scholarly journals AB0210 ACREULAR: AN R PACKAGE FOR THE CALCULATION AND VISUALISATION OF ACR/EULAR RELATED RHEUMATOID ARTHRITIS MEASURES

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1405.1-1406
Author(s):  
F. Morton ◽  
J. Nijjar ◽  
C. Goodyear ◽  
D. Porter
Keyword(s):  
Rheumatoid Arthritis ◽  
Functional Status ◽  
Rheumatic Diseases ◽  
Web Application ◽  
R Package ◽  
Diagnostic Classification ◽  
Microsoft Excel ◽  
Link Type ◽  
Large Joint ◽  
Programming Skills

Background:The American College of Rheumatology (ACR) and the European League Against Rheumatism (EULAR) individually and collaboratively have produced/recommended diagnostic classification, response and functional status criteria for a range of different rheumatic diseases. While there are a number of different resources available for performing these calculations individually, currently there are no tools available that we are aware of to easily calculate these values for whole patient cohorts.Objectives:To develop a new software tool, which will enable both data analysts and also researchers and clinicians without programming skills to calculate ACR/EULAR related measures for a number of different rheumatic diseases.Methods:Criteria that had been developed by ACR and/or EULAR that had been approved for the diagnostic classification, measurement of treatment response and functional status in patients with rheumatoid arthritis were identified. Methods were created using the R programming language to allow the calculation of these criteria, which were incorporated into an R package. Additionally, an R/Shiny web application was developed to enable the calculations to be performed via a web browser using data presented as CSV or Microsoft Excel files.Results:acreular is a freely available, open source R package (downloadable fromhttps://github.com/fragla/acreular) that facilitates the calculation of ACR/EULAR related RA measures for whole patient cohorts. Measures, such as the ACR/EULAR (2010) RA classification criteria, can be determined using precalculated values for each component (small/large joint counts, duration in days, normal/abnormal acute-phase reactants, negative/low/high serology classification) or by providing “raw” data (small/large joint counts, onset/assessment dates, ESR/CRP and CCP/RF laboratory values). Other measures, including EULAR response and ACR20/50/70 response, can also be calculated by providing the required information. The accompanying web application is included as part of the R package but is also externally hosted athttps://fragla.shinyapps.io/shiny-acreular. This enables researchers and clinicians without any programming skills to easily calculate these measures by uploading either a Microsoft Excel or CSV file containing their data. Furthermore, the web application allows the incorporation of additional study covariates, enabling the automatic calculation of multigroup comparative statistics and the visualisation of the data through a number of different plots, both of which can be downloaded.Figure 1.The Data tab following the upload of data. Criteria are calculated by the selecting the appropriate checkbox.Figure 2.A density plot of DAS28 scores grouped by ACR/EULAR 2010 RA classification. Statistical analysis has been performed and shows a significant difference in DAS28 score between the two groups.Conclusion:The acreular R package facilitates the easy calculation of ACR/EULAR RA related disease measures for whole patient cohorts. Calculations can be performed either from within R or by using the accompanying web application, which also enables the graphical visualisation of data and the calculation of comparative statistics. We plan to further develop the package by adding additional RA related criteria and by adding ACR/EULAR related measures for other rheumatic disorders.Disclosure of Interests:Fraser Morton: None declared, Jagtar Nijjar Shareholder of: GlaxoSmithKline plc, Consultant of: Janssen Pharmaceuticals UK, Employee of: GlaxoSmithKline plc, Paid instructor for: Janssen Pharmaceuticals UK, Speakers bureau: Janssen Pharmaceuticals UK, AbbVie, Carl Goodyear: None declared, Duncan Porter: None declared

scholarly journals FRI0651-HPR A RANDOMIZED PROSPECTIVE OPEN-LABEL CONTROLLED TRIAL COMPARING THE PERFORMANCE OF A CONNECTED MONITORING INTERFACE IN PATIENTS WITH RHEUMATOID ARTHRITIS VERSUS PHYSICAL ROUTINE MONITORING

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 929.1-930
Author(s):  
Y. M. Pers ◽  
V. Valsecchi ◽  
T. Mura ◽  
S. Aouinti ◽  
N. Filippi ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Rheumatoid Arthritis ◽  
Disease Activity ◽  
Rheumatic Diseases ◽  
Tight Control ◽  
Link Type ◽  
Functional Scores ◽  
Monitoring Group ◽  
Disease Modifying

Background:Telemedicine has found wider application in chronic diseases for encouraging tight home-monitoring in order to improve patients’ outcome (Smolen et al. 2017).In previous studies, a high feasibility and high patient-satisfaction rate was found as well as the evidence for a superior or equal effectiveness of telemedicine compared to the standard face-to-face approach, however the results were weakened by some methodological biases and wide heterogeneity of interventions, thus preventing to draw definitive conclusions (Piga et al. 2017; Najm, Gossec, et al. 2019).Objectives:In rheumatoid arthritis (RA), telemedicine may allow a tight control of disease activity while reducing hospital visits. We developed a smartphone application connected with a physician’s interface to monitor RA patients. We aimed to assess the performance of this e-Health solution in comparison with routine practice in the management of patients with RA.Methods:A 6-month pragmatic, randomized, controlled, prospective, clinical trial was conducted in RA patients with high to moderate disease activity starting a new Disease Modifying Anti-Rheumatic Drug (DMARD) therapy. Two groups were established: “connected monitoring” and “conventional monitoring”. The primary outcome was the number of physical visits between baseline and 6 months. Secondary outcomes included adherence, satisfaction, changes in clinical, functional, and health status scores (SF-12).Results:Of the 94 randomized patients, 89 completed study: 44 in the “conventional monitoring” arm and 45 in the “connected monitoring” arm. The total number of physical visits between baseline and 6 month was significantly lower in the “connected monitoring” group (0.42 ± 0.58 versus 1.93 ± 0.55; p<0.05). No differences between groups were observed in the clinical and functional scores. A better quality of life for SF-12 subscores (Role-Physical, Social-Functioning and Role-Emotional) were found in the “connected monitoring” group.Conclusion:According to our results, a connected monitoring reduces the number of physical visits while maintaining a tight control of disease activity and improving quality of life in patients with RA starting a new treatment.References:[1] Najm, Aurelie, Laure Gossec, Catherine Weill, David Benoist, Francis Berenbaum, and Elena Nikiphorou. 2019. “Mobile Health Apps for Self-Management of Rheumatic and Musculoskeletal Diseases: Systematic Literature Review.”JMIR MHealth and UHealth7 (11): e14730.https://doi.org/10.2196/14730.[2] Piga, Matteo, Ignazio Cangemi, Alessandro Mathieu, and Alberto Cauli. 2017. “Telemedicine for Patients with Rheumatic Diseases: Systematic Review and Proposal for Research Agenda.”Seminars in Arthritis and Rheumatism47 (1): 121–28.https://doi.org/10.1016/j.semarthrit.2017.03.014.[3] Smolen, Josef S, Robert Landewe, Johannes Bijlsma, Gerd Burmester, Katerina Chatzidionysiou, Maxime Dougados, Jackie Nam, et al. 2017. “EULAR Recommendations for the Management of Rheumatoid Arthritis with Synthetic and Biological Disease-Modifying Antirheumatic Drugs: 2016 Update.”Annals of the Rheumatic Diseases76 (6): 960–77.https://doi.org/10.1136/annrheumdis-2016-210715.Disclosure of Interests:None declared

scholarly journals ORFhunteR: an accurate approach for the automatic identification and annotation of open reading frames in human mRNA molecules

2021 ◽  
Author(s):  
Vasily V. Grinev ◽  
Mikalai M. Yatskou ◽  
Victor V. Skakun ◽  
Maryna K. Chepeleva ◽  
Petr V. Nazarov
Keyword(s):  
Single Molecule ◽  
Web Application ◽  
R Package ◽  
Nucleotide Sequences ◽  
Open Reading Frames ◽  
Classification Model ◽  
Automatic Identification ◽  
Large Set ◽  
Link Type ◽  
Reading Frames

AbstractMotivationModern methods of whole transcriptome sequencing accurately recover nucleotide sequences of RNA molecules present in cells and allow for determining their quantitative abundances. The coding potential of such molecules can be estimated using open reading frames (ORF) finding algorithms, implemented in a number of software packages. However, these algorithms show somewhat limited accuracy, are intended for single-molecule analysis and do not allow selecting proper ORFs in the case of long mRNAs containing multiple ORF candidates.ResultsWe developed a computational approach, corresponding machine learning model and a package, dedicated to automatic identification of the ORFs in large sets of human mRNA molecules. It is based on vectorization of nucleotide sequences into features, followed by classification using a random forest. The predictive model was validated on sets of human mRNA molecules from the NCBI RefSeq and Ensembl databases and demonstrated almost 95% accuracy in detecting true ORFs. The developed methods and pre-trained classification model were implemented in a powerful ORFhunteR computational tool that performs an automatic identification of true ORFs among large set of human mRNA molecules.Availability and implementationThe developed open-source R package ORFhunteR is available for the community at GitHub repository (https://github.com/rfctbio-bsu/ORFhunteR), from Bioconductor (https://bioconductor.org/packages/devel/bioc/html/ORFhunteR.html) and as a web application (http://orfhunter.bsu.by).

scholarly journals Using the MR-Base platform to investigate risk factors and drug targets for thousands of phenotypes

2019 ◽  
Vol 4 ◽  
pp. 113 ◽  
Author(s):  
Venexia M Walker ◽  
Neil M Davies ◽  
Gibran Hemani ◽  
Jie Zheng ◽  
Philip C Haycock ◽  
...  
Keyword(s):  
Risk Factors ◽  
Web Application ◽  
Drug Targets ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
R Package ◽  
Sensitivity Analyses ◽  
Link Type ◽  
Study Results

Mendelian randomization (MR) estimates the causal effect of exposures on outcomes by exploiting genetic variation to address confounding and reverse causation. This method has a broad range of applications, including investigating risk factors and appraising potential targets for intervention. MR-Base has become established as a freely accessible, online platform, which combines a database of complete genome-wide association study results with an interface for performing Mendelian randomization and sensitivity analyses. This allows the user to explore millions of potentially causal associations. MR-Base is available as a web application or as an R package. The technical aspects of the tool have previously been documented in the literature. The present article is complementary to this as it focuses on the applied aspects. Specifically, we describe how MR-Base can be used in several ways, including to perform novel causal analyses, replicate results and enable transparency, amongst others. We also present three use cases, which demonstrate important applications of Mendelian randomization and highlight the benefits of using MR-Base for these types of analyses.

MetENP/MetENPWeb: An R package and web application for metabolomics enrichment and pathway analysis in Metabolomics Workbench

2020 ◽  
Author(s):  
Kumari Sonal Choudhary ◽  
Eoin Fahy ◽  
Kevin Coakley ◽  
Manish Sud ◽  
Mano R Maurya ◽  
...  
Keyword(s):  
Pathway Analysis ◽  
Web Application ◽  
Enrichment Analysis ◽  
R Package ◽  
Pathway Enrichment Analysis ◽  
Pathway Enrichment ◽  
Kegg Pathways ◽  
Link Type ◽  
Species Specific ◽  
User Friendly

ABSTRACTWith the advent of high throughput mass spectrometric methods, metabolomics has emerged as an essential area of research in biomedicine with the potential to provide deep biological insights into normal and diseased functions in physiology. However, to achieve the potential offered by metabolomics measures, there is a need for biologist-friendly integrative analysis tools that can transform data into mechanisms that relate to phenotypes. Here, we describe MetENP, an R package, and a user-friendly web application deployed at the Metabolomics Workbench site extending the metabolomics enrichment analysis to include species-specific pathway analysis, pathway enrichment scores, gene-enzyme information, and enzymatic activities of the significantly altered metabolites. MetENP provides a highly customizable workflow through various user-specified options and includes support for all metabolite species with available KEGG pathways. MetENPweb is a web application for calculating metabolite and pathway enrichment analysis.Availability and ImplementationThe MetENP package is freely available from Metabolomics Workbench GitHub: (https://github.com/metabolomicsworkbench/MetENP), the web application, is freely available at (https://www.metabolomicsworkbench.org/data/analyze.php)

scholarly journals SCOPIT: sample size calculations for single-cell sequencing experiments

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Alexander Davis ◽  
Ruli Gao ◽  
Nicholas E. Navin
Keyword(s):  
Single Cell ◽  
Web Application ◽  
Multinomial Distribution ◽  
R Package ◽  
Cell Type ◽  
Single Cell Sequencing ◽  
Link Type ◽  
Dna And Rna ◽  
Sample Size Calculations ◽  
Number Of Cells

Abstract Background In single cell DNA and RNA sequencing experiments, the number of cells to sequence must be decided before running an experiment, and afterwards, it is necessary to decide whether sufficient cells were sampled. These questions can be addressed by calculating the probability of sampling at least a defined number of cells from each subpopulation (cell type or cancer clone). Results We developed an interactive web application called SCOPIT (Single-Cell One-sided Probability Interactive Tool), which calculates the required probabilities using a multinomial distribution (www.navinlab.com/SCOPIT). In addition, we created an R package called pmultinom for scripting these calculations. Conclusions Our tool for fast multinomial calculations provide a simple and intuitive procedure for prospectively planning single-cell experiments or retrospectively evaluating if sufficient numbers of cells have been sequenced. The web application can be accessed at navinlab.com/SCOPIT.

scholarly journals hdnom: Building Nomograms for Penalized Cox Models with High-Dimensional Survival Data

2016 ◽  
Author(s):  
Nan Xiao ◽  
Qing-Song Xu ◽  
Miao-Zhu Li
Keyword(s):  
Survival Data ◽  
Web Application ◽  
Model Building ◽  
Cox Regression ◽  
Cox Model ◽  
R Package ◽  
High Dimensional ◽  
Cox Models ◽  
Link Type ◽  
Open Source Software Package

AbstractSummaryWe developed hdnom, an R package for survival modeling with high-dimensional data. The package is the first free and open-source software package that streamlines the workflow of penalized Cox model building, validation, calibration, comparison, and nomogram visualization, with nine types of penalized Cox regression methods fully supported. A web application and an online prediction tool maker are offered to enhance interac-tivity and flexibility in high-dimensional survival analysis.AvailabilityThe hdnom R package is available from CRAN:https://cran.r-project.org/package=hdnomunder GPL. The hdnom web application can be accessed athttp://hdnom.io. The web application maker is available fromhttp://hdnom.org/appmaker. The hdnom project website:http://[email protected]@duke.edu

scholarly journals Correlation AnalyzeR: functional predictions from gene co-expression correlations

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Henry E. Miller ◽  
Alexander J. R. Bishop
Keyword(s):  
Web Application ◽  
Bone Cancer ◽  
R Package ◽  
Limited Range ◽  
Web Interface ◽  
Generating Functional ◽  
Link Type ◽  
Genome Wide ◽  
User Friendly

Abstract Background Co-expression correlations provide the ability to predict gene functionality within specific biological contexts, such as different tissue and disease conditions. However, current gene co-expression databases generally do not consider biological context. In addition, these tools often implement a limited range of unsophisticated analysis approaches, diminishing their utility for exploring gene functionality and gene relationships. Furthermore, they typically do not provide the summary visualizations necessary to communicate these results, posing a significant barrier to their utilization by biologists without computational skills. Results We present Correlation AnalyzeR, a user-friendly web interface for exploring co-expression correlations and predicting gene functions, gene–gene relationships, and gene set topology. Correlation AnalyzeR provides flexible access to its database of tissue and disease-specific (cancer vs normal) genome-wide co-expression correlations, and it also implements a suite of sophisticated computational tools for generating functional predictions with user-friendly visualizations. In the usage example provided here, we explore the role of BRCA1-NRF2 interplay in the context of bone cancer, demonstrating how Correlation AnalyzeR can be effectively implemented to generate and support novel hypotheses. Conclusions Correlation AnalyzeR facilitates the exploration of poorly characterized genes and gene relationships to reveal novel biological insights. The database and all analysis methods can be accessed as a web application at https://gccri.bishop-lab.uthscsa.edu/correlation-analyzer/ and as a standalone R package at https://github.com/Bishop-Laboratory/correlationAnalyzeR.

scholarly journals miRBaseConverter: An R/Bioconductor Package for Converting and Retrieving miRNA Name, Accession, Sequence and Family Information in Different Versions of miRBase

2018 ◽  
Author(s):  
Taosheng Xu ◽  
Ning Su ◽  
Lin Liu ◽  
Junpeng Zhang ◽  
Hongqiang Wang ◽  
...  
Keyword(s):  
Web Application ◽  
Mature Mirnas ◽  
R Package ◽  
Mirna Sequence ◽  
Bioconductor Package ◽  
Related Data ◽  
Link Type ◽  
Comprehensive Information ◽  
Wide Range ◽  
Different Sources

AbstractBackgroundmiRBase is the primary repository for published miRNA sequence and annotation data, and serves as the “go-to” place for miRNA research. However, the definition and annotation of miRNAs have been changed significantly across different versions of miRBase. The changes cause inconsistency in miRNA related data between different databases and articles published at different times. Several tools have been developed for different purposes of querying and converting the information of miRNAs between different miRBase versions, but none of them individually can provide the comprehensive information about miRNAs in miRBase and users will need to use a number of different tools in their analyses.ResultsWe introducemiRBaseConverter,an R package integrating the latest miRBase version 22 available in Bioconductor to provide a suite of functions for converting and retrieving miRNA name (ID), accession, sequence, species, version and family information in different versions of miRBase. The package is implemented in R and available under the GPL-2 license from the Bioconductor website (http://bioconductor.org/packages/miRBaseConverter/). A Shiny-based GUI suitable for non-R users is also available as a standalone application from the package and also as a web application athttp://nugget.unisa.edu.au:3838/miRBaseConverter.miRBaseConverterhas a built-in database for querying miRNA information in all species and for both pre-mature and mature miRNAs defined by miRBase. In addition, it is the first tool for batch querying the miRNA family information. The package aims to provide a comprehensive and easy-to-use tool for miRNA research community where researchers often utilize published miRNA data from different sources.ConclusionsThe Bioconductor packagemiRBaseConverterand the Shiny-based web application are presented to provide a suite of functions for converting and retrieving miRNA name, accession, sequence, species, version and family information in different versions of miRBase. The package will serve a wide range of applications in miRNA research and could provide a full view of the miRNAs of interest.

scholarly journals TableOne: an online web application and R package for summarising and visualising data

2020 ◽  
Vol 23 (3) ◽  
pp. 127-130
Author(s):  
Agapios Panos ◽  
Dimitris Mavridis
Keyword(s):  
Clinical Trial ◽  
Comparative Effectiveness ◽  
Web Application ◽  
R Package ◽  
Descriptive Statistics ◽  
P Values ◽  
Treatment Groups ◽  
Link Type ◽  
Baseline Characteristics ◽  
Online Web

ObjectiveTo develop an easy-to-use R package and web application that summarises baseline characteristics across different arms of a clinical trial or different exposures.MethodsTables and figures are the efficient means of visualising, communicating and summarising data. It is common in comparative effectiveness research to provide a synopsis of characteristics and outcomes across the various treatment groups. The popularity of such a table has earned it a name and we simply call it the ‘TableOne’, as it is usually the first TableOne encounters looking at a published clinical trial. Such a table includes not only descriptive statistics for each group but also appropriate tests (p values and 95% CIs) for checking for differences across groups. We have developed an R package (called TableOne) (accessible through https://github.com/agapiospanos/TableOne) that quickly summarises and compares results across different groups. We have also extended it to an online web application that is easily handled by the researcher. All computations are done in R and plots are produced using the plotly library. We provide a detailed description on how to use the web application.ResultsThe application guides the user in a step by step format (wizard) and it is accessible through any browser in the following link (https://esm.uoi.gr/shiny/tableone/). Finally, appropriate interactive plots are provided for each variable.ConclusionsThis easy-to-use web application will help researchers quickly and easily to visualise differences across treatment groups or different exposures.

scholarly journals Using the MR-Base platform to investigate risk factors and drug targets for thousands of phenotypes

2019 ◽  
Vol 4 ◽  
pp. 113 ◽  
Author(s):  
Venexia M Walker ◽  
Neil M Davies ◽  
Gibran Hemani ◽  
Jie Zheng ◽  
Philip C Haycock ◽  
...  
Keyword(s):  
Risk Factors ◽  
Web Application ◽  
Drug Targets ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
R Package ◽  
Sensitivity Analyses ◽  
Link Type ◽  
Genome Wide ◽  
Study Results

Mendelian randomization (MR) uses genetic information to strengthen causal inference concerning the effect of exposures on outcomes. This method has a broad range of applications, including investigating risk factors and appraising potential targets for intervention. MR-Base has become established as a freely accessible, online platform, which combines a database of complete genome-wide association study results with an interface for performing Mendelian randomization and sensitivity analyses. This allows the user to explore millions of potentially causal associations. MR-Base is available as a web application or as an R package. The technical aspects of the tool have previously been documented in the literature. The present article is complimentary to this as it focuses on the applied aspects. Specifically, we describe how MR-Base can be used in several ways, including to perform novel causal analyses, replicate results and enable transparency, amongst others. We also present three use cases, which demonstrate important applications of Mendelian randomization and highlight the benefits of using MR-Base for these types of analyses.

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