u-CARE: user-friendly Comprehensive Antibiotic resistance Repository of<i>Escherichia coli</i>

Saha, Saurav; Uttam, Vishwas; Verma, Vivek

doi:10.1136/jclinpath-2015-202927

Cited by 12 publications

(6 citation statements)

References 16 publications

(14 reference statements)

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“…The data on antibiotic resistance genes/proteins was collected from various databases that contains information about antibiotic resistance genes/proteins such as ARDB 9 , ARG-ANNOT 24 , CARD 12 , CBMAR 10 , INTEGRALL 25 , RAC 26 , Tetracycline + MLS nomenclature, UCARE 27 , Lahey Clinic, Resfams 13 , ResFinder 11 , HMP 28 , LacED 29 , MvirDB 30 , Institut Pasteur and Patric 31 . Some of these databases are focused only on the ARGs while few provide the ARG information as a part of the larger schema such as integrons (INTEGRALL), Antibiotic resistance Cassettes (RAC).…”

Section: Retrieval Of Antibiotic Resistance Gene and Protein Sequencesmentioning

confidence: 99%

BacARscan: A Comprehensive and Interactive Web-Resource to Discern Antibiotic Resistance Gene Diversity in –Omics Datasets

Pandey

Kumari

Singhal

et al. 2021

Preprint

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Regular surveillance of antibiotic resistance genes (ARGs) is important to understand the emergence and epidemiology of antibiotic resistance (AR) in clinical and environmental niches. With diminishing costs, NGS technologies are anticipated to replace classical microbiological and molecular methods for determination of AR. One major hindrance underlying identification and annotation of ARGs from WGS data is that a major part of genome databases contain fragmented genes/genomes (due to incomplete assembly). Herein, we propose a web resource of Bacterial ARGs, named as BacARscan (Bacterial Antibiotic Resistance scan), to detect, predict and characterize ARGs in metagenomic, genomic and proteomic data. The current version of BacARscan comprises 254 ARG models, each annotated with a resistant profile against different classes of antibiotics, resistance mechanism etc. Benchmarking on a combined dataset of AR and non-AR proteins found 92% precision & 95% F-measure. BacARscan can also discriminate between the protein families that are homologous but not all families are involved in the AR. BacARscan identified more ARGs in (a) gut microbiome and (b) datasets comprising short read genomic and proteomic sequences of ESKAPE pathogens. Analysis of clinical metagenomic data indicated its potential to complement and/or supplement WGS based identification of ARGs in clinical samples. BacARscan standalone software and web-server are freely available at http://proteininformatics.org/mkumar/bacarscan and github repository (https://github.com/University-of-Delhi-south-campus/BacARscan).

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Section: Retrieval Of Antibiotic Resistance Gene and Protein Sequencesmentioning

confidence: 99%

BacARscan: A Comprehensive and Interactive Web-Resource to Discern Antibiotic Resistance Gene Diversity in –Omics Datasets

Pandey

Kumari

Singhal

et al. 2021

Preprint

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“…The contents of the databases such as the bacterial species focused, data types, availability of mutation data were thoroughly studied and compared. Out of these 17 databases, three were beta-lactamases related resources [38][39][40] , five were specific to single species such as uCARE 41 is for E.coli and TBDB, ReSeqTB 42 , TBDReaMDB and MUBII-TB-DB were dedicated to MTB. A comparison of TBDreamDB and MUBII-TB-DB, two well-known databases for mutations associated with drug resistance in MTB with DRAGdb is presented in Table 1.…”

Section: Dragdb Statistics the Basic Statistics Of Dragdb Is Shown Inmentioning

confidence: 99%

Survey of drug resistance associated gene mutations in Mycobacterium tuberculosis, ESKAPE and other bacterial species

Ghosh

Saran

Saha

2020

Sci Rep

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tuberculosis treatment includes broad-spectrum antibiotics such as rifampicin, streptomycin and fluoroquinolones, which are also used against other pathogenic bacteria. We developed Drug Resistance Associated Genes database (DRAGdb), a manually curated repository of mutational data of drug resistance associated genes (DRAGs) across ESKAPE (i.e. Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.) pathogens, and other bacteria with a special focus on Mycobacterium tuberculosis (MTB). Analysis of mutations in drug-resistant genes listed in DRAGdb suggested both homoplasy and pleiotropy to be associated with resistance. Homoplasy was observed in six genes namely gidB, gyrA, gyrB, rpoB, rpsL and rrs. For these genes, drug resistance-associated mutations at codon level were conserved in MTB, ESKAPE and many other bacteria. Pleiotropy was exemplified by a single nucleotide mutation that was associated with resistance to amikacin, gentamycin, rifampicin and vancomycin in Staphylococcus aureus. DRAGdb data also revealed that mutations in some genes such as pncA, inhA, katG and embA,B,C were specific to Mycobacterium species. for inhA and pncA, the mutations in the promoter region along with those in coding regions were associated with resistance to isoniazid and pyrazinamide respectively. In summary, the DRAGdb database is a compilation of all the major MTB drug resistance genes across bacterial species, which allows identification of homoplasy and pleiotropy phenomena of DRAGs. There is a rise in the use of broad spectrum antibiotics such as rifamycins, aminoglycosides and fluoroquinolones against tuberculosis (TB), as well as common bacterial infections such as gastro-intestinal infections 1-3. The multi-and extensively drug-resistant (MDR and XDR) Mycobacterium tuberculosis (MTB) pose a global threat to public health as new resistance mechanisms are developing and making the treatment for patients prolonged and expensive. Drug resistance is not restricted to TB, but also observed in common bacterial infections such as pneumonia and foodborne infections 4,5. Genome-wide analysis of MDR and XDR MTB reveals that drug resistance arises due to mutations in the gene and/or the promoter region. Drug resistance associated mutations are linked to increasing drug efflux, modifications of the drugs or their targets 6-8. The accessibility to next-generation sequencing technologies and characterization of bacteria specific drug resistance allows the extensive study of other pathogenic bacteria as well 9-11. Antibiotic resistance mutations specific to pathogenic bacteria are available. The Infectious Diseases Society of America has grouped Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter spp. as ESKAPE pathogens that are capable of 'escaping' the actions of antibiotics thereby developing antibiotic resistance 12. The ESKAPE pathogens are the leading cause of Hospi...

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“…The User-friendly Comprehensive Antibiotic Resistance Repository of Escherichia coli (U-CARE; http://www .e-bioinformatics.net/ucare/) is a manually curated resource that provides E. coli-related AR information, including information from 52 antibiotics and 107 resistance genes and associated information of transcription factors and SNPs (29).…”

Section: Antibiotic Resistance Gene Databasesmentioning

confidence: 99%

Consolidating and Exploring Antibiotic Resistance Gene Data Resources

et al. 2016

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The unrestricted use of antibiotics has resulted in rapid acquisition of antibiotic resistance (AR) and spread of multidrug-resistant (MDR) bacterial pathogens. With the advent of next-generation sequencing technologies and their application in understanding MDR pathogen dynamics, it has become imperative to unify AR gene data resources for easy accessibility for researchers. However, due to the absence of a centralized platform for AR gene resources, availability, consistency, and accuracy of information vary considerably across different databases. In this article, we explore existing AR gene data resources in order to make them more visible to the clinical microbiology community, to identify their limitations, and to propose potential solutions.

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u-CARE: user-friendly Comprehensive Antibiotic resistance Repository ofEscherichia coli

Cited by 12 publications

References 16 publications

BacARscan: A Comprehensive and Interactive Web-Resource to Discern Antibiotic Resistance Gene Diversity in –Omics Datasets

BacARscan: A Comprehensive and Interactive Web-Resource to Discern Antibiotic Resistance Gene Diversity in –Omics Datasets

Survey of drug resistance associated gene mutations in Mycobacterium tuberculosis, ESKAPE and other bacterial species

Consolidating and Exploring Antibiotic Resistance Gene Data Resources

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