KRSA: An R package and R Shiny web application for an end-to-end upstream kinase analysis of kinome array data

Phosphorylation by serine-threonine and tyrosine kinases is critical for determining protein function. Array-based platforms for measuring reporter peptide signal levels allow for differential phosphorylation analysis between conditions for distinct active kinases. Peptide array technologies like the PamStation12 from PamGene allow for generating high-throughput, multi-dimensional, and complex functional proteomics data. As the adoption rate of such technologies increases, there is an imperative need for software tools that streamline the process of analyzing such data. We present Kinome Random Sampling Analyzer (KRSA), an R package and R Shiny web-application for analyzing kinome array data to help users better understand the patterns of functional proteomics in complex biological systems. KRSA is an All-In-One tool that reads, formats, fits models, analyzes, and visualizes PamStation12 kinome data. While the underlying algorithm has been experimentally validated in previous publications, we demonstrate KRSA workflow on dorsolateral prefrontal cortex (DLPFC) in male (n = 3) and female (n = 3) subjects to identify differential phosphorylation signatures and upstream kinase activity. Kinase activity differences between males and females were compared to a previously published kinome dataset (11 female and 7 male subjects) which showed similar global phosphorylation signals patterns.

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KRSA: Network-based Prediction of Differential Kinase Activity from Kinome Array Data

10.1101/2020.08.26.268581 ◽

2020 ◽

Author(s):

Erica A. K. DePasquale ◽

Khaled Alganem ◽

Eduard Bentea ◽

Nawshaba Nawreen ◽

Jennifer L. McGuire ◽

...

Keyword(s):

Gene Expression ◽

Protein Function ◽

Web Application ◽

Kinase Activity ◽

Supplementary Information ◽

Array Data ◽

Network Analyses ◽

Link Type ◽

Upstream Kinase ◽

Differential Phosphorylation

AbstractMotivationPhosphorylation by serine-threonine and tyrosine kinases is critical for determining protein function. Array-based approaches for measuring multiple kinases allow for the testing of differential phosphorylation between conditions for distinct sub-kinomes. While bioinformatics tools exist for processing and analyzing such kinome array data, current open-source tools lack the automated approach of upstream kinase prediction and network modeling. The presented tool, alongside other tools and methods designed for gene expression and protein-protein interaction network analyses, help the user better understand the complex regulation of gene and protein activities that forms biological systems and cellular signaling networks.ResultsWe present the Kinome Random Sampling Analyzer (KRSA), a web-application for kinome array analysis. While the underlying algorithm has been experimentally validated in previous publications, we tested the full KRSA application on dorsolateral prefrontal cortex (DLPFC) in male (n=3) and female (n=3) subjects to identify differential phosphorylation and upstream kinase activity. Kinase activity differences between males and females were compared to a previously published kinome dataset (11 female and 7 male subjects) which showed similar patterns to the global phosphorylation signal. Additionally, kinase hits were compared to gene expression databases for in silico validation at the transcript level and showed differential gene expression of kinases.Availability and implementationKRSA as a web-based application can be found at http://bpg-n.utoledo.edu:3838/CDRL/KRSA/. The code and data are available at https://github.com/kalganem/KRSA.Supplementary informationSupplementary data are available online.

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Netpredictor: R and Shiny package to perform Drug-Target Bipartite network analysis and prediction of missing links

10.1101/080036 ◽

2016 ◽

Author(s):

Abhik Seal ◽

David J. Wild

Keyword(s):

Random Walk ◽

Web Application ◽

Drug Target ◽

Network Inference ◽

R Package ◽

Bipartite Network ◽

Inference Algorithm ◽

Random Walk With Restart ◽

R Shiny ◽

Network Properties

AbstractNetpredictor is an R package for prediction of missing links in any given bipartite network. The package provides utilities to compute missing links in a bipartite and well as unipartite networks using Random Walk with Restart and Network inference algorithm. The package also allows computation of Bipartite network properties, visualization of communities for two different sets of nodes, and calculation of significant interactions between two sets of nodes using permutation based testing. The R standalone package (including detailed introductory vignettes) and associated R Shiny web application is available under the GPL-2 Open Source license and is freely available to download from github Netpredictor repository and Shiny Netpredictor repository respectively.

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

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2020-eular.2326 ◽

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

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A New Analysis Tool for Continuous Glucose Monitor Data

Journal of Diabetes Science and Technology ◽

10.1177/19322968211028909 ◽

2021 ◽

pp. 193229682110289

Author(s):

Evan Olawsky ◽

Yuan Zhang ◽

Lynn E Eberly ◽

Erika S Helgeson ◽

Lisa S Chow

Keyword(s):

Glucose Monitoring ◽

Glycemic Variability ◽

R Package ◽

Analysis Tool ◽

Continuous Glucose Monitor ◽

Analysis Tools ◽

R Shiny ◽

Primary Driver ◽

Rich Information ◽

Web App

Background: With the development of continuous glucose monitoring systems (CGMS), detailed glycemic data are now available for analysis. Yet analysis of this data-rich information can be formidable. The power of CGMS-derived data lies in its characterization of glycemic variability. In contrast, many standard glycemic measures like hemoglobin A1c (HbA1c) and self-monitored blood glucose inadequately describe glycemic variability and run the risk of bias toward overreporting hyperglycemia. Methods that adjust for this bias are often overlooked in clinical research due to difficulty of computation and lack of accessible analysis tools. Methods: In response, we have developed a new R package rGV, which calculates a suite of 16 glycemic variability metrics when provided a single individual’s CGM data. rGV is versatile and robust; it is capable of handling data of many formats from many sensor types. We also created a companion R Shiny web app that provides these glycemic variability analysis tools without prior knowledge of R coding. We analyzed the statistical reliability of all the glycemic variability metrics included in rGV and illustrate the clinical utility of rGV by analyzing CGM data from three studies. Results: In subjects without diabetes, greater glycemic variability was associated with higher HbA1c values. In patients with type 2 diabetes mellitus (T2DM), we found that high glucose is the primary driver of glycemic variability. In patients with type 1 diabetes (T1DM), we found that naltrexone use may potentially reduce glycemic variability. Conclusions: We present a new R package and accompanying web app to facilitate quick and easy computation of a suite of glycemic variability metrics.

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An open-source R-package and web application for high-quality probabilistic predictions in hydrology

10.5194/egusphere-egu21-8549 ◽

2021 ◽

Author(s):

Jason Hunter ◽

Mark Thyer ◽

Dmitri Kavetski ◽

David McInerney

Keyword(s):

Open Source ◽

Web Application ◽

R Package ◽

Error Model ◽

Objective Functions ◽

High Quality ◽

Wide Range ◽

Probabilistic Error

Probabilistic predictions provide crucial information regarding the uncertainty of hydrological predictions, which are a key input for risk-based decision-making. However, they are often excluded from hydrological modelling applications because suitable probabilistic error models can be both challenging to construct and interpret, and the quality of results are often reliant on the objective function used to calibrate the hydrological model.We present an open-source R-package and an online web application that achieves the following two aims. Firstly, these resources are easy-to-use and accessible, so that users need not have specialised knowledge in probabilistic modelling to apply them. Secondly, the probabilistic error model that we describe provides high-quality probabilistic predictions for a wide range of commonly-used hydrological objective functions, which it is only able to do by including a new innovation that resolves a long-standing issue relating to model assumptions that previously prevented this broad application. &#160;We demonstrate our methods by comparing our new probabilistic error model with an existing reference error model in an empirical case study that uses 54 perennial Australian catchments, the hydrological model GR4J, 8 common objective functions and 4 performance metrics (reliability, precision, volumetric bias and errors in the flow duration curve). The existing reference error model introduces additional flow dependencies into the residual error structure when it is used with most of the study objective functions, which in turn leads to poor-quality probabilistic predictions. In contrast, the new probabilistic error model achieves high-quality probabilistic predictions for all objective functions used in this case study.The new probabilistic error model and the open-source software and web application aims to facilitate the adoption of probabilistic predictions in the hydrological modelling community, and to improve the quality of predictions and decisions that are made using those predictions. In particular, our methods can be used to achieve high-quality probabilistic predictions from hydrological models that are calibrated with a wide range of common objective functions.

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MALDIViz: A Comprehensive Informatics Tool for MALDI-MS Data Visualization and Analysis

SLAS DISCOVERY Advancing Life Sciences ◽

10.1177/2472555217727517 ◽

2017 ◽

Vol 22 (10) ◽

pp. 1246-1252 ◽

Cited By ~ 1

Author(s):

Kishore Kumar Jagadeesan ◽

Simon Ekström

Keyword(s):

Mass Spectrometry ◽

High Throughput ◽

High Throughput Screening ◽

Web Application ◽

Low Cost ◽

Maldi Ms ◽

Ionization Mass ◽

Label Free ◽

Label Free Detection ◽

R Shiny

Recently, mass spectrometry (MS) has emerged as an important tool for high-throughput screening (HTS) providing a direct and label-free detection method, complementing traditional fluorescent and colorimetric methodologies. Among the various MS techniques used for HTS, matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) provides many of the characteristics required for high-throughput analyses, such as low cost, speed, and automation. However, visualization and analysis of the large datasets generated by HTS MALDI-MS can pose significant challenges, especially for multiparametric experiments. The datasets can be generated fast, and the complexity of the experimental data (e.g., screening many different sorbent phases, the sorbent mass, and the load, wash, and elution conditions) makes manual data analysis difficult. To address these challenges, a comprehensive informatics tool called MALDIViz was developed. This tool is an R-Shiny-based web application, accessible independently of the operating system and without the need to install any program locally. It has been designed to facilitate easy analysis and visualization of MALDI-MS datasets, comparison of multiplex experiments, and export of the analysis results to high-quality images.

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Quickomics: exploring omics data in an intuitive, interactive and informative manner

10.1101/2021.01.19.427296 ◽

2021 ◽

Author(s):

Benbo Gao ◽

Jing Zhu ◽

Soumya Negi ◽

Xinmin Zhang ◽

Stefka Gyoneva ◽

...

Keyword(s):

Modular Design ◽

Functional Module ◽

Supplementary Information ◽

Data Sets ◽

Omics Data ◽

Proteomics Data ◽

Primary Analysis ◽

Link Type ◽

R Shiny ◽

Advanced Analysis

AbstractSummaryWe developed Quickomics, a feature-rich R Shiny-powered tool to enable biologists to fully explore complex omics data and perform advanced analysis in an easy-to-use interactive interface. It covers a broad range of secondary and tertiary analytical tasks after primary analysis of omics data is completed. Each functional module is equipped with customized configurations and generates both interactive and publication-ready high-resolution plots to uncover biological insights from data. The modular design makes the tool extensible with ease.AvailabilityResearchers can experience the functionalities with their own data or demo RNA-Seq and proteomics data sets by using the app hosted at http://quickomics.bxgenomics.com and following the tutorial, https://bit.ly/3rXIyhL. The source code under GPLv3 license is provided at https://github.com/interactivereport/[email protected], [email protected] informationSupplementary materials are available at https://bit.ly/37HP17g.

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A R-SHINY BASED PHENOLOGY ANALYSIS SYSTEM AND CASE STUDY USING DIGITAL CAMERA DATASET

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-2571-2018 ◽

2018 ◽

Vol XLII-3 ◽

pp. 2571-2574

Author(s):

Y. K. Zhou

Keyword(s):

Data Processing ◽

Web Application ◽

Vegetation Index ◽

Digital Camera ◽

Parameter Extraction ◽

Growth Trajectory ◽

Phenological Data ◽

Combination Methods ◽

Parameters Extraction ◽

R Shiny

Accurate extracting of the vegetation phenology information play an important role in exploring the effects of climate changes on vegetation. Repeated photos from digital camera is a useful and huge data source in phonological analysis. Data processing and mining on phenological data is still a big challenge. There is no single tool or a universal solution for big data processing and visualization in the field of phenology extraction. In this paper, we proposed a R-shiny based web application for vegetation phenological parameters extraction and analysis. Its main functions include phenological site distribution visualization, ROI (Region of Interest) selection, vegetation index calculation and visualization, data filtering, growth trajectory fitting, phenology parameters extraction, etc. the long-term observation photography data from Freemanwood site in 2013 is processed by this system as an example. The results show that: (1) this system is capable of analyzing large data using a distributed framework; (2) The combination of multiple parameter extraction and growth curve fitting methods could effectively extract the key phenology parameters. Moreover, there are discrepancies between different combination methods in unique study areas. Vegetation with single-growth peak is suitable for using the double logistic module to fit the growth trajectory, while vegetation with multi-growth peaks should better use spline method.

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

10.1101/2021.02.05.429963 ◽

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).

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

Wellcome Open Research ◽

10.12688/wellcomeopenres.15334.2 ◽

2019 ◽

Vol 4 ◽

pp. 113 ◽

Cited By ~ 12

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.

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