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Open-source analytical pipeline for robust data analysis, visualizations and sharing in crop breeding.

Waseem Hussain1, Mahender Anumalla2, Margaret Catolos2

  • 1Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), Los Banos, Laguna, Philippines. waseem.hussain@irri.org.

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This study introduces an open-source phenotypic data analysis pipeline for crop breeding programs. It enhances data interpretation and visualization for better breeding decisions, ensuring reproducible results.

Keywords:
Breeding analyticsInteractive visualizationsOpen-resourceReproducibilityRice

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Area of Science:

  • Agricultural Science
  • Computational Biology
  • Plant Breeding

Background:

  • Systematic phenotypic data analysis is vital for extracting insights and improving breeding decisions.
  • The Rainfed Rice Breeding (RRB) program at the International Rice Research Institute (IRRI) sought to enhance its data analysis capabilities.

Purpose of the Study:

  • To develop an open-source, end-to-end data analysis workflow and pipeline for phenotypic data.
  • To create a reproducible document integrating analysis, interpretation, and visualization for breeding programs.

Main Methods:

  • Leveraged R computational power and open-source tools (R Markdown, plotly, LaTeX, HTML).
  • Developed a pipeline for data pre-processing, quality checking, and robust statistical analysis.
  • Integrated explanatory text, R code, outputs, and interpretations into a unified, reproducible document.

Main Results:

  • Implemented a state-of-the-art phenotypic data analysis pipeline embedded in a descriptive document.
  • The pipeline provides step-by-step instructions for analyzing phenotypic data in crop breeding.
  • Analysis is highly reproducible and can be regenerated, with source codes and demo data available online.

Conclusions:

  • The presented analysis workflow and document are applicable beyond IRRI's RRB program to any full-fledged breeding program.
  • This initiative modernizes data analysis in plant breeding, offering guidance for analyzing breeding trial data effectively.
  • The pipeline is easily implementable by plant breeders and researchers for improved data analysis.