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...
| Autores principales: | , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Springer
2022
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/127200 |
| _version_ | 1855519161141166080 |
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| author | Hussain, Waseem Anumalla, Mahender Catolos, Margaret Khanna, Apurva Sta. Cruz, Ma Teresa Ramos, Joie M. Bhosale, Sankalp |
| author_browse | Anumalla, Mahender Bhosale, Sankalp Catolos, Margaret Hussain, Waseem Khanna, Apurva Ramos, Joie M. Sta. Cruz, Ma Teresa |
| author_facet | Hussain, Waseem Anumalla, Mahender Catolos, Margaret Khanna, Apurva Sta. Cruz, Ma Teresa Ramos, Joie M. Bhosale, Sankalp |
| author_sort | Hussain, Waseem |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| 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 workfow 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 workfow 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 workfow 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. |
| format | Journal Article |
| id | CGSpace127200 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1272002025-11-12T04:56:09Z 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 M. Bhosale, Sankalp breeding crop improvement crop management analysis data analysis genetics biotechnology plant sciences 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 workfow 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 workfow 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 workfow 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. 2022-12 2023-01-16T13:06:26Z 2023-01-16T13:06:26Z Journal Article https://hdl.handle.net/10568/127200 en Open Access application/pdf Springer Hussain, W., Anumalla, M., Catolos, M., Khanna, A., Sta Cruz, M.T., Ramos, J. and Bhosale, S. 2022. Open-source analytical pipeline for robust data analysis, visualizations and sharing in crop breeding. Plant Methods 18, no. 14 (2022): 1-12. |
| spellingShingle | breeding crop improvement crop management analysis data analysis genetics biotechnology plant sciences Hussain, Waseem Anumalla, Mahender Catolos, Margaret Khanna, Apurva Sta. Cruz, Ma Teresa Ramos, Joie M. 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 | breeding crop improvement crop management analysis data analysis genetics biotechnology plant sciences |
| url | https://hdl.handle.net/10568/127200 |
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