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Supplementary Python Jupyter and R/qtl Notebooks
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Part 2: Supplementary Python Jupyter and R/qtl Notebooks is the essential companion to the main volume QTL Mapping with Python and R/qtl: A Reproducible Pipeline for Crop Genetics. This collection provides fully functional, ready-to-run code notebooks designed to replicate and extend the quantitative trait loci (QTL) mapping pipeline demonstrated in the main text.
The supplement includes:
• A structured Python Jupyter Notebook for phenotype/genotype data preprocessing, quality control, and file export
• A modular R Markdown (.Rmd) script covering QTL analysis using R/qtl, including CIM, permutation tests, QTL effect plots, and chromosome-level visualization
• Custom code for cross-environment compatibility, rpy2 integration, and visualization-ready figure generation
Each notebook is annotated with comments and markdown cells to help guide students, researchers, and breeders through each analytical step. Together, these notebooks serve as a hands-on educational and research toolkit for extending QTL workflows to diverse crops and datasets.
Title: Supplementary Python Jupyter and R/qtl Notebooks
Description:
Part 2: Supplementary Python Jupyter and R/qtl Notebooks is the essential companion to the main volume QTL Mapping with Python and R/qtl: A Reproducible Pipeline for Crop Genetics.
This collection provides fully functional, ready-to-run code notebooks designed to replicate and extend the quantitative trait loci (QTL) mapping pipeline demonstrated in the main text.
The supplement includes:
• A structured Python Jupyter Notebook for phenotype/genotype data preprocessing, quality control, and file export
• A modular R Markdown (.
Rmd) script covering QTL analysis using R/qtl, including CIM, permutation tests, QTL effect plots, and chromosome-level visualization
• Custom code for cross-environment compatibility, rpy2 integration, and visualization-ready figure generation
Each notebook is annotated with comments and markdown cells to help guide students, researchers, and breeders through each analytical step.
Together, these notebooks serve as a hands-on educational and research toolkit for extending QTL workflows to diverse crops and datasets.
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