Release 0.99.12 Please note that new Bioconductor packages start with version numbers smaller than 1.0.0. All rgoslin versions in Bioconductor are based on the version 2.0 reimplementation of the goslin grammars and parsers. What's Changed Fixed #12 - updated cppgoslin to latest version, added tests. Addressed Bioconductor review issues. Full Changelog: https://github.com/lifs-tools/rgoslin/compare/v0.99.2...v0.99.12 rgoslin - R implementation for parsing of lipid shorthand nomenclature names, version 2.0 This project is a parser, validator and normalizer implementation for shorthand lipid nomenclatures, based on the Grammar of Succinct Lipid Nomenclatures project. https://github.com/lifs-tools/goslin defines multiple grammars for different sources of shorthand lipid nomenclature. This allows to generate parsers based on the defined grammars, which provide immediate feedback whether a processed lipid shorthand notation string is compliant with a particular grammar, or not. NOTE: Please report any issues you might find to help improve it! Here, rgoslin 2.0 uses the Goslin grammars and the cppgoslin parser to support the following general tasks: Facilitate the parsing of shorthand lipid names dialects. Provide a structural representation of the shorthand lipid after parsing. Use the structural representation to generate normalized names, following the latest shorthand nomenclature. Related Projects This project Goslin grammars and reference test files C++ implementation C# implementation Java implementation Python implementation Webapplication and REST API Changes in Version 2.0 The column names within the data frames returned from the parse* methods now use column names with dots instead of spaces. This makes it easier to use the column names unquoted within other R expressions. All parse* methods now return data frames. The Messages column has been added to capture parser messages. If parsing succeeds, this will contain NA and Normalized.Name will contain the normalized lipid shorthand name. Parser implementations have been updated to reflect the latest lipid shorthand nomenclature changes. Please see the Goslin repository for more details. Exceptions in the C++ part of the library are captured as warnings in R. However, if you parse multiple lipid names, exceptions will not stop the parsing process. Installation rgoslin is available as a Bioconductor package. You can install it as follows: if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("rgoslin") Installing RTools NOTE: The following instructions are only necessary if you want to compile the package from source. Regular installation via Bioconductor does not require the installation of RTools. rgoslin uses C++ source code of cppgoslin as its backend. This requires compilation during installation. If you do not have RTools installed already, please follow the instructions for your operating system: Windows: https://cran.r-project.org/bin/windows/Rtools/ Mac OSX: https://mac.r-project.org/tools/ Linux: Install packages r-devel or r-base-dev, depending on your Linux distribution. Usage To load the package, start an R session and type library(rgoslin) Type the following to see the package vignette / tutorial: vignette('introduction', package = 'rgoslin') Getting help & support If you find any issues with the library, would like to have other functionality included, require help or would like to contribute, please contact us via our GitHub Project or via the LIFS support page. Instructions for development Installation From GitHub (for development versions) Install the devtools package with the following command. if(!require(devtools)) { install.packages("devtools") } Adjusting Makevars for more performance In order to apply platform-specific optimizations, you can edit your user Makevars file. This file is in ~/.R/Makevars, where ~ is your user directory. If it does not exist, you may need to create the directory and the file. To apply optimizations, put the following lines into your Makevars file. CFLAGS = -O3 -Wall -mtune=native -march=native CXXFLAGS = -O3 -Wall -mtune=native -march=native CXX1XFLAGS = -O3 -Wall -mtune=native -march=native CXX11FLAGS = -O3 -Wall -mtune=native -march=native Depending on the number of available cores, you can speed up compilation by redefining MAKE in Makevars (here for 4 CPU cores): MAKE = make -j4 Please note that these settings will apply to all R packages that require compilation from this point on! Also, -O3 may have detrimental influence on some code. You can also replace it with R's default -O2. Installing rgoslin development versions from GitHub Run install_github("lifs-tools/rgoslin") to install from the github repository. This will install the latest, potentially unstable development version of the package with all required dependencies into your local R installation. If you want to use a proper release version, referenced by a Git tag (here: v2.0.0) install the package as follows: install_github("lifs-tools/rgoslin", ref="v2.0.0") If you want to work off of a specific branch (here: adding_masses), install the package as follows: install_github("lifs-tools/rgoslin", ref="adding_masses") If you also want to build the help and vignette, add the following arguments: install_github("lifs-tools/rgoslin", ref="adding_masses", build_manual = TRUE, build_vignettes = TRUE) If you have cloned the code locally, use devtools as follows. Make sure you set the working directory to where the API code is located. Then execute library(devtools) install(".") To run the tests, execute ```R library(devtools) test()