scholarly journals genomalicious: serving up a smorgasbord of R functions for population genomic analyses

2019 ◽  
Author(s):  
Joshua A. Thia ◽  
Cynthia Riginos
Keyword(s):  
Source Code ◽  
R Package ◽  
Allele Frequencies ◽  
Data Exploration ◽  
Programming Environments ◽  
Population Genomic ◽  
Snp Data ◽  
Genomic Analyses ◽  
Selection Of ◽  
R Functions

ABSTRACTTurning SNP data into biologically meaningful results requires considerable computational acrobatics, including importing, exporting, and manipulating data among different analytical packages and programming environments, and finding ways to visualise results for data exploration and presentation. We introduce genomalicious, an R package designed to provide a selection of functions for population genomicists to simply, intuitively, and flexibly, guide SNP data through their analytical pipelines, within and outside R. Moreover, researchers using pooled allele frequencies, or individually sequenced genotypes, are sure to find functions that accommodate their tastes in genomalicious. The source code for this package is freely available on GitHub.

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).

scholarly journals Proteus: an R package for downstream analysis of MaxQuant output

2018 ◽  
Author(s):  
Marek Gierlinski ◽  
Francesco Gastaldello ◽  
Chris Cole ◽  
Geoffrey J. Barton
Keyword(s):  
Mass Spectrometry ◽  
Differential Expression Analysis ◽  
Simulated Data ◽  
R Package ◽  
Data Exploration ◽  
Label Free ◽  
Interactive Analysis ◽  
Quality Checks ◽  
Downstream Analysis ◽  
Selection Of

AbstractProteus is a package for downstream analysis of MaxQuant evidence data in the R environment. It provides tools for peptide and protein aggregation, quality checks, data exploration and visualisation. Interactive analysis is implemented in the Shiny framework, where individual peptides or protein may be examined in the context of a volcano plot. Proteus performs differential expression analysis with the well-established tool limma, which offers robust treatment of missing data, frequently encountered in label-free mass-spectrometry experiments. We demonstrate on real and simulated data that limma results in improved sensitivity over random imputation combined with a t-test as implemented in the popular package Perseus. Embedding Proteus in R provides access to a wide selection of statistical and graphical tools for further analysis and reproducibility by scripting. Availability and implementation: The open-source R package, including example data and tutorials, is available to install from GitHub (https://github.com/bartongroup/proteus).

scholarly journals WORCS: A workflow for open reproducible code in science

Data Science ◽  
2021 ◽  
pp. 1-21
Author(s):  
Caspar J. Van Lissa ◽  
Andreas M. Brandmaier ◽  
Loek Brinkman ◽  
Anna-Lena Lamprecht ◽  
Aaron Peikert ◽  
...  
Keyword(s):  
Best Practices ◽  
Source Code ◽  
R Package ◽  
Open Science ◽  
Research Projects ◽  
Tabular Data ◽  
Step Procedure ◽  
Starting Point ◽  
Conducting Research ◽  
And Training

Adopting open science principles can be challenging, requiring conceptual education and training in the use of new tools. This paper introduces the Workflow for Open Reproducible Code in Science (WORCS): A step-by-step procedure that researchers can follow to make a research project open and reproducible. This workflow intends to lower the threshold for adoption of open science principles. It is based on established best practices, and can be used either in parallel to, or in absence of, top-down requirements by journals, institutions, and funding bodies. To facilitate widespread adoption, the WORCS principles have been implemented in the R package worcs, which offers an RStudio project template and utility functions for specific workflow steps. This paper introduces the conceptual workflow, discusses how it meets different standards for open science, and addresses the functionality provided by the R implementation, worcs. This paper is primarily targeted towards scholars conducting research projects in R, conducting research that involves academic prose, analysis code, and tabular data. However, the workflow is flexible enough to accommodate other scenarios, and offers a starting point for customized solutions. The source code for the R package and manuscript, and a list of examplesof WORCS projects, are available at https://github.com/cjvanlissa/worcs.

scholarly journals Open Plot Project: an open-source toolkit for 3-D structural data analysis

Solid Earth ◽  
2011 ◽  
Vol 2 (1) ◽  
pp. 53-63 ◽  
Author(s):  
S. Tavani ◽  
P. Arbues ◽  
M. Snidero ◽  
N. Carrera ◽  
J. A. Muñoz
Keyword(s):  
Spatial Distribution ◽  
Data Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Source Code ◽  
Structural Data ◽  
Geological Modelling ◽  
Analysis Tools ◽  
Transect Analysis ◽  
Selection Of

Abstract. In this work we present the Open Plot Project, an open-source software for structural data analysis, including a 3-D environment. The software includes many classical functionalities of structural data analysis tools, like stereoplot, contouring, tensorial regression, scatterplots, histograms and transect analysis. In addition, efficient filtering tools are present allowing the selection of data according to their attributes, including spatial distribution and orientation. This first alpha release represents a stand-alone toolkit for structural data analysis. The presence of a 3-D environment with digitalising tools allows the integration of structural data with information extracted from georeferenced images to produce structurally validated dip domains. This, coupled with many import/export facilities, allows easy incorporation of structural analyses in workflows for 3-D geological modelling. Accordingly, Open Plot Project also candidates as a structural add-on for 3-D geological modelling software. The software (for both Windows and Linux O.S.), the User Manual, a set of example movies (complementary to the User Manual), and the source code are provided as Supplement. We intend the publication of the source code to set the foundation for free, public software that, hopefully, the structural geologists' community will use, modify, and implement. The creation of additional public controls/tools is strongly encouraged.

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.

scholarly journals fullsibQTL: an R package for QTL mapping in biparental populations of outcrossing species

2020 ◽  
Author(s):  
Rodrigo Gazaffi ◽  
Rodrigo R. Amadeu ◽  
Marcelo Mollinari ◽  
João R. B. F. Rosa ◽  
Cristiane H. Taniguti ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Open Source ◽  
Qtl Analysis ◽  
Source Code ◽  
R Package ◽  
Genetic Maps ◽  
Linkage Phase ◽  
Position Effects ◽  
Genetic Features ◽  
Outcrossing Species

ABSTRACTAccurate QTL mapping in outcrossing species requires software programs which consider genetic features of these populations, such as markers with different segregation patterns and different level of information. Although the available mapping procedures to date allow inferring QTL position and effects, they are mostly not based on multilocus genetic maps. Having a QTL analysis based in such maps is crucial since they allow informative markers to propagate their information to less informative intervals of the map. We developed fullsibQTL, a novel and freely available R package to perform composite interval QTL mapping considering outcrossing populations and markers with different segregation patterns. It allows to estimate QTL position, effects, segregation patterns, and linkage phase with flanking markers. Additionally, several statistical and graphical tools are implemented, for straightforward analysis and interpretations. fullsibQTL is an R open source package with C and R source code (GPLv3). It is multiplatform and can be installed from https://github.com/augusto-garcia/fullsibQTL.

scholarly journals The Popgen Pipeline Platform: A Software Platform for Facilitating Population Genomic Analyses

2019 ◽  
Author(s):  
Andrew Webb ◽  
Jared Knoblauch ◽  
Nitesh Sabankar ◽  
Apeksha Sukesh Kallur ◽  
Jody Hey ◽  
...  
Keyword(s):  
Open Source ◽  
Development Time ◽  
End Users ◽  
File Format ◽  
Software Platform ◽  
Format Conversion ◽  
Link Type ◽  
Population Genomic ◽  
Genomic Analyses ◽  
File Format Conversion

AbstractHere we present the Pop-Gen Pipeline Platform (PPP), a software platform with the goal of reducing the computational expertise required for conducting population genomic analyses. The PPP was designed as a collection of scripts that facilitate common population genomic workflows in a consistent and standardized Python environment. Functions were developed to encompass entire workflows, including: input preparation, file format conversion, various population genomic analyses, output generation, and visualization. By facilitating entire workflows, the PPP offers several benefits to prospective end users - it reduces the need of redundant in-house software and scripts that would require development time and may be error-prone, or incorrect. The platform has also been developed with reproducibility and extensibility of analyses in mind. The PPP is an open-source package that is available for download and use at https://ppp.readthedocs.io/en/latest/PPP_pages/install.html

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