Mohamed Soudy1, Ali Mostafa Anwar1, Eman Ali Ahmed2, Aya Osama1, Shahd Ezzeldin1, Sebaey Mahgoub1, Sameh Magdeldin3. 1. Proteomics and metabolomics unit, Basic research department, Children's Cancer Hospital, 57357 Cairo, (CCHE-57357), Egypt. 2. Proteomics and metabolomics unit, Basic research department, Children's Cancer Hospital, 57357 Cairo, (CCHE-57357), Egypt; Department of Pharmacology, Faculty of Veterinary Medicine, Suez Canal University, 41522 Ismailia, Egypt. 3. Proteomics and metabolomics unit, Basic research department, Children's Cancer Hospital, 57357 Cairo, (CCHE-57357), Egypt; Department of Physiology, Faculty of Veterinary Medicine, Suez Canal University, 41522 Ismailia, Egypt. Electronic address: sameh.magdeldin@57357.org.
Abstract
UniprotR is a software package designed to easily retrieve, cluster and visualize protein data from UniProt knowledgebase (UniProtKB) using R language. The package is implemented mainly to process, parse and illustrate proteomics data in a handy and time-saving approach allowing researchers to summarize all required protein information available at UniProtKB in a readable data frame, Excel CSV file, and/or graphical output. UniprotR generates a set of graphics including gene ontology, chromosomal location, protein scoring and status, protein networking, sequence phylogenetic tree, and physicochemical properties. In addition, the package supports clustering of proteins based on primary gene name or chromosomal location, facilitating additional downstream analysis. SIGNIFICANCE: In this work, we implemented a robust package for retrieving and visualizing information from multiple sources such UniProtKB, SWISS-MODEL, and STRING. UniprotR Contains functions that enable retrieving and cluster data in a handy way and visualize data in publishable graphs to facilitate researcher's work and fulfill their needs. UniprotR will aid in saving time for downstream data analysis instead of manual time consuming data analysis. AVAILABILITY AND IMPLEMENTATION: UniprotR released as free open source code under the license of GPLv3, and available in CRAN (The Comprehensive R Archive Network) and GitHub. (https://cran.r-project.org/web/packages/UniprotR/index.html). (https://github.com/Proteomicslab57357/UniprotR).
UniprotR is a software package designed to easily retrieve, cluster and visualize protein data from UniProt knowledgebase (UniProtKB) using R language. The package is implemented mainly to process, parse and illustrate proteomics data in a handy and time-saving approach allowing researchers to summarize all required protein information available at UniProtKB in a readable data frame, Excel CSV file, and/or graphical output. UniprotR generates a set of graphics including gene ontology, chromosomal location, protein scoring and status, protein networking, sequence phylogenetic tree, and physicochemical properties. In addition, the package supports clustering of proteins based on primary gene name or chromosomal location, facilitating additional downstream analysis. SIGNIFICANCE: In this work, we implemented a robust package for retrieving and visualizing information from multiple sources such UniProtKB, SWISS-MODEL, and STRING. UniprotR Contains functions that enable retrieving and cluster data in a handy way and visualize data in publishable graphs to facilitate researcher's work and fulfill their needs. UniprotR will aid in saving time for downstream data analysis instead of manual time consuming data analysis. AVAILABILITY AND IMPLEMENTATION: UniprotR released as free open source code under the license of GPLv3, and available in CRAN (The Comprehensive R Archive Network) and GitHub. (https://cran.r-project.org/web/packages/UniprotR/index.html). (https://github.com/Proteomicslab57357/UniprotR).
Authors: Ye Hong; Dani Flinkman; Tomi Suomi; Sami Pietilä; Peter James; Eleanor Coffey; Laura L Elo Journal: Brief Bioinform Date: 2022-01-17 Impact factor: 11.622
Authors: Mourad Zerfaoui; Koji Tsumagari; Eman Toraih; Youssef Errami; Emmanuelle Ruiz; Mohammed Sohail M Elaasar; Moroz Krzysztof; Andrew B Sholl; Sameh Magdeldin; Mohamed Soudy; Zakaria Y Abd Elmageed; A Hamid Boulares; Emad Kandil Journal: Am J Cancer Res Date: 2022-07-15 Impact factor: 5.942
Authors: Mathew J Baldwin; Jolet Y Mimpen; Adam P Cribbs; Edward Stace; Martin Philpott; Stephanie G Dakin; Andrew J Carr; Sarah Jb Snelling Journal: Front Bioeng Biotechnol Date: 2022-01-12
Authors: Dagmar Waltemath; Martin Golebiewski; Michael L Blinov; Padraig Gleeson; Henning Hermjakob; Michael Hucka; Esther Thea Inau; Sarah M Keating; Matthias König; Olga Krebs; Rahuman S Malik-Sheriff; David Nickerson; Ernst Oberortner; Herbert M Sauro; Falk Schreiber; Lucian Smith; Melanie I Stefan; Ulrike Wittig; Chris J Myers Journal: J Integr Bioinform Date: 2020-06-29