Models tumor immune evasion by integrating expression signatures of T cell dysfunction and T cell exclusion. TIDE identifies factors that determine these two mechanisms of tumor immune escape. This tool integrates data from 189 human cancer studies including up to 33 000 samples. The generated signatures enable immune checkpoint blockade (ICB) clinical response based on pre-treatment tumor profiles.
Allows to calculate and mine drug-response data. GR Calculator permits to proceed analysis, and visualization of growth rate (GR) inhibition metrics. It can quantify how drug sensitivity changes in the face of variables that affect division rate. The tool provides an intuitive user interface that facilitates quick adoption of GR metrics. The website offers to users two examples to show capabilities of the tool.
An interpretation system to analyse hepatitis C virus (HCV) sequence data with respect to viral subtype and to predict drug resistance. g2p-HCV aligns the input sequences, identifies the genomic regions, predicts the HCV geno- and subtypes and generates for each direct-acting antiviral agent (DAA) a drug resistance prediction report. The web-service g2p-HCV was created to predict clinically relevant phenotypes based on viral sequence data.
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.
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.
Measures the genetic potential for evolutionary escape of the virus from the selective pressure of combination therapy. IGB can assist in identification of risk factors of therapeutic failure. The viral genotype can be represented by the IGB, which produces interpretable probability summarizing the predicted dynamics of viral evolutionary escape.
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.
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.
Provides a computational pipeline for HIV-1 genotyping. MinVar is a software able to detect Human immunodeficiency virus drug resistance mutations. It allows an easy work flow to go from the raw sequencing reads to a list of amino acid mutations annotated with their frequencies, combined with information from the Stanford HIV database to highlight those conferring resistance to antiviral drugs.
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.
Conducts simulations of models of bacteria, antibiotics, enzymes, and their interactions. ARSim leans on four models that constitute the processes of antibiotic resistance, bacteria-antibiotic interactions, enzymes, and the environment. It integrates horizontal and vertical transfer mechanisms of antibiotic resistance genes. This tool is useful for the discovery or the development of new antibiotics.
Assists in computing changes in nucleotide and amino acid sequences. BMA is an online application that stores all information related to the mutation analyses. This tool performs thanks to an analysis algorithm able to evaluate multiple patients, where each one can include multiple sequences. Moreover, the algorithm can analyze desired positions that the analyst can define.
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.
Produces drug resistance predictions given HIV-1 sequence data. sierra-local consists of a local implementation of the Stanford HIVdb genotypic resistance interpretation system. The main goal of this tool is to provide a lightweight alternative for transmitting HIV-1 sequences to the HIVdb web server with minimal software dependencies. It allows genotypic resistance predictions to be generated without the risk of potential network failures.
Simulates accumulation and spread of pathogens resistance among the population. VERA is an agent-based model that can be useful for the assessment of an epidemiological state both in some place (a city, town or village) or in various institutions (kindergarten). A number of statistics could be visualized in a report or displayed through the local web page.
Determines the antibody resistance for human immunodeficiency virus (HIV)-1. bNAb-ReP employs the gradient boosting machine (GBM) approach that consists of a white-box non-linear predictive modeling technique. It can be useful for the prediction of neutralization susceptibility of HIV-1 isolates for several independent clinical test sets. This tool serves for the evaluation of clinical settings.
Predicts antibiotic resistance using nanopore sequencing. RASE prediction pipeline uses rapid approximate k-mer-based matching of long sequencing reads against a database of genomes to predict resistance via lineage calling. The software iterates over reads from the nanopore sequencer and provides real-time predictions of phylogroup and resistance. It can serve for pathogen surveillance and diagnoses of resistant infections.
Uses a Support Vector Machine classifier to predict, with high accuracy, whether a compound is likely to be a substrate of the P-gp drug efflux pump. Moreover, The BioZyne P-gp server outputs the results of simple, interpretable decision tree models, as well as highlighting 'effluxophores', molecular fragments that enhance or abolish P-gp efflux.
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.
Guides the selection of drugs to treat people with resistant infections. The tool applies the unified sequence/structure encoding and the machine learning algorithms, k-nearest neighbor (KNN) and random forest (RF), for HIV genomic data. It handles genotype-phenotype datasets of HIV protease (PR) and reverse transcriptase (RT).
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