Skip to content

TeamMacLean/candisnp

 
 

Repository files navigation

candiSNP

candiSNP is now an R package that you can run on your own computer. You will need to install R and some packages to make it work. You will also need a Java installation but that should already be present on your machine.

Prerequisites

The SNPEff part of the process requires 2 Gb free memory in your machine. You will therefore need a machine with greater than that to run candiSNP. The process will not take more than 2 Gb, so at this time the larger (e.g Human) databases will not run.

Installation

Install R

The R statistical programming language can be installed from one of the CRAN mirrors listed here https://cran.r-project.org/mirrors.html. You will need version 4.1.0 or later.

Install the candiSNP package

Once you have installed R, use the R console to install the development version of candiSNP using the devtools package, install that first.

install.packages('devtools')

Then you can install candiSNP

devtools::install_github("TeamMacLean/candiSNP")

Installing genome annotations for SNPEff

candiSNP brings with it version 3.6 of SNPEff, but not any genome annotations. To install the default genomes used in the original web version of candiSNP you can use the install_default_genomes() function.

At the R console, type

library("candiSNP")
install_default_genomes()

This operation can take a few minutes, depending on your internet speed. You should only need to do it once per candiSNP install, though.

Starting the app

Once the genomes are installed, you can proceed to start candiSNP as follows.

app()

Note that the app starts in a browser window. The R console remains busy while the app is running. Use Ctrl-C or esc to quit the process and get your console back.

About

code for candiSNP web tool

Resources

License

Unknown, MIT licenses found

Licenses found

Unknown
LICENSE
MIT
LICENSE.md

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • R 43.0%
  • Perl 31.6%
  • Python 17.7%
  • Shell 7.7%