Correlated Trait Locus (CTL) mapping
This repository contains the source code for the Correlated Trait Locus (CTL) mapping algorithm. CTL mapping is a novel approach to detect genetic regulation of phenotypes in natural and experimental populations. It is a method which complements classical QTL analysis, providing additional insights overlooked by the classical QTL approach.
Learn more about the algorithm behind CTL mapping
Installing the R package from CRAN
The quickest and prefered way to start mapping CTLs, is to install the package directly from CRAN, start R and issue the following command to install the package:
After installation, load the package by:
Sometimes it is needed to install a develpoment version, since CRAN might take a while to update after a bug is fixed. To learn how to install a development version see: doc/DEVELOPMENT.md
CTL mapping uses the build in R framework to test the package for global regressions and unit-testing of documented functions. Tests can be executed from the commandline, by using the following command:
R CMD check Rctl # Run the unit-tests of the R package
A short online introduction is available and help files with examples are also available for almost all functions in R using:
library(ctl) # Load the library ?ctl # Show the general help for ctl ?CTLscan # Show the help for the CTLscan function
Issues can be raised through the github issue tracker.
Want to contribute? Great! We're actively looking for someone to do the website www.mapctl.org
Contribute to CTL source code by forking the Github repository, and sending us pull requests.
For a list of active developments tasks, see Rctl/inst/TODO.txt
Its also possible to just post comments on code / commits.
Or be a maintainer, and adopt a function
Citations are also available for import in bibtex format [TODO: bibtex with DOIs].
The CTL mapping source code is released under the GNU GENERAL PUBLIC LICENSE Version 3 (GPLv3). See LICENSE.txt.
This software was developed between 2012-2016 at the Groningen Bioinformatics Centre by Danny Arends, Yang Li, Pjotr Prins and Ritsert C. Jansen
Code managed by Dr. Danny Arends and the Groningen Bioinformatics Centre, Groningen, NLD.