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ARTS / Antibiotic Resistant Target Seeker
Allows for specific and efficient genome mining for antibiotics with interesting and novel targets. ARTS automates the screening of large amounts of sequence data. It aims to focus on the most promising strains that produce antibiotics with new modes of action. The tool includes target directed genome mining methods, antibiotic gene cluster predictions and ‘essential gene screening’ functions. It can be used as a cluster prioritization tool and can detect complement current methods to the orthogonal detection method.
A code for calculation and analysis of positional mutual information. positionalmi is an information-theoretic metric to sensitively and robustly detect both local and distant residues that affect substrate conformation and catalytic activity. This code was used in multiple microsecond-length molecular dynamics simulations to predict residues linked to catalytic activity of the CTX-M9 beta lactamase, in a drug resistance study. Excess mutual information quantifies drug-protein positional coupling in a fashion corrected for protein motions and capable of robustly identifying even weak but physically significant coupling. It measures the symmetric uncertainty between a protein atom and the beta-lactam ring but corrects for bulk protein motion by subtracting the average symmetric uncertainty to the rest of the protein.
The Resistome
Contains over 5,000 mutants of Escherichia coli genotypes-phenotypes that resist hundreds of compounds and environmental conditions. The Resistome is composed of flat key-value records, where each record represents a single study containing one or more mutants of a given E. coli species. It provides a standardized, consistent window into our current state of knowledge regarding resistance phenotypes, in the hopes that researchers can better understand future experiments by looking into the past for contextualization.
Identifies and localizes virulence or antibiotic resistance genes and extended mobilome-related gene clusters as well, in sequenced bacterial genomes. VRprofile is assisted by CDSeasy to quickly annotate newly sequenced chromosomes, by CGCfinder to detect gene clusters, by COGviewer to localize and cluster user-provided COGs, and by MobilomeDB that collected and organized the known data about virulence factors and antibiotic resistance determinants on the single gene and gene cluster scale.
CancerDR / Cancer Drug Resistance database
Provides information of 148 anti-cancer drugs, and their pharmacological profiling across 952 cancer cell lines. CancerDR provides comprehensive information about each drug target that includes; (i) sequence of natural variants, (ii) mutations, (iii) tertiary structure, and (iv) alignment profile of mutants/variants. A number of web-based tools have been integrated in CancerDR. This database will be very useful for identification of genetic alterations in genes encoding drug targets, and in turn the residues responsible for drug resistance.
A method to detect and annotate novel classes of qnr antibiotic resistance genes in nucleotide sequence data. Qnr-search uses a hidden Markov model with a fragment length-dependent classification rule and has a high sensitivity and specificity, even for sequences as short at 100 nucleotides. This makes the method directly applicable to the immense amount of data generated by the next-generation DNA sequencing techniques. Based on sequence data currently available in the repositories, the method was able to identify all previously reported plasmid-mediated qnr genes as well as the vast majority of the previously reported chromosomal variants. In addition, the method predicted several novel putative qnr genes and some of these were discovered in shotgun metagenomes, which may indicate a large and unknown diversity of qnr genes in uncultured environmental bacteria.
GWAMAR / Genome-wide assessment of mutations associated with drug resistance in bacteria
Employs eCAMBer tool to identify homologous gene families, mutations among the strains of interest and which mutations are the most associated with drug-resistance. GWAMAR is a pipeline developed for identifying of drug-resistance associated mutations based on comparative analysis of whole-genome sequences in bacterial strains. It includes: (i) download of genome sequences and gene annotations, (ii) unification of gene annotations among the set of considered strains, (iii) identification of gene families, (iv) computation of multiple alignments and identification of point mutations which constitute the input genotype data.
A web service for drug resistance prediction of commonly used drugs in antiretroviral therapy, i.e., protease inhibitors (PIs), reverse transcriptase inhibitors (NRTIs and NNRTIs), and integrase inhibitors (INIs), but also for the novel drug class of maturation inhibitors. SHIVA provides 24 prediction models for several drug classes. SHIVA can be used with single RNA/DNA or amino acid sequences, but also with large amounts of next-generation sequencing data and allows prediction of a user specified selection of drugs simultaneously.
MATEPred / Multidrug And Toxin Extrusion proteins PREDiction
Identifies Multidrug And Toxin Extrusion (MATE) proteins. MATEPred is based on Position Specific Scoring Matrix (PSSM) and uses Support Vector Machine (SVM). It returns sequence number, score and decision of the model. The tool was used to scan the proteomes of Vibrio parahaemolyticus and Shigella boydii for the presence of MATE proteins. It is able to differentiate MATE sequences from non-MATE sequences on the basis of PSSM profile.
RM-seq / Resistance Mutation SEQuencing
Allows unbiased quantification of resistance alleles from complex in vitro derived resistant clone libraries. RM-seq recognizes and characterizes mutational resistance and its consequences. It employs the capability of bacteria to quickly develop resistance in vitro to proceed. This tool was tested on a defined population of genetically reconstructed rifampicin resistant clones. It can be used for any combination of microorganisms and resistance.
MDP / Mutations and Drugs Portal
Allows users to predict cancer cell lines mutations and drugs sensitivity dependencies. MDP provides a web interface that permits to inquire the National Cancer Institute's anticancer drug screen data (DTP NCI60) and the Cancer Cell Line Encyclopedia (CCLE). The software permits to perform three analysis for determining: (i) drugs correlated to gene mutations, (ii) gene mutations associated to drugs, and (iii) drugs correlated to active signatures.
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