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

BACKGROUND: Developing a systematic phenotypic data analysis pipeline, creating enhanced visualizations, and interpreting the results is crucial to extract meaningful insights from data in making better breeding decisions. Here, we provide an overview of how the Rainfed Rice Breeding (RRB) program a...

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Autores principales: Hussain, Waseem, Anumalla, Mahender, Catolos, Margaret, Khanna, Apurva, Sta. Cruz, Ma. Teresa, Ramos, Joie, Bhosale, Sankalp
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8817612/
https://www.ncbi.nlm.nih.gov/pubmed/35123539
http://dx.doi.org/10.1186/s13007-022-00845-7
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author Hussain, Waseem
Anumalla, Mahender
Catolos, Margaret
Khanna, Apurva
Sta. Cruz, Ma. Teresa
Ramos, Joie
Bhosale, Sankalp
author_facet Hussain, Waseem
Anumalla, Mahender
Catolos, Margaret
Khanna, Apurva
Sta. Cruz, Ma. Teresa
Ramos, Joie
Bhosale, Sankalp
author_sort Hussain, Waseem
collection PubMed
description BACKGROUND: Developing a systematic phenotypic data analysis pipeline, creating enhanced visualizations, and interpreting the results is crucial to extract meaningful insights from data in making better breeding decisions. Here, we provide an overview of how the Rainfed Rice Breeding (RRB) program at IRRI has leveraged R computational power with open-source resource tools like R Markdown, plotly, LaTeX, and HTML to develop an open-source and end-to-end data analysis workflow and pipeline, and re-designed it to a reproducible document for better interpretations, visualizations and easy sharing with collaborators. RESULTS: We reported the state-of-the-art implementation of the phenotypic data analysis pipeline and workflow embedded into a well-descriptive document. The developed analytical pipeline is open-source, demonstrating how to analyze the phenotypic data in crop breeding programs with step-by-step instructions. The analysis pipeline shows how to pre-process and check the quality of phenotypic data, perform robust data analysis using modern statistical tools and approaches, and convert it into a reproducible document. Explanatory text with R codes, outputs either in text, tables, or graphics, and interpretation of results are integrated into the unified document. The analysis is highly reproducible and can be regenerated at any time. The analytical pipeline source codes and demo data are available at https://github.com/whussain2/Analysis-pipeline. CONCLUSION: The analysis workflow and document presented are not limited to IRRI’s RRB program but are applicable to any organization or institute with full-fledged breeding programs. We believe this is a great initiative to modernize the data analysis of IRRI’s RRB program. Further, this pipeline can be easily implemented by plant breeders or researchers, helping and guiding them in analyzing the breeding trials data in the best possible way.
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spelling pubmed-88176122022-02-07 Open-source analytical pipeline for robust data analysis, visualizations and sharing in crop breeding Hussain, Waseem Anumalla, Mahender Catolos, Margaret Khanna, Apurva Sta. Cruz, Ma. Teresa Ramos, Joie Bhosale, Sankalp Plant Methods Commentary BACKGROUND: Developing a systematic phenotypic data analysis pipeline, creating enhanced visualizations, and interpreting the results is crucial to extract meaningful insights from data in making better breeding decisions. Here, we provide an overview of how the Rainfed Rice Breeding (RRB) program at IRRI has leveraged R computational power with open-source resource tools like R Markdown, plotly, LaTeX, and HTML to develop an open-source and end-to-end data analysis workflow and pipeline, and re-designed it to a reproducible document for better interpretations, visualizations and easy sharing with collaborators. RESULTS: We reported the state-of-the-art implementation of the phenotypic data analysis pipeline and workflow embedded into a well-descriptive document. The developed analytical pipeline is open-source, demonstrating how to analyze the phenotypic data in crop breeding programs with step-by-step instructions. The analysis pipeline shows how to pre-process and check the quality of phenotypic data, perform robust data analysis using modern statistical tools and approaches, and convert it into a reproducible document. Explanatory text with R codes, outputs either in text, tables, or graphics, and interpretation of results are integrated into the unified document. The analysis is highly reproducible and can be regenerated at any time. The analytical pipeline source codes and demo data are available at https://github.com/whussain2/Analysis-pipeline. CONCLUSION: The analysis workflow and document presented are not limited to IRRI’s RRB program but are applicable to any organization or institute with full-fledged breeding programs. We believe this is a great initiative to modernize the data analysis of IRRI’s RRB program. Further, this pipeline can be easily implemented by plant breeders or researchers, helping and guiding them in analyzing the breeding trials data in the best possible way. BioMed Central 2022-02-05 /pmc/articles/PMC8817612/ /pubmed/35123539 http://dx.doi.org/10.1186/s13007-022-00845-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Commentary
Hussain, Waseem
Anumalla, Mahender
Catolos, Margaret
Khanna, Apurva
Sta. Cruz, Ma. Teresa
Ramos, Joie
Bhosale, Sankalp
Open-source analytical pipeline for robust data analysis, visualizations and sharing in crop breeding
title Open-source analytical pipeline for robust data analysis, visualizations and sharing in crop breeding
title_full Open-source analytical pipeline for robust data analysis, visualizations and sharing in crop breeding
title_fullStr Open-source analytical pipeline for robust data analysis, visualizations and sharing in crop breeding
title_full_unstemmed Open-source analytical pipeline for robust data analysis, visualizations and sharing in crop breeding
title_short Open-source analytical pipeline for robust data analysis, visualizations and sharing in crop breeding
title_sort open-source analytical pipeline for robust data analysis, visualizations and sharing in crop breeding
topic Commentary
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8817612/
https://www.ncbi.nlm.nih.gov/pubmed/35123539
http://dx.doi.org/10.1186/s13007-022-00845-7
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