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Peer-reviewedCC BY 4.0Applications in Plant Sciences

PbAT: A user‐friendly R/Shiny platform for data‐driven decision support in crop improvement

  • Krishnan P. Abhijith1
  • K. K. Vinod2
  • Ranjith Kumar Ellur2
  • K. T. Ravikiran3,4
  • Rachit Kumar Saxena1
  • Vignesh Muthusamy2
  • S. Gopala Krishnan2
4 affiliations
  1. 1School of Crop Science, ICAR‐Indian Agricultural Research Institute Assam, Gogamukh Dhemaji 787035 Assam India
  2. 2Division of Genetics ICAR‐Indian Agricultural Research Institute 110012 New Delhi India
  3. 3ICAR‐Central Soil Salinity Research Institute Regional Research Station Lucknow 226002 Uttar Pradesh India
  4. 4Division of Crop Improvement ICAR‐Sugarcane Breeding Institute Coimbatore 641007 Tamil Nadu India
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Abstract

Premise: Modern plant breeding requires robust analysis of complex, multi‐environment datasets to identify superior genotypes. Although R offers powerful statistical packages, their use often requires programming expertise. Existing graphical user interface (GUI)‐based tools are either limited in scope or not openly accessible, leaving breeders without a comprehensive solution.

Methods and Results: We introduce PbAT (Plant breeding Analytical Tools), an open‐access Shiny application that unifies multiple R packages into a seamless, code‐free workflow. PbAT enables trial design, data curation, experimental design analysis, stability testing, multivariate methods, and mating design analysis. Outputs include variance components, best linear unbiased estimators (BLUEs), best linear unbiased predictors (BLUPs), ANOVA tables, and publication‐ready plots, accompanied by automated plain‐language interpretations and model equations. Its modular architecture supports future integration of additional tools. PbAT is available online as a web application (https://pbat.online) and as an R package (https://github.com/abhijithkpgen/PbAT).

Conclusions: By integrating powerful statistical methods with intuitive interfaces, PbAT streamlines plant breeding analytics, enhances reproducibility, and accelerates data‐driven decision‐making in crop improvement.

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Cite

BibTeXRIS
BibTeX
@article{abhijith2026pbat,
  title = {{PbAT: A user‐friendly R/Shiny platform for data‐driven decision support in crop improvement}},
  author = {Krishnan P. Abhijith and K. K. Vinod and Ranjith Kumar Ellur and K. T. Ravikiran and Rachit Kumar Saxena and Vignesh Muthusamy and S. Gopala Krishnan},
  journal = {Applications in Plant Sciences},
  year = {2026},
  month = jun,
  doi = {10.1002/aps3.70068},
  url = {https://doi.org/10.1002/aps3.70068},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - PbAT: A user‐friendly R/Shiny platform for data‐driven decision support in crop improvement
AU  - Krishnan P. Abhijith
AU  - K. K. Vinod
AU  - Ranjith Kumar Ellur
AU  - K. T. Ravikiran
AU  - Rachit Kumar Saxena
AU  - Vignesh Muthusamy
AU  - S. Gopala Krishnan
PY  - 2026
DA  - 2026/06/29
JO  - Applications in Plant Sciences
DO  - 10.1002/aps3.70068
UR  - https://doi.org/10.1002/aps3.70068
AB  - Premise: Modern plant breeding requires robust analysis of complex, multi‐environment datasets to identify superior genotypes. Although R offers powerful statistical packages, their use often requires programming expertise. Existing graphical user interface (GUI)‐based tools are either limited in scope or not openly accessible, leaving breeders without a comprehensive solution.  Methods and Results: We introduce PbAT (Plant breeding Analytical Tools), an open‐access Shiny application that unifies multiple R packages into a seamless, code‐free workflow. PbAT enables trial design, data curation, experimental design analysis, stability testing, multivariate methods, and mating design analysis. Outputs include variance components, best linear unbiased estimators (BLUEs), best linear unbiased predictors (BLUPs), ANOVA tables, and publication‐ready plots, accompanied by automated plain‐language interpretations and model equations. Its modular architecture supports future integration of additional tools. PbAT is available online as a web application (https://pbat.online) and as an R package (https://github.com/abhijithkpgen/PbAT).  Conclusions: By integrating powerful statistical methods with intuitive interfaces, PbAT streamlines plant breeding analytics, enhances reproducibility, and accelerates data‐driven decision‐making in crop improvement.
SN  - 2168-0450
ER  -