#GENODIVE WINDOWS INSTALL#
#GENODIVE WINDOWS SOFTWARE#
#GENODIVE WINDOWS MAC#

If (!require('ggplot2')) install.packages('ggplot2') Here the list of packages that assigner is depending on: if (!require('reshape2')) install.packages('reshape2') # Changing 'package_name' to the problematic package. If you know your way around the terminal and understand the consequences of using sudo rm -R command, here something faster to remove problematic packages: sudo rm -R /Library/Frameworks/R.framework/Resources/library/package_name

#GENODIVE WINDOWS MAC#

On MAC computers, in the Finder, use the shortcut cmd+shift+g, or in the menu bar : GO -> Go to Folder, copy/paste the text below: /Library/Frameworks/R.framework/Resources/library Delete the problematic packages manually and reinstall. Warning: cannot remove prior installation of package ‘stringi’ Sometimes you'll get warnings while installing dependencies required for assigner or other R packages. Sudo cp ~/Downloads/gsi_sim/gsi_sim-Linux /usr/local/bin/gsi_sim Sudo cp ~/Downloads/gsi_sim/gsi_sim-Darwin /usr/local/bin/gsi_sim Read this short section of my tutorial on ( ).īinary, the gsi_sim executable, in the folder /usr/local/bin. If you have no idea what i'm saying here, you might want to first Is properly installed and available on the command line, so it is executable fromĪny directory. Option 2: Use a pre-compiled binary (Mac OSX & Windows) ( ) or quick copy/paste solution below: # Mac OSXĪssigner assumes that the command line version of ( ) Install.packages(pkgs = '~/Downloads/randomForestSRC', repos = NULL, type = 'source') Option 1: From source (Linux & Mac OSX) # Terminal

#GENODIVE WINDOWS INSTALL#

Step 3 For faster imputations, you need to install an OpenMP enabled randomForestSRC package website. Step 2 Install assigner: install_github('thierrygosselin/assigner') # to install Step 1 You will need the package devtools if (!require('devtools')) install.packages('devtools') # to install You can try out the dev version of assigner.

Fast computations with optimized codes to run in parallel!.

ggplot2-based plotting to view assignment results and create publication-ready figures.

Import and summarise the assignment results from ( ) (Meirmans and Van Tienderen, 2004).

Compute the genotype likelihood ratio distance metric (Dlr) (Paetkau's et al.

#GENODIVE WINDOWS SOFTWARE#

The impact of filters used in other software can be explored by using the whitelist.markers argument.The impact of the minor allele frequency, MAF, (local and global) can also be easily explored with custom thresholds.Use thod and/or iteration.subsample arguments to resample markers or individuals to get statistics!.

Markers can be randomly selected for a classic LOO (Leave-One-Out) assignment orĬhosen based on ranked Fst (Weir & Cockerham, 1984) for a THL (Training, Holdout, Leave-one-out) assignment analysis (reviewed in Anderson 2010).in coverage, genotype likelihood or sequencing errors) can be erased prior to imputations or assignment analysis with the use of a blacklist.genotype argument. Map-independent imputation of missing genotype or alleles using Random Forest or the most frequent category is also available to test the impact of missing data on assignment analysis.Individuals, populations and markers can be filtered and/or selected in several ways using blacklist,.very large files (> 50 000 markers) can be imported in PLINK tped/tfam format (Purcell et al.an haplotypes data frame file ( batch_x.haplotypes.tsv) produced by ( ) (Catchen et al.2011) ( batch_x.vcf) produced by ( ) (Catchen et al. 2008 and Anderson 2010) or ( ), a R package developed by Thibaul Jombart, to conduct the assignment analysis. Conduct assignment analysis using ( ), a tool developedīy Eric C.This is the development page of the assigner package for the R software. Additionally, combining the use of tools like ( ) and ( ) will make effortless documenting your workflows and pipelines. The end results is usually poor data exploration, constrained by time, and poor reproducibility.Īssigner was tailored to make it easy to conduct assignment analysis using GBS/RAD data within R. This reality of GBS/RAD data is quite hard on GUI software traditionally used for assignment analysis. After hitting the bioinformatic wall with the different workflows you'll likely end up with several folders containing whitelist and blacklist of markers and individuals, data sets with various de novo and/or filtering parameters and missing data. Restriction-site-associated DNA sequencing (RADseq)) produce huge numbers of markers that hold great potential and promises for assignment analysis. Next-generation sequencing techniques that reduce the size of the genome (e.g. In eriqande/assigner: Assignment Analysis with GBS/RADseq Data using R assigner

Genodive windows