CAAPP-QTL is a python package for connecting CAAPP data/tools with widely-used QTL tools.

Get started by following the installation instructions and copying-and-pasting the example code below.

CAAPP bitbucket account holders can access the latest source code here

Installation

  1. You will need a Python 3 code editor. (Anaconda with Python 3.6 is reccomended for new python users. It comes with both the python enterpreter and the jupyter notebook python editor.)
  2. Install the "caapp_qtl" python package from the command line
    pip install https://caapp-msu.bitbucket.io/projects/caapp-qtl/package.tar.gz --upgrade --no-cache-dir

Convert R/qtl results into heatmaps

Your R/qtl results should be contained in a .csv file (we use "input.csv" in the example below)

This example assumes that you have an existing folder called "output".

import caapp_qtl.rqtl as cqtl
cqtl.build_combined_heatmap( "input.csv", "output" )
cqtl.build_chromosome_heatmaps( "input.csv", "output" )

A spreadsheet will be saved in the output folder.

The spreadsheet can be dragged into OLIVER to be viewed as a heatmap.

Convert ICIMapping results into LOD heatmaps

Your ICIMapping results should be contained in a .ric file (we use "input.ric" in the example below)

This example assumes that you have an existing folder called "output".

import caapp_qtl.icim as cqtl
cqtl.build_combined_heatmap( "input.ric", "output", "LOD" )
cqtl.build_chromosome_heatmaps( "input.ric", "output", "LOD" )

Spreadsheets will be saved in the output folder. In these spreadsheets each row is a trait and each column is a position in a chromosome.

These spreadsheets can be dragged into OLIVER to be viewed as heatmaps.

Create "directed-LOD" heatmaps for RILs

Directed-LOD results are equivalent to LOD results, only some LOD values are negated to indicate which parent line was dominant.

Same as the example above, only instead of the "LOD" keyword, we use "DIRECTED_LOD"

import caapp_qtl.icim as cqtl
cqtl.build_combined_heatmap( "input.ric", "output", "DIRECTED_LOD" )
cqtl.build_chromosome_heatmaps( "input.ric", "output", "DIRECTED_LOD" )

Create PVE heatmaps

PVE results are percentages, represented using the range [0,1] in the resulting heatmaps.

Same as the example above, only instead of the "LOD" keyword, we use "PVE"

import caapp_qtl.icim as cqtl
cqtl.build_combined_heatmap( "input.ric", "output", "PVE" )
cqtl.build_chromosome_heatmaps( "input.ric", "output", "PVE" )

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