiology.
Alex Orlek,Alex Orlek,Nicole Stoesser,Muna F. Anjum,Muna F. Anjum,Michel Doumith,Matthew J. Ellington,Matthew J. Ellington,Tim E. A. Peto,Tim E. A. Peto,Derrick W. Crook,Derrick W. Crook,Neil Woodford,Neil Woodford,A. Sarah Walker,A. Sarah Walker,Hang Phan,Hang Phan,Anna E. Sheppard,Anna E. Sheppard +19 more
TL;DR: The application of plasmid classification in WGS-based studies of antibiotic resistance epidemiology is discussed, and novel in silico plasmids analysis tools are highlighted.
Journal ArticleDOI
Next-Generation Sequencing for Infectious Disease Diagnosis and Management: A Report of the Association for Molecular Pathology
TL;DR: Although NGS holds enormous promise for clinical infectious disease testing, many challenges remain, including automation, standardizing technical protocols and bioinformatics pipelines, improving reference databases, establishing proficiency testing and quality control measures, and reducing cost and turnaround time, all of which would be necessary for widespread adoption of NGS in clinical microbiology laboratories.
Posted ContentDOI
DeepARG: A deep learning approach for predicting antibiotic resistance genes from metagenomic data
Gustavo Arango-Argoty,Emily Garner,Amy Pruden,Lenwood S. Heath,Peter J. Vikesland,Liqing Zhang +5 more
TL;DR: A deep leaning approach to ARG forecasting is proposed, taking into account a dissimilarity matrix created using all known categories of ARGs, which demonstrates that the deepARG models can predict ARGs with both high precision and recall for most of the antibiotic resistance categories.
Journal ArticleDOI
A fast and agnostic method for bacterial genome-wide association studies: Bridging the gap between k-mers and genetic events.
Magali Jaillard,Magali Jaillard,Leandro Lima,Leandro Lima,Maud Tournoud,Pierre Mahé,Alex van Belkum,Vincent Lacroix,Vincent Lacroix,Laurent Jacob +9 more
TL;DR: DBGWAS as discussed by the authors uses compacted De Bruijn graphs (cDBG) to gather nodes, identified by the association model, into subgraphs defined from their neighbourhood in the initial cDBG.
Journal ArticleDOI
Antibiotic resistome in a large-scale healthy human gut microbiota deciphered by metagenomic and network analyses
TL;DR: Co-occurrence patterns obtained via network analysis implied that 12 species might be potential hosts of 58 ARG subtypes, suggesting their common occurrence in the human gut.
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