scholarly journals BioInstaller: a comprehensive R package to construct interactive and reproducible biological data analysis applications based on the R platform

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5853 ◽  
Author(s):  
Jianfeng Li ◽  
Bowen Cui ◽  
Yuting Dai ◽  
Ling Bai ◽  
Jinyan Huang
Keyword(s):  
Data Analysis ◽  
R Package ◽  
Biological Data ◽  
Representational State Transfer ◽  
State Transfer ◽  
Biological Data Analysis ◽  
Application Programming ◽  
Programming Interfaces ◽  
Shiny Application ◽  
R Functions

The increase in bioinformatics resources such as tools/scripts and databases poses a great challenge for users seeking to construct interactive and reproducible biological data analysis applications. Here, we propose an open-source, comprehensive, flexible R package named BioInstaller that consists of the R functions, Shiny application, the HTTP representational state transfer application programming interfaces, and a docker image. BioInstaller can be used to collect, manage and share various types of bioinformatics resources and perform interactive and reproducible data analyses based on the extendible Shiny application with Tom’s Obvious, Minimal Language and SQLite format databases. The source code of BioInstaller is freely available at our lab website, http://bioinfo.rjh.com.cn/labs/jhuang/tools/bioinstaller, the popular package host GitHub, https://github.com/JhuangLab/BioInstaller, and the Comprehensive R Archive Network, https://CRAN.R-project.org/package=BioInstaller. In addition, a docker image can be downloaded from DockerHub (https://hub.docker.com/r/bioinstaller/bioinstaller).

scholarly journals BioInstaller: a comprehensive R package to integrate bioinformatics resources

2018 ◽  
Author(s):  
Jianfeng Li ◽  
Bowen Cui ◽  
Yuting Dai ◽  
Ling Bai ◽  
Jinyan Huang
Keyword(s):  
Source Code ◽  
R Package ◽  
Community Based ◽  
Representational State Transfer ◽  
State Transfer ◽  
Application Programming ◽  
Representational State ◽  
Programming Interfaces ◽  
Shiny Application ◽  
R Functions

The number of bioinformatics resources, such as tools/scripts and databases are growing exponentially. This poses a great challenge for users to access, manage, and integrate the corresponding bioinformatics resources. To facilitate the request, we proposed a comprehensive R package, BioInstaller, which includes the R functions, Shiny application, and the HTTP representational state transfer (REST) application programming interfaces (APIs). We also established a community-based configuration pool to collect, access and share bioinformatics resources. The source code of BioInstaller is freely available at our lab website http://bioinfo.rjh.com.cn/labs/jhuang/tools/bioinstaller or popular package host GitHub at: https://github.com/JhuangLab/BioInstaller. Also, a docker image can be downloaded from DockerHub (https://hub.docker.com/r/bioinstaller).

scholarly journals BioInstaller: a comprehensive R package to integrate bioinformatics resources

2018 ◽  
Author(s):  
Jianfeng Li ◽  
Bowen Cui ◽  
Yuting Dai ◽  
Ling Bai ◽  
Jinyan Huang
Keyword(s):  
Source Code ◽  
R Package ◽  
Community Based ◽  
Representational State Transfer ◽  
State Transfer ◽  
Application Programming ◽  
Representational State ◽  
Programming Interfaces ◽  
Shiny Application ◽  
R Functions

The number of bioinformatics resources, such as tools/scripts and databases are growing exponentially. This poses a great challenge for users to access, manage, and integrate the corresponding bioinformatics resources. To facilitate the request, we proposed a comprehensive R package, BioInstaller, which includes the R functions, Shiny application, and the HTTP representational state transfer (REST) application programming interfaces (APIs). We also established a community-based configuration pool to collect, access and share bioinformatics resources. The source code of BioInstaller is freely available at our lab website http://bioinfo.rjh.com.cn/labs/jhuang/tools/bioinstaller or popular package host GitHub at: https://github.com/JhuangLab/BioInstaller. Also, a docker image can be downloaded from DockerHub (https://hub.docker.com/r/bioinstaller).

Case Studies Using Adobe™ Flex

2014 ◽  
pp. 210-226
Keyword(s):  
Web Services ◽  
Case Studies ◽  
Third Party ◽  
Internet Applications ◽  
Rich Internet Applications ◽  
Representational State Transfer ◽  
State Transfer ◽  
Application Programming ◽  
Restful Web Services ◽  
Programming Interfaces

REpresentational State Transfer (REST) is an architectural style that has recently emerged as a new approach to develop and deliver Web services. In fact, a great number of companies, such as eBay™, Twitter™, and Amazon.com™, have adopted REST to deliver Web services and Web feeds. This chapter offers a review of the support for consuming RESTful Web services in Adobe™ Flex™; then, it presents two case studies about the development of third-party RESTful Web services-based Rich Internet Applications (RIAs) using Adobe Flex™ along with HyperText Markup Languages (HTML) and JavaScript. The case studies presented in this chapter are intended to explain the common prerequisites for using RESTful Web services Application Programming Interfaces (APIs) as well as the particular implementation details, including the challenges and alternatives facing the capabilities and limitations of the target technologies. In addition, they exemplify the use of some User Interface (UI) patterns.

Practical R for biologists: an introduction

2021 ◽  
Keyword(s):  
Statistical Tests ◽  
Statistical Modelling ◽  
Biological Data ◽  
Early Years ◽  
Main Text ◽  
Biological Data Analysis ◽  
Base Functions ◽  
Almost All ◽  
Selection Of ◽  
R Functions

Abstract R is an open-source statistical environment modelled after the previously widely used commercial programs S and S-Plus, but in addition to powerful statistical analysis tools, it also provides powerful graphics outputs. In addition to its statistical and graphical capabilities, R is a programming language suitable for medium-sized projects. This book presents a set of studies that collectively represent almost all the R operations that beginners, analysing their own data up to perhaps the early years of doing a PhD, need. Although the chapters are organized around topics such as graphing, classical statistical tests, statistical modelling, mapping and text parsing, examples have been chosen based largely on real scientific studies at the appropriate level and within each the use of more R functions is nearly always covered than are simply necessary just to get a p-value or a graph. R comes with around a thousand base functions which are automatically installed when R is downloaded. This book covers the use of those of most relevance to biological data analysis, modelling and graphics. Throughout each chapter, the functions introduced and used in that chapter are summarized in Tool Boxes. The book also shows the user how to adapt and write their own code and functions. A selection of base functions relevant to graphics that are not necessarily covered in the main text are described in Appendix 1, and additional housekeeping functions in Appendix 2.

Graph Cutting in Image Processing Handling with Biological Data Analysis

2019 ◽  
pp. 203-216
Author(s):  
Mária Ždímalová ◽  
Tomáš Bohumel ◽  
Katarína Plachá-Gregorovská ◽  
Peter Weismann ◽  
Hisham El Falougy
Keyword(s):  
Image Processing ◽  
Data Analysis ◽  
Biological Data ◽  
Biological Data Analysis
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