From 8d3d464230ecd74ae09dff48119b924261bad643 Mon Sep 17 00:00:00 2001 From: Peter Selby Date: Thu, 1 Aug 2024 16:50:11 +0000 Subject: [PATCH] minor updates and G-Crunch re-org [ci skip] This build is based on https://github.com/plantbreeding/BrAPI-Manuscript2/commit/cd105a0d6c9ddd8cefe2cf6e257d7329e9d9e59d. This commit was created by the following CI build and job: https://github.com/plantbreeding/BrAPI-Manuscript2/commit/cd105a0d6c9ddd8cefe2cf6e257d7329e9d9e59d/checks https://github.com/plantbreeding/BrAPI-Manuscript2/actions/runs/10202371722 --- README.md | 4 +- index.html | 228 +- manuscript.pdf | Bin 2312409 -> 2315281 bytes .../images/AGENT_Genotyping_Data_Flow.png | Bin 0 -> 177493 bytes .../images/AGENT_WebFrontend.pptx | Bin 0 -> 1447179 bytes .../images/AGENT_Web_Frontend.png | Bin 0 -> 268377 bytes .../images/BrAPI_Application_Chart.pdf | Bin 0 -> 41569 bytes .../images/BrAPI_Application_Chart.xlsx | Bin 0 -> 13562 bytes .../images/BrAPI_Domains_v2-1_vertical.png | Bin 0 -> 495793 bytes .../images/BrAPI_Paper_Applications_Chart.png | Bin 0 -> 2465428 bytes .../images/BrAPI_org_structure.jpg | Bin 0 -> 41681 bytes .../images/Schema_FAIDARE.png | Bin 0 -> 142775 bytes .../images/Schema_Florilege.jpg | Bin 0 -> 72787 bytes .../images/github.svg | 4 + .../images/green_check.png | Bin 0 -> 1043 bytes .../images/mastodon.svg | 4 + .../images/orcid.svg | 4 + .../images/twitter.svg | 4 + .../index.html | 4849 +++++++++++++++++ .../index.html.ots | Bin 0 -> 538 bytes .../manuscript.pdf | Bin 0 -> 2315281 bytes .../manuscript.pdf.ots | Bin 0 -> 538 bytes .../index.html.ots | Bin 608 -> 4964 bytes .../manuscript.pdf.ots | Bin 678 -> 5034 bytes v/freeze/index.html | 6 +- v/latest/index.html | 228 +- v/latest/index.html.ots | Bin 608 -> 538 bytes v/latest/manuscript.pdf | Bin 2312409 -> 2315281 bytes v/latest/manuscript.pdf.ots | Bin 678 -> 538 bytes 29 files changed, 5112 insertions(+), 219 deletions(-) create mode 100644 v/cd105a0d6c9ddd8cefe2cf6e257d7329e9d9e59d/images/AGENT_Genotyping_Data_Flow.png create mode 100644 v/cd105a0d6c9ddd8cefe2cf6e257d7329e9d9e59d/images/AGENT_WebFrontend.pptx create mode 100644 v/cd105a0d6c9ddd8cefe2cf6e257d7329e9d9e59d/images/AGENT_Web_Frontend.png create mode 100644 v/cd105a0d6c9ddd8cefe2cf6e257d7329e9d9e59d/images/BrAPI_Application_Chart.pdf create mode 100644 v/cd105a0d6c9ddd8cefe2cf6e257d7329e9d9e59d/images/BrAPI_Application_Chart.xlsx create mode 100644 v/cd105a0d6c9ddd8cefe2cf6e257d7329e9d9e59d/images/BrAPI_Domains_v2-1_vertical.png create mode 100644 v/cd105a0d6c9ddd8cefe2cf6e257d7329e9d9e59d/images/BrAPI_Paper_Applications_Chart.png create mode 100644 v/cd105a0d6c9ddd8cefe2cf6e257d7329e9d9e59d/images/BrAPI_org_structure.jpg create mode 100644 v/cd105a0d6c9ddd8cefe2cf6e257d7329e9d9e59d/images/Schema_FAIDARE.png create mode 100644 v/cd105a0d6c9ddd8cefe2cf6e257d7329e9d9e59d/images/Schema_Florilege.jpg create mode 100644 v/cd105a0d6c9ddd8cefe2cf6e257d7329e9d9e59d/images/github.svg create mode 100644 v/cd105a0d6c9ddd8cefe2cf6e257d7329e9d9e59d/images/green_check.png create mode 100644 v/cd105a0d6c9ddd8cefe2cf6e257d7329e9d9e59d/images/mastodon.svg create mode 100644 v/cd105a0d6c9ddd8cefe2cf6e257d7329e9d9e59d/images/orcid.svg create mode 100644 v/cd105a0d6c9ddd8cefe2cf6e257d7329e9d9e59d/images/twitter.svg create mode 100644 v/cd105a0d6c9ddd8cefe2cf6e257d7329e9d9e59d/index.html create mode 100644 v/cd105a0d6c9ddd8cefe2cf6e257d7329e9d9e59d/index.html.ots create mode 100644 v/cd105a0d6c9ddd8cefe2cf6e257d7329e9d9e59d/manuscript.pdf create mode 100644 v/cd105a0d6c9ddd8cefe2cf6e257d7329e9d9e59d/manuscript.pdf.ots diff --git a/README.md b/README.md index 6b7c6ef..9ad14c5 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ # Output directory containing the formatted manuscript The [`gh-pages`](https://github.com/plantbreeding/BrAPI-Manuscript2/tree/gh-pages) branch hosts the contents of this directory at . -The permalink for this webpage version is . +The permalink for this webpage version is . To redirect to the permalink for the latest manuscript version at anytime, use the link . ## Files @@ -35,4 +35,4 @@ Verifying timestamps with the `ots verify` command requires running a local bitc ## Source The manuscripts in this directory were built from -[`e787e4c0ef83569aff82e68b4b63bcd7fbb05973`](https://github.com/plantbreeding/BrAPI-Manuscript2/commit/e787e4c0ef83569aff82e68b4b63bcd7fbb05973). +[`cd105a0d6c9ddd8cefe2cf6e257d7329e9d9e59d`](https://github.com/plantbreeding/BrAPI-Manuscript2/commit/cd105a0d6c9ddd8cefe2cf6e257d7329e9d9e59d). diff --git a/index.html b/index.html index 8c1798a..5ccf49b 100644 --- a/index.html +++ b/index.html @@ -79,7 +79,8 @@ - + + BrAPI Success Stories + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

BrAPI Success Stories

+
+

+This manuscript +(permalink) +was automatically generated +from plantbreeding/BrAPI-Manuscript2@cd105a0 +on August 1, 2024. +

+

Authors

+
    +
  • Peter Selby +
    +ORCID icon +0000-0001-7151-4445GitHub icon +BrapiCoordinatorSelby +
    + +Cornell University +· Funded by The BrAPI Project is funded by the USDA grant NIFA-DSFAS 2022-67021-37024. +

  • +
  • Rafael Abbeloos +
    +ORCID icon +0000-0002-0177-3887GitHub icon +raabb +
    + +VIB Agro-Incubator +

  • +
  • Anne-Francoise Adam-Blondon +
    +ORCID icon +0000-0002-3412-9086 +
    + +Université Paris-Saclay, INRAE, BioinfOmics, Plant Bioinformatics Facility, 78026, Versailles, France; Université Paris-Saclay, INRAE, URGI, 78026, Versailles, France +

  • +
  • Francisco J. Agosto-Pérez +
    +ORCID icon +0000-0002-5059-253XGitHub icon +agostof +
    + +Cornell University +· Funded by Innovation Lab for Crop Improvement Cornell, ILCI: 7200AA19LE00005, FFAR: CA20-SS-0000000103 +

  • +
  • Michael Alaux +
    +ORCID icon +0000-0001-9356-4072 +
    + +Université Paris-Saclay, INRAE, BioinfOmics, Plant Bioinformatics Facility, 78026, Versailles, France; Université Paris-Saclay, INRAE, URGI, 78026, Versailles, France +· Funded by The AGENT project is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 862613. +

  • +
  • Isabelle Alic +
    +ORCID icon +0000-0002-8961-6068GitHub icon +Isabelle-inrae +
    + +MISTEA, University of Montpellier, INRAE, Institut Agro, Montpellier, France +

  • +
  • Khaled Al-Shamaa +
    +ORCID icon +0000-0002-7668-3798GitHub icon +khaled-alshamaa +
    + +ICARDA +

  • +
  • Johan Steven Aparicio +
    + +Alliance Bioversity-CIAT +

  • +
  • Jan Erik Backlund +
    + +Integrated Breeding Platform +

  • +
  • Aldrin Batac +
    + +Integrated Breeding Platform; Leafnode LLC +

  • +
  • Sebastian Beier +
    +ORCID icon +0000-0002-2177-8781GitHub icon +sebeier +
    + +Institute of Bio- and Geosciences (IBG-4: Bioinformatics), CEPLAS, Forschungszentrum Jülich GmbH, Wilhelm Johnen Straße, 52428 Jülich, Germany; Bioeconomy Science Center (BioSC), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany +

  • +
  • Gabriel Besombes +
    +ORCID icon +0009-0004-1359-2145GitHub icon +Gabriel-Besombes +
    + +MISTEA, University of Montpellier, INRAE, Institut Agro, Montpellier, France +

  • +
  • Alice Boizet +
    +ORCID icon +0000-0003-4096-6689GitHub icon +aliceboizet +
    + +CIRAD, UMR AGAP Institut, Montpellier, France.; AGAP Institut, CIRAD, INRAE, Institut Agro, Université de Montpellier, Montpellier, France. +

  • +
  • Matthijs Brouwer +
    +ORCID icon +0000-0001-8183-0484GitHub icon +matthijsbrouwer +
    + +Wageningen University and Research +

  • +
  • Terry Casstevens +
    +ORCID icon +0000-0001-7602-0487GitHub icon +tcasstevens +
    + +Buckler Lab and Institute for Genomic Diversity, Cornell University +

  • +
  • Arnaud Charleroy +
    +ORCID icon +0009-0009-7575-3617GitHub icon +niio972 +
    + +MISTEA, University of Montpellier, INRAE, Institut Agro, Montpellier, France +

  • +
  • Keo Corak +
    +ORCID icon +0000-0002-4129-3319GitHub icon +keocorak +
    + +USDA-ARS Genomics and Bioinformatics Research Unit +

  • +
  • Chaney Courtney +
    GitHub icon +chaneylc +
    + +Clemson University +

  • +
  • Mariano Crimi +
    + +Integrated Breeding Platform +

  • +
  • Gouripriya Davuluri +
    +ORCID icon +0009-0005-2308-0773GitHub icon +Gouripriya5 +
    + +Leibniz Institute of Plant Genetics and Crop Plant Research +· Funded by The AGENT project is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 862613. +

  • +
  • Kauê de Sousa +
    +ORCID icon +0000-0002-7571-7845GitHub icon +kauedesousa +
    + +Digital Inclusion, Bioversity International, Montpellier, France +

  • +
  • Jeremy Destin +
    + +Université Paris-Saclay, INRAE, BioinfOmics, Plant Bioinformatics Facility, 78026, Versailles, France; Université Paris-Saclay, INRAE, URGI, 78026, Versailles, France +

  • +
  • Stijn Dhondt +
    +ORCID icon +0000-0003-4402-2191 +
    + +VIB AgroIncubator +

  • +
  • Ajay Dhungana +
    GitHub icon +GenAjay +
    + +Louisiana State University (LSU) +

  • +
  • Bert Droesbeke +
    +ORCID icon +0000-0003-0522-5674GitHub icon +bedroesb +
    + +VIB Data Core +

  • +
  • Manuel Feser +
    +ORCID icon +0000-0001-6546-1818GitHub icon +feserm +
    + +Leibniz Institute of Plant Genetics and Crop Plant Research; Graduate School DILS, Bielefeld Institute for Bioinformatics Infrastructure (BIBI) +

  • +
  • Mirella Flores-Gonzalez +
    +ORCID icon +0000-0002-7759-1617GitHub icon +mflores202 +
    + +Boyce Thompson Institute +

  • +
  • Valentin Guignon +
    +ORCID icon +0000-0003-0903-6811GitHub icon +guignonv +
    + +Bioversity International, Parc Scientifique Agropolis II, 34397 Montpellier, France +

  • +
  • Corina Habito +
    + +Integrated Breeding Platform +

  • +
  • Asis Hallab +
    +ORCID icon +0000-0002-2421-5431GitHub icon +asishallab +
    + +Jülich research center, Institute of Bio- and Geosciences (IBG), Bioinformatics (IBG-4) and Bingen Technical University of Applied Sciences, Germany +

  • +
  • Jenna Hershberger +
    +ORCID icon +0000-0002-3147-6867GitHub icon +jmh579 +
    + +Clemson University +

  • +
  • Puthick Hok +
    GitHub icon +puthick +
    + +Diversity Arrays Technology (DArT) +

  • +
  • Lynn Carol Johnson +
    +ORCID icon +0000-0001-8103-2722 +
    + +Buckler Lab and Institute for Genomic Diversity, Cornell University +

  • +
  • Sook Jung +
    +ORCID icon +0000-0003-3968-2769 +
    + +Department of Horticulture, Washington State University +· Funded by USDA National Institute of Food and Agriculture National Research Support Project 10 +

  • +
  • Paul Kersey +
    + +Royal Botanic Gardens, Kew +· Funded by The AGENT project is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 862613. +

  • +
  • Andrzej Kilian +
    + +Diversity Arrays Technology (DArT) +

  • +
  • Patrick König +
    +ORCID icon +0000-0002-8948-6793GitHub icon +patrick-koenig +
    + +Leibniz Institute of Plant Genetics and Crop Plant Research +· Funded by The AGENT project is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 862613. +

  • +
  • Suman Kumar +
    +ORCID icon +0009-0005-5832-7190GitHub icon +sumankumar1 +
    + +Leibniz Institute of Plant Genetics and Crop Plant Research +· Funded by The AGENT project is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 862613. +

  • +
  • Josh Lamos-Sweeney +
    GitHub icon +jlamossweeney +
    + +Cornell University +

  • +
  • Laszlo Lang +
    +ORCID icon +0009-0009-8936-4532GitHub icon +LzLang +
    + +Bingen Technical University of Applied Sciences, Berlinstraße 109, 55411 Bingen am Rhein, Germany +

  • +
  • Matthias Lange +
    +ORCID icon +0000-0002-4316-078XGitHub icon +langeipk +
    + +Leibniz Institute of Plant Genetics and Crop Plant Research +· Funded by The AGENT project is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 862613. +

  • +
  • Marie-Angélique Laporte +
    +ORCID icon +0000-0002-8461-9745GitHub icon +marieALaporte +
    + +Digital Inclusion, Bioversity International, Montpellier, France. +

  • +
  • Taein Lee +
    +ORCID icon +0000-0002-2000-2586GitHub icon +leetaei +
    + +Department of Horticulture, Washington State University +

  • +
  • Erwan Le-Floch +
    +ORCID icon +0000-0002-1010-6859GitHub icon +erlefloch +
    + +Université Paris-Saclay, INRAE, BioinfOmics, Plant Bioinformatics Facility, 78026, Versailles, France; Université Paris-Saclay, INRAE, URGI, 78026, Versailles, France +

  • +
  • Francisco López +
    + +International Treaty on Plant Genetic Resources for Food and Agriculture, FAO +

  • +
  • Brandon Madriz +
    + +MrBot Software Solutions, Cartago, Costa Rica +

  • +
  • Dorrie Main +
    +ORCID icon +0000-0002-1162-2724 +
    + +Department of Horticulture, Washington State University +· Funded by {‘USDA NIFA’: ‘2022-51181-38449’} +

  • +
  • Marco Marsella +
    +ORCID icon +0000-0003-0334-8785 +
    + +International Treaty on Plant Genetic Resources for Food and Agriculture, FAO +

  • +
  • Maud Marty +
    +ORCID icon +0009-0001-8764-6466GitHub icon +maudmarty +
    + +Université Paris-Saclay, INRAE, BioinfOmics, Plant Bioinformatics Facility, 78026, Versailles, France; Université Paris-Saclay, INRAE, URGI, 78026, Versailles, France +

  • +
  • Célia Michotey +
    +ORCID icon +0000-0003-1877-1703GitHub icon +cmichotey +
    + +Université Paris-Saclay, INRAE, BioinfOmics, Plant Bioinformatics Facility, 78026, Versailles, France; Université Paris-Saclay, INRAE, URGI, 78026, Versailles, France +

  • +
  • Zachary Miller +
    +ORCID icon +0000-0002-5454-4527GitHub icon +zrm22 +
    + +Buckler Lab and Institute for Genomic Diversity, Cornell University +

  • +
  • Iain Milne +
    +ORCID icon +0000-0002-4126-0859GitHub icon +imilne +
    + +The James Hutton Institute +

  • +
  • Lukas A. Mueller +
    +ORCID icon +0000-0001-8640-1750GitHub icon +lukasmueller +
    + +The Boyce Thompson Institute +

  • +
  • Moses Nderitu +
    GitHub icon +jndmose +
    + +SEQART AFRICA +

  • +
  • Pascal Neveu +
    +ORCID icon +0000-0003-4189-7793GitHub icon +pascalneveu +
    + +MISTEA, University of Montpellier, INRAE, Institut Agro, Montpellier, France +· Funded by This work was supported by the Agence Nationale de la Recherche, programme Investissements d’avenir, ANR-11-INBS-0012 (Phenome) +

  • +
  • Nick Palladino +
    +ORCID icon +0009-0009-1645-297XGitHub icon +nickpalladino +
    + +Breeding Insight, Cornell University +· Funded by U.S. Department of Agriculture, under agreement numbers [8062-21000-043-004-A, 8062-21000-052-002-A, and 8062-21000-052-003-A] +

  • +
  • Tim Parsons +
    +ORCID icon +0000-0001-6209-2455GitHub icon +timparsons +
    + +Breeding Insight, Cornell University +· Funded by U.S. Department of Agriculture, under agreement numbers [8062-21000-043-004-A, 8062-21000-052-002-A, and 8062-21000-052-003-A] +

  • +
  • Cyril Pommier +
    +ORCID icon +0000-0002-9040-8733GitHub icon +cpommier +
    + +Université Paris-Saclay, INRAE, BioinfOmics, Plant Bioinformatics Facility, 78026, Versailles, France; Université Paris-Saclay, INRAE, URGI, 78026, Versailles, France +

  • +
  • Jean-François Rami +
    +ORCID icon +0000-0002-5679-3877GitHub icon +jframi +
    + +CIRAD, UMR AGAP Institut, Montpellier, France.; AGAP Institut, CIRAD, INRAE, Institut Agro, Université de Montpellier, Montpellier, France. +

  • +
  • Sebastian Raubach +
    +ORCID icon +0000-0001-5659-247XGitHub icon +sebastian-raubach +
    + +The James Hutton Institute +

  • +
  • Trevor Rife +
    +ORCID icon +0000-0002-5974-6523GitHub icon +trife +
    + +Clemson University +

  • +
  • Kelly Robbins +
    +ORCID icon +0000-0001-9522-9585 +
    + +Cornell University +· Funded by The BrAPI Project is funded by the USDA grant NIFA-DSFAS 2022-67021-37024. +

  • +
  • Mathieu Rouard +
    +ORCID icon +0000-0003-0284-1885GitHub icon +mrouard +
    + +Bioversity International, Parc Scientifique Agropolis II, 34397 Montpellier, France +

  • +
  • Joseph Ruff +
    + +Royal Botanic Gardens, Kew +· Funded by The AGENT project is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 862613. +

  • +
  • Guilhem Sempéré +
    +ORCID icon +0000-0001-7429-2091GitHub icon +GuilhemSempere +
    + +CIRAD, UMR INTERTRYP, Montpellier, France INTERTRYP, Univ Montpellier, CIRAD, IRD, Montpellier, France French Institute of Bioinformatics (IFB); South Green Bioinformatics Platform, Bioversity, CIRAD, INRAE, IRD, Montpellier, France +

  • +
  • Romil Mayank Shah +
    GitHub icon +romil2807 +
    + +North Carolina State University +

  • +
  • Paul Shaw +
    +ORCID icon +0000-0002-0202-1150GitHub icon +cardinalb +
    + +The James Hutton Institute +

  • +
  • Becky Smith +
    +ORCID icon +0000-0002-8968-3383GitHub icon +Batbaby91 +
    + +The James Hutton Institute +

  • +
  • Nahuel Soldevilla +
    + +Integrated Breeding Platform; Leafnode LLC +

  • +
  • Anne Tireau +
    +ORCID icon +0000-0001-8501-6922GitHub icon +annetireau +
    + +MISTEA, University of Montpellier, INRAE, Institut Agro, Montpellier, France +

  • +
  • Clarysabel Tovar +
    + +Integrated Breeding Platform; Leafnode LLC +

  • +
  • Grzegorz Uszynski +
    GitHub icon +grzegorz69 +
    + +Diversity Arrays Technology (DArT) +

  • +
  • Vivian Bass Vega +
    +ORCID icon +0009-0002-2476-9888GitHub icon +VivianBass +
    + +Bingen Technical University of Applied Sciences, Berlinstraße 109, 55411 Bingen am Rhein, Germany +

  • +
  • Stephan Weise +
    +ORCID icon +0000-0003-4031-9131 +
    + +Leibniz Institute of Plant Genetics and Crop Plant Research +· Funded by The AGENT project is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 862613. +

  • +
  • Shawn C. Yarnes +
    +ORCID icon +0000-0002-1090-0403GitHub icon +SCYarnes +
    + +Breeding Insight, Cornell University +· Funded by U.S. Department of Agriculture, under agreement numbers [8062-21000-043-004-A, 8062-21000-052-002-A, and 8062-21000-052-003-A] +

  • +
  • The BrAPI Consortium +
    + +The BrAPI Consortium +

  • +
+
+

✉ — Correspondence possible via GitHub Issues +or email to +Peter Selby <ps664@cornell.edu>.

+
+

Abstract

+

Population growth and climate change require extraordinary efforts to increase efficiency in breeding programs around the world. In the last few years, new phenotyping techniques, genomics technologies, and genetic approaches such as genomic prediction have provided a boost in genetic gain in breeding, but have also created a flood of data that needs careful management to be fully harnessed. In particular, data integration is a challenge due to the multiple types of data being handled by a variety of disparate and dispersed systems. +The Breeding API (BrAPI) project is an international, grass-roots effort to enable more efficient data management by enabling interoperability among research databases and tools, using a standardized RESTful web service API specification for exchanging breeding related data. +This community driven standard is software agnostic and free to be used by anyone interested in plant breeding, genetics and agronomy data management, including trial, germplasm, phenotyping, and genotyping data management. +This manuscript presents the substantial growth of the project, a wide variety of open source breeding research tools with active BrAPI implementations, and an overview about the current version of BrAPI.

+

Introduction

+ +

To address consequences of climate change and population growth, plant and animal breeding needs to become more efficient and data driven to ensure a healthy, resilient, and sustainable agricultural production system. Modern breeding techniques require large amounts of high quality data to be effective, requiring digital methods for data collection, management, and analysis. They rely on several research disciplines, including plant phenomics, genetics, genomics, and agronomy, involving research institutes, genebanks, and breeding companies. Interoperability between research software tools, systems, and databases can substantially increase the efficiency of a breeding program. The ability to efficiently share data means access to larger and more complete datasets, enabling more accurate computational models, more accurate predictions, and improved selections.

+

The Breeding API (BrAPI) project is an effort to enable interoperability among breeding tools, systems, and databases. BrAPI is a standardized Representational State Transfer (REST), web service, Application Programming Interface (API), specification for breeding, genetics, phenomics and related agricultural data.1 By using the BrAPI standard, breeding software can more easily become interoperable, allowing groups to more easily share data and software tools.

+

Since its first publication1, BrAPI has seen a significant increase in community services, compatible tools, and participating organizations. The community has organized numerous hackathons to evolve the specifications, resulting in continuous improvements and enhancements. This report includes a short technical description of the standard and a showcase of the applications, services, and tools available in the BrAPI community. BrAPI has become an essential part of the digital infrastructure for breeding, genetics and phenomics applications and related agricultural projects. It is the intention of this manuscript to demonstrate the value of BrAPI to the wider scientific community as an effective and efficient means to collaborate and share resources.

+

How it works

+

An API is a technical connection between two pieces of software. Just as a Graphical User Interface (GUI) or a Command Line Interface (CLI) allows a human user to interact with a piece of software, an API allows one software application to interact with another. A GUI or CLI might allow a user to input data, read data, and start processes within an application. An API allows one piece of software (sometimes called a client, user agent, or service consumer) to programmatically input data, read data, and start process within another piece of software (sometimes called a server or service provider).

+

A REST style web service is a type of API commonly used in today’s web infrastructure. REST is a technical architecture that describes the stateless transmission of data between applications. Typically, RESTful web service APIs are implemented using the standard HTTP protocol that most of the modern internet is built upon2. These implementations generally use JavaScript Object Notation (JSON) to represent the data being transferred. Both HTTP and JSON are programming language agnostic, very stable, and very flexible. This means BrAPI can be implemented in almost any piece of software, and can solve a wide range of use cases.

+

Data repositories and service providers can choose to represent their data as a BrAPI compatible API. By mapping their internal data structures to the standard models, data repositories can easily expose data to the outside world. Similarly, they can accept new data from external sources and automatically map the new data into an existing database. Client application developers can take advantage of this standardization by building tools and connectors that integrate with all BrAPI compatible data repositories. Visualization, reporting, analytics, data collection, and quality control tools can be built once and shared with other organizations following the standards. As the number of BrAPI compatible databases, tools, and organizations grows, so does the value added by implementing the standard into a given application.

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Project Updates

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Over its lifetime, the BrAPI project has grown and changed substantially. The latest stable version of the specification (v2.1) looks vastly different from the first version (v1.0) released in 2017. The total size of the specification has almost quadrupled in that time, going from 51 endpoints documented in v1.0 to 201 endpoints documented in v2.1. Because of this growth, the specification documents were reorganized into four modules: BrAPI-Core, BrAPI-Germplasm, BrAPI-Genotyping, and BrAPI-Phenotyping. Figure 1 shows a simplified domain map of the whole BrAPI v2.1 data model, divided into the organizational modules. The early versions of the specification focused on read-only phenotype data, with a small consideration to the other domains. Now the specification has a full representation of most of the major concepts applicable to the breeding and research process. The new specification is also internally consistent, easier to navigate, and allows for read, write, and update capabilities. None of those qualities were a guarantee for the earlier versions.

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+Figure 1: A simplified domain map of the whole BrAPI data model, divided into organizational modules. A more detailed Entity Relationship Diagram (ERD) is available on brapi.org. + +
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As the specification has matured, so have the tools, services, and libraries available to the community to work with the specification. Every version of the specification is now released with a change log to guide developers upgrading from a previous version, an Entity Relationship Diagram (ERD) to describe the whole data model visually, and a JSON Schema data model to be used in some automated development and validation efforts. For groups who are using Java, Java Script, Python, R, or Drupal, there are community maintained libraries available that contain full BrAPI implementations ready to be added to some existing code. The BrAPI Test Server and the BRAVA validation tool are both still available to the community for testing purposes, and they have been maintained to support every version of the specification. Finally, there are three new resource list pages on brapi.org to advertise the BrAPI compatible software available in the community. The BrAPPs list page, servers list page, and compatible software list page showcase many of the BrAPI compatible applications and data resources available in the community.

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Community Growth

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The international BrAPI Community consists of software developers, biologists, and other scientists working on BrAPI related projects and data sources. This community is what sustains the BrAPI project, builds implementations, maintains development tools, and provides input to enhance the specification. As the project has grown, so has the community. The BrAPI project started in June 2014 with less than ten people coming together to discuss the idea. Over the next ten years, the community has grown to between 200 and 250 members.

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The BrAPI Hackathons are a major staple of the BrAPI community3. Twice a year, the community gathers to discuss the specification and collaborate on BrAPI related projects. This time is very valuable to the community; for some organizations, the hackathon is the only time during the year when they can collaboratively work on anything related to BrAPI. During the COVID-19 pandemic, virtual hackathons took the place of in-person events. While the virtual hackathons do not provide the same level of face-to-face time that is crucial to collaborative work, they did allow for more attendees to gather and share their opinions. Going forward, the community leadership has decided to have one in-person hackathon and one virtual hackathon each year, to balance the advantages of both.

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Results

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Below are a number of short success stories from the BrAPI community. These tools, applications, and infrastructure projects serve as another indicator of community growth and success over the past 5-10 years. These stories clearly illustrate all the different ways the BrAPI standard can be used productively and in practice. Figure 2 contains a summary of the tools described below.

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+Figure 2: A summary of all the tools described below and the general areas each tool is designed to handle + +
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Phenotyping

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Phenotyping is fundamental to plant breeding and genetics research, providing the accurate, high-quality data needed for downstream analyses and selection decisions. +Effective phenotyping requires a thorough understanding of both biological research questions and operational data gathering techniques to ensure successful outcomes. +Collected data, subsequent analyses, and data visualizations all impact and shape downstream experiments. +The BrAPI specification supports phenotypic data throughout the entire breeding pipeline, including collection, analyses, publication, and archiving. +The BrAPI community has developed several BrAPI-compatible tools to facilitate data standardization, efficient storage, and curation of phenotypic data and trait metadata. +Ongoing development efforts are creating tools to manage images and other high-throughput phenotypic data sources, further enhancing the precision and efficiency of plant breeding research. +By supporting the collection and storage of phenotypic data accurately and efficiently, BrAPI compatible tools simplify the conversion of phenotypes into insights that are necessary to help digitize and boost modern breeding and genetics research programs.

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Field Book

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Data from plant breeding and genetics experiments has traditionally been collected using pen and paper, but this approach often results in transcription errors and delayed analysis. +Field Book4, a highly-customizable Android app, was developed to help scientists digitize and organize their phenotypic data as measurements are collected. +This effectively improves data collection speed, reduces errors, and enables larger and more robust breeding populations and data sets. +Field Book has added support for BrAPI to streamline data transfer to and from BrAPI-compatible servers. +Removing the need to manually transfer data files simplifies data exchange between these systems and reduces the opportunities for human error and data loss.

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GridScore

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GridScore5 is a multi-platform, web-based application for recording phenotypic observations that harnesses mobile devices to enrich the data collection process. +The GridScore interface closely mirrors the look and feel of printed field plans, creating an intuitive user experience. +GridScore performs a wide range of functions, including data validation, data visualization, georeferencing, cross-platform support, and data synchronization across multiple devices. +The application’s data collection approach employs a top-down view onto the trial and offers field navigation mechanisms using barcodes, QR codes, or guided walks that take the data collector through the field in one of 16 pre-defined orders.

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BrAPI has further increased the value of GridScore by integrating it into the overarching plant breeding workflow. +Trial designs and trait definitions can be imported into GridScore using BrAPI and a finalized trial can be exported via BrAPI to any compatible database.

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ClimMob

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ClimMob6 is a software suite for an alternative research paradigm in experimental agriculture. +In traditional breeding, a small number of researchers design complicated trials in search of the best solutions for few target environments. +ClimMob applies the principles of citizen science and choice experiments to scale the data collection process, mostly in the form of accession rankings. +Although these data may not be as detailed as from a centralized experiment, they can inform decisions across a wide range of environments with increased external validity. +Applications of ClimMob include crop variety testing, evaluating agronomic practices, and investigating climate resilience strategies. +The platform supports experiment design, data collection through mobile apps, and data analysis to provide actionable insights.

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ClimMob uses BrAPI to retrieve curated germplasm information from breeding databases for trial design, subsequently enabling the automatic upload of collected ClimMob-collected data to a central breeding database for long-term storage and analysis. +Analyzed data can also be pushed from ClimMob to breeding databases, providing breeders with insights into the potential adoption of the tested crop varieties.

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ImageBreed

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Unoccupied aerial and ground vehicles (UAVs and UGVs) enable the high throughput collection of images and other sensor data in the field, but the rapid processing and management of these data is often a bottleneck for breeding programs seeking to deploy these technologies for time-sensitive decision making. +ImageBreed7 is an open-source, BrAPI-compliant image processing tool that supports the routine use of UAVs and UGVs in breeding programs through standardized pipleines. +It creates orthophotomosaics, applies filters, assigns plot polygons, and extracts ontology-based phenotypes from raw UAV-collected images. +The BrAPI standard is used to push these phenotypes back to a central breeding database where they can be analyzed with other experiment data. +The ImageBreed team has collaborated with others in the community to enhance the BrAPI image data standards, which it uses to upload raw images to a central breeding database, or any other BrAPI-compatible long term storage service.

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PHIS

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PHIS8, the Hybrid Phenotyping Information System, is an ontology-driven information system based on semantic web technologies and the OpenSILEX framework. +PHIS is deployed in several field and greenhouse platforms of the French national PHENOME and European EMPHASIS infrastructures. +It manages and collects data from basic phenotyping and high-throughput phenotyping experiments on a daily basis. +PHIS unambiguously identifies the objects and traits in an experiment and establishes their types and relationships via ontologies and semantics.

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Since its inception, PHIS has been designed to be BrAPI compliant, encompassing the Core, Phenotyping, and Germplasm BrAPI modules. +This enables integration with other BrAPI-compliant systems and platforms, simplifying the exchange of accession and phenotyping data across systems. PHIS is actively integrated with the genebank accessions management system OLGA, and is indexed by the FAIDARE data portal9. +BrAPI-enabled interoperability promotes a more coherent and efficient approach to the management and use of phenotyping data on various platforms and research initiatives within the European scientific community. +BrAPI compliance also ensures that PHIS is compatible with other standards such as the Minimal Information About a Plant Phenotyping Experiment (MIAPPE10). +By integrating BrAPI requirements into its structure, PHIS not only meets the standards of the phenotyping field, but also strengthens its capacity for interoperability and effective collaboration in the wider context of plant breeding and related fields.

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PIPPA

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PIPPA11 is a data management system used for collecting data from the Weighing, Imaging and Watering Machines (WIWAM)12 range of automated high-throughput phenotyping platforms. +These platforms have been deployed by research institutes and commercial breeders across Europe. +They can be set up in a variety of configurations with different types of equipment including weighing scales, cameras, and environment sensors. +The software features a web interface with functionality for setting up new experiments, planning imaging and irrigation treatments, linking metadata (genotype, growth media, manual treatments) to pots, and importing, exporting, and visualizing data. +It also supports the integration of image analysis scripts and connects to a compute cluster for job submission.

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To share the phenotypic data from PIPPA experiments linked to publications, an implementation of BrAPI v1.3 was developed which allowed read only access to the data in the BrAPI standardized format. +This server was registered on FAIDARE9, allowing the data to be found alongside data from other BrAPI-compatible repositories.

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Throughout its development, the PIPPA project has adhered to guidelines set forth by BrAPI and the MIAPPE10 scientific standard. +Current efforts are focused on delivering a public BrAPI v2.1 endpoint and increasing the availability of public high-throughput datasets via BrAPI.

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Trait Selector BrAPP

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The Trait Selector BrAPP is a JavaScript-based application used to visually search and select traits from an ontology. +The Trait Selector employs a visual aid, an image of a plant, to connect plant anatomy with relevant trait ontology terms. +Instead of scrolling through a long list of possible traits, the user can click on pieces of the image to show the traits associated with specific plant structures. +The Trait Selector BrAPP can be used to quickly find specific traits or to identify accessions that have a specific phenotype of interest.

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The Trait Selector BrAPP has been successfully added to Cassavabase13 and MGIS14, and it can be integrated into any website or system with a BrAPI-compatible data source. +A breeding database would need to only implement the BrAPI endpoints for Traits, Observations, and Variables, while a genebank would require only Traits and Germplasm Attributes.

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Genotyping

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Genotyping has become a cornerstone of most breeding processes, but managing the data can be challenging. Understanding different genotyping protocols for various crops is crucial due to the unique genetic structures of each species. Techniques such as SNP genotyping, Genotyping-by-Sequencing (GBS), SSRs, Whole Genome Sequencing (WGS), and array-based genotyping each offer specific advantages depending on the crop and research objectives. BrAPI supports genotypic data by utilizing existing standards such as VCF15 and the GA4GH Variants schema16. The BrAPI community has developed compatible tools for storing, searching, visualizing, and analyzing genotypic data, making it easier to integrate and utilize this information in breeding programs. Mastery of the various genotyping protocols ensures efficient and effective breeding, while BrAPI-compliant tools streamline data management and analysis, enhancing the ability to make data-driven decisions in developing superior crop varieties.

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DArT Sample Submission

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The Diversity Arrays Technology (DArT) genotyping lab is heavily used world wide when it comes to plant genotyping. With over 1200 available organisms and species, a client base on every continent, and many millions of samples processed, DArT provides services for several generic and bespoke genotyping technologies and solutions. Processes of sample tracking and fast data delivery are at the core of the ordering system developed at DArT. The ordering system is tightly integrated with DArTdb - DArT’s custom LIMS operational system, which drives laboratory, quality, and analytical processes.

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Diversity Arrays Technology has been a part of BrAPI community since its inception. DArT developers have worked with the BrAPI community contributing to various aspects of the API specification. One key aspect was establishing a standard API for sending sample metadata to the lab for genotyping. This solution eliminates much of the human error involved with sending samples to an external lab, and also allows for an automated process of sample batch transfers. The current implementation also allows for an order status verification, automated data discovery, and data downloads. Data are delivered as standard data packages with self-describing metadata.

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The current BrAPI implementation at DArT is in production and it is compatible with the newest BrAPI specification. Further details about DArT’s ordering system can be found at DArT Ordering System and also at DArT Help.

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DArTView

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DArTView is a desktop application for marker data curation via metadata filtering. DArTView enables genotype variant data visualization designed such that users can easily identify trends or correlations within their data. The primary goal of the tool is to overcome tedious manual calculation of marker data through common spreadsheet applications like Excel. Users are able to import marker data from csv files, but DArTView has been recently enhanced to be BrAPI compatible. BrAPI provides a consistent data standard across databases and data resources, which allows DArTView to use any BrAPI-compatible server as an input data source. DArTView’s compatibility with BrAPI also ensures easy integration with other tools and pipelines that would use DArTView for marker filtering and exploration.

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Initially developed by DArT, the tool is gaining popularity within the breeding community, especially in Africa. Future releases will focus on enhancing the BrAPI compatibility, making it accessible to more breeders and researchers in the region. A web enabled version of DArTView is in development. This new version will allow for further collaboration opportunities with other interested partners who would like to integrate it as part of their pipelines.

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DivBrowse

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DivBrowse17 is a web platform for exploratory data analysis of large genotyping studies. +The software can be run standalone or integrated as a plugin into existing data web portals. +At its core, DivBrowse combines the convenience of a genome browser with features tailored to germplasm diversity analysis. +DivBrowse provides visual access to VCF files obtained through genotyping experiments, and can handle hundreds of millions of variants across thousands of samples. +It is able to display genomic features such as nucleotide sequence, associated gene models, and short genomic variants. DivBrowse also calculates and displays variant statistics such as minor allele frequencies, the proportion of heterozygous calls, and the proportion of missing variant calls. +Dynamic principal component analyses can be performed on a user-specified genomic area to provide information on local genomic diversity.

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DivBrowse employs the BrAPI Genotyping module to access genotypic data from external BrAPI endpoints. +DivBrowse also has an interface to BLAST, which can be used to directly access genes or other genomic features. +The modular structure of DivBrowse allows developers to configure and easily embed links to other external information systems.

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Flapjack

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Flapjack18 is a multi-platform desktop application for data visualization and breeding analysis (e.g., pedigree verification, marker-assisted backcrossing and forward breeding) using high-throughput genotype data. +Data can be imported into Flapjack from any BrAPI-compatible data source with genotype data available. +Flapjack Bytes is a smaller, lightweight, and fully web-based counterpart to Flapjack that can be easily embedded into a database website to provide similar visualizations online. +Traditionally supporting its own text-based data formats, Flapjack’s use of BrAPI has streamlined the end-user experience for data import. +Work is underway to determine the best methods to exchange analysis results using future versions of the API.

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Gigwa

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Gigwa is a Java EE web application providing a means to centralize, share, finely filter, and visualize high-throughput genotyping data19. Built on top of MongoDB, it is scalable and can support working smoothly with datasets containing billions of genotypes. It is installable as a Docker image or as an all-in-one bundle archive. It is straightforward to deploy on servers or local computers and has thus been adopted by numerous research institutes from around the world. Notably, Gigwa serves as a collaborative management tool and a portal for exposing public data for genebanks and breeding programs at some CGIAR centers20. The total amount of data hosted and made widely accessible using this system has continued to grow over the last few years.

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The Gigwa development team has been involved in the BrAPI community since 2016 and took part in designing the genotype-related section of the BrAPI standard. Gigwa’s first BrAPI-compliant features were designed for compatibility with the Flapjack visualization tool18. Over time, Gigwa has established itself as the first and most reliable implementation of the BrAPI-Genotyping endpoints. Local collaborators and external partners used it as a reference solution to design a number of tools taking advantage of the BrAPI-Genotyping features (e.g., BeegMac, SnpClust, QBMS).

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Some use-cases require Gigwa to also consume data from other BrAPI servers. This requirement led to the implementation of BrAPI client features within Gigwa. A close collaboration was established with the Integrated Breeding Platform team their widely used Breeding Management System (BMS). This collaboration means both applications are now frequently deployed together; Gigwa pulling germplasm or sample metadata from BMS, and BMS displaying Gigwa-hosted genotypes within its own UI. +

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PHG

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The Practical Haplotype Graph (PHG) is a graph-based computational framework that represents large-scale genetic variation and is optimized for plant breeding and genetics21. Using a pangenome approach, each PHG stores haplotypes (the sequence of part of an individual chromosome) to represent the collected genes of a species. This allows for a simplified approach for dealing with large scale variation in plant genomes. The PHG pipeline provides support for a range of genomic analyses and allows for the use of graph data to impute complete genomes from low density sequence or variant data.

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Users access the crop databases either with direct calls to the PHG embedded server or indirectly using the rPHG library from an R environment. The PHG server accepts BrAPI queries to return information on sample lists and the variants used to define the graph’s haplotypes. In addition, PHG users utilize the BrAPI Variant Sets endpoint query to return links to VCF files containing haplotype data. Work on the PHG is ongoing and it is expected to support additional BrAPI endpoints that allow for fine tuned slicing genotypic data in the near future.

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Germplasm Management

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Germplasm data management is crucial due to the vast quantity of new accessions, variants, and lines created yearly. Germplasm is the basis of variation on which plant breeders rely to upgrade and optimize their breeding programs. This is essential at any scale including individual breeding programs, national initiatives, and international collaborations. BrAPI supports the transmission of germplasm passport data, pedigree trees, and crossing metadata. The BrAPI community has developed compliant tools for storing, searching, and visualizing this metadata, facilitating efficient management. Additionally, there are plans to establish federated networks of genebank data connected via BrAPI, enhancing global accessibility and collaboration in germplasm management.

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AGENT

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In the global system for ex situ conservation of plant genetic resources (PGR)22, material is being conserved in about 1750 collections23 totalling ~5.8 million accessions. Unique and permanent identifiers in the form of DOIs are available for more than 1.7 million accessions via the Global Information System24 of the International Treaty on Plant Genetic Resources for Food and Agriculture. Each DOI is linked to some basic descriptive data that facilitates the use of these resources, mainly passport data. However, a data space beyond the most basic information is needed that includes genotypic and phenotypic data. This space will help to answer questions about the global biological diversity of plant species, the detection of duplicates, the tracking of provenance for the identification of genetic integrity, the selection of the most suitable material for different purposes, and to support further applications in the field of data mining or AI. In this context, the aim of the AGENT project, funded by the European Commission, is to develop a concept for the digital exploitation and activation of this PGR via European ex situ genebanks according to the FAIR principles25 and to test it in practice using two important crops, barley and wheat. Thirteen European genebanks and five bioinformatics centers are working together and have agreed on standards and protocols for data flow and data formats26 for the collection, integration, and archiving of genotypic and phenotypic data.

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The BrAPI specification is one of the agreed standards, that are detailed in the AGENT guidelines for dataflow27. The implemented BrAPI interface enables the analysis of current and historic genotypic and phenotypic information. This will drive the discovery of genes, traits, and knowledge for future missions, complement existing information for wheat and barley, and use the new data standards and infrastructure to promote better access and use of PGR for other crops in European genebanks. The AGENT database backend aggregates curated passport data, phenotypic data, and genotypic data on wheat and barley accessions of 18 project partners. This data is accessible via BrAPI endpoints and explorable in a web portal. Genotyping data uses the DivBrowse17 storage engine and its BrAPI interface. Soon, the BrAPI implementation will be expanded to enable the integration of analysis pipelines in the AGENT portal, such as the FIGS+ pipeline developed by ICARDA28. There is also a plan to integrate the data collected by the AGENT project into the European Search Catalogue for Plant Genetic Resources (EURISCO)29. + +

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Florilège

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Florilège is a web portal designed primarily for the general public to access public plant genetic resources held by Biological Resource Centers across France. Through this portal, users can browse accessions from over 50 plant genera, spread across 19 genebanks. It allows users to view available seeds and plant material, including options for ordering material. Florilège provides a centralized access to the various French collections of plant genetic resources available to the public.

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Florilège retrieves accession information from several BrAPI-compliant systems. Key among these are OLGA, a genebank accessions management system, and GnpIS, an INRAE data repository for plant genetic resources, phenomics, and genetics30,31. Using BrAPI to gather data from these systems reduced development efforts and enabled standardized data retrieval. As a result, BrAPI has become the de facto standard within the French plant genetic resources community for exchanging information. During development, the Florilège team also proposed several enhancements to the BrAPI specifications themselves, such as additional support for Collection objects or improved reference linking, to better accommodate their specific use case.

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GLIS

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The Global Information System (GLIS) on Plant Genetic Resources for Food and Agriculture (PGRFA) of the International Treaty on Plant Genetic Resources for Food and Agriculture (ITPGRFA) is a web-based global entry point for PGRFA data24. It allows users and third-party systems to access information and knowledge on scientific, technical, and environmental matters to strengthen PGRFA conservation, management, and utilization activities. The system and its portal also enable recipients of PGRFA to make available all non-confidential information on germplasm according to the provisions of the Treaty and facilitates access to the results of their research and development.

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Thanks to the adoption of Digital Object Identifiers (DOIs) for Multi-Crop Passport Descriptors (MCPD) of PGRFA accessions, the GLIS Portal provides access to 1.7 million PGRFA in collections conserved worldwide. Of these, over 1.5 million are accessible for research, training and plant breeding in the food and agriculture domain.

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The Scientific Advisory Committee of the ITPGRFA have repeatedly welcomed efforts on interoperability among germplasm information systems. In this context, the GLIS Portal adopted the BrAPI v1 in 2022. Integrating BrAPI among the GLIS content negotiators facilitates queries and the exchange of content for data management in plant breeding. The Portal also offers other protocols (XML, DarwinCore, JSON and JSON-LD) to increase data and metadata connectivity. In the near future, depending on the availability of resources, upgrading to BrAPI v2 is planned.

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Helium

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Helium32 is a plant pedigree visualization platform designed to account for the specific problems that are unique to plant pedigrees. A pedigree is a representation of how genetically discrete individuals are related to one another and is therefore a representation of the genetic relationship between individual plant lines, their parents and progeny. Plant pedigrees are often used to check for potential genotyping or phenotyping errors, since these errors, by the very nature of Mendelian inheritance, are constrained by the pedigree structure in which they exist33. The accurate representation of pedigrees, and the ability to pull pedigree data from different data sources is important in plant breeding and genetics. Therefore, ways to visualize and interact this complex data in meaningful ways is critical.

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From its original desktop interface, Helium has developed into a web-based visualization platform implementing BrAPI calls to allow users to import data from other BrAPI-compliant databases. The ability to pull data from BrAPI-compliant data sources has significantly expanded Helium’s capability and utility within the community. Helium is used in projects ranging in size from tens to tens of thousands of lines and across a wide variety of crops and species. While originally designed for plant data34 it has also found utility in other non-plant projects35 highlighting its broad utility. BrAPI also allows Helium to provide direct dataset links to collaborators, allowing the original data to be held with the data provider and utilizing Helium for its visualization functionality. Our current Helium deployment includes example BrAPI calls to a barley dataset at the James Hutton Institute to allow users to test the system and features it offers.

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MGIS

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The Musa Germplasm Information System (MGIS) serves as a comprehensive community portal dedicated to banana diversity, a crop critical to global food security14. MGIS offers detailed information on banana germplasm, focusing on the collections held by the CGIAR International Banana Genebank (ITC)36. It is built on the Drupal/Tripal technology, like BIMS37 and Florilège.

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Since its inception, MGIS developers have actively participated in the BrAPI community. The MGIS team pushed for the integration of the Multi-Crop Passport Data (MCPD) standard into the Germplasm module of the API. MCPD support was added in BrAPI v1.3, and MGIS now provides passport data information on ITC banana genebank accessions (with GLIS DOI), synchronized with Genesys. MGIS also enriches the passport data by incorporating additional information from other germplasm collections worldwide. All the germplasm data is available through the BrAPI Germplasm module implementation. For genotyping data, MGIS integrates with Gigwa19, which provides a tailored implementation of the BrAPI genotyping module. Furthermore, MGIS supports a set of BrAPI phenotyping endpoints, facilitating the exposure of morphological descriptors and trait information supported by ontologies like the Crop Ontology38. MGIS has integrated the Trait Selector BrAPP, and there are use cases implemented to interlink genebank and breeding data between MGIS and the breeding database MusaBase.

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Breeding and Genetics Data Management

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While specialty data management is important for some use cases, often breeders want a central repository or access point of critical data. General breeding and genetics data management systems and web portals support some level of phenotypic, genotypic, and germplasm data, as well as trial, equipment, and people management. By enabling BrAPI support, these larger systems can connect with smaller tools and specialty systems to provide more functionality under the same user interface. There are several breeding data management systems developed in the BrAPI community, each with their own strengths.

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BIMS

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The Breeding Information Management System (BIMS)37 is a free, secure, online breeding management system which allows breeders to store, manage, archive, and analyze their private breeding program data. +BIMS enables individual breeders to have complete control of their own breeding data along with access to tools such as data import, export, analysis, and archiving for their germplasm, phenotype, genotype, and image data. +BIMS is currently implemented in five community databases, the Genome Database for Rosaceae39, CottonGEN40, the Citrus Genome Database, the Pulse Crop Database, and the Genome Database for Vaccinium, where it enables individual breeders to import publicly available data. +BIMS is also implemented in the public database breedwithbims.org that any crop breeder can use.

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BIMS primarily utilizes BrAPI to connect with Field Book4, enabling seamless data transfer between data collection and subsequent management in BIMS. Data transfer through BrAPI between BIMS and other resources such as Breedbase13, GIGWA19, and Breeder Genomics Hub is under development.

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BMS

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The Breeding Management System (BMS), developed by the Integrated Breeding Platform (IBP), is a suite of tools designed to enhance the efficiency and effectiveness of plant breeding. BMS covers all stages of the breeding process, with the emphasis on germplasm management and ontology-harmonized phenotyping. It also features analytics and decision-support tools. With its focus on interoperability, BMS integrates smoothly with BrAPI, facilitating easy connections with a broad array of complementary tools and databases. Notably, the BMS is often deployed together with Gigwa to fulfill the genotyping data management needs of BMS users.

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The brapi-sync tool, a significant component of BMS’s BrAPI capabilities, was developed by the IBP and released as a BrAPP for community use. Brapi-sync is designed to enhance collaboration among partner institutes within a network such as Innovation and Plant Breeding in West Africa (IAVAO). The tool enables the sharing of germplasm and trial metadata across BrAPI-enabled systems. It helps overcome traditional barriers to collaboration, ensuring data that was once isolated within specific programs or platforms can now be easily shared, integrated, and synchronized.

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Additionally, brapi-sync improves data management by maintaining links to the original source of each entity it transmits. This retains the original context of the data and establishes a traceability mechanism for accurate data source attribution and verification. Such practices are crucial for maintaining data integrity and fostering trust among collaborative partners, ensuring access to accurate, reliable, and current information.

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Breedbase

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Breedbase is a comprehensive breeding data management system13,41 that implements a digital ecosystem for all breeding data, including trial data, phenotypic data, and genotypic data. Data acquisition is supported through data collection apps such as Fieldbook4, Coordinate, and InterCross, as well as through drone imagery, Near Infra-Red Spectroscopy (NIRS), and other technologies. Search functions, such as the Search Wizard interface, provide powerful query capabilities. Various breeding-centric analysis tools are available, including mixed models, heritability, stability, PCA, and various clustering algorithms. The original impetus for creating Breedbase was the advent of new breeding paradigms based on genomic information such as genomic prediction algorithms42 and the accompanying data management challenges. Thus, complete genomic prediction workflow is integrated in the system.

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The BrAPI interface is crucial for Breedbase. Breedbase uses BrAPI to connect with the data collection apps, other projects such as CLIMMOB6, and native BrAPPs built into the Breedbase webpage. Users also appreciate the ability to connect to Breedbase instances using packages such as QBMS43 for data import into R for custom analyses. The Breedbase team has been part of the BrAPI community since its inception, and has continuously adopted and contributed to the BrAPI standard.

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DeltaBreed

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DeltaBreed is an open-source breeding data management system designed and developed by Breeding Insight to support USDA-ARS specialty crop and animal breeders. DeltaBreed differs from other related systems in that is customizable to small breeding teams and generalized enough to support the workflows of diverse niche species. DeltaBreed is a unified system that connects a variety of BrAPI applications (see list below). BrAPI integration allows the complexity underlying interoperability to be hidden, shielding users from multifactorial differences between various applications. DeltaBreed, adhering to the BrAPI model, establishes data standards and validations for users and provides a singular framework for data management and user training.

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DeltaBreed users need not be aware of BrAPI or the specifics of underlying applications but will notice that BrAPI interoperability reduces the need for human-mediated file transfers and data manipulation. Field Book users, for example, can connect to their DeltaBreed program, authenticate, and pull studies and traits directly from DeltaBreed to Field Book on their data collection device. The subsequent step of pushing observations from Field Book to DeltaBreed is straightforward via BrAPI, but is pending implementation until observation transaction handling is improved, intentional and inadvertent repeated measures are differentiated, and a data staging area is implemented for quality control.

+ +

DeltaBreed is integrated with at least eight other BrAPI community tools, and plans to integrate with several more as the project continues. There is a BrAPI enabled connection, either in development or production, with all of the following tools: BrAPI Java Test Server, BreedBase, Field Book, Gigwa, QBMS, Mr Bean, Helium and the Pedigree Viewer BrAPP.

+

FAIDARE

+ +

FAIDARE44 is a data discovery portal providing a biologist-friendly search system over a global federation of 40 plant research databases. +It allows users to identify data resources using a full text search approach combined with domain specific filters. +Each search result contains a link back to the original database for visualization, analysis, and download. +The indexed data types are broad and include genomic features, selected bibliography, QTL, markers, genetic variation studies, phenomic studies, and plant genetic resources. +This inclusiveness is achieved thanks to a two stage indexation data model. The first index, more generic, provides basic search functionalities and relies on five fields: name, link back URL, data type, species, and exhaustive description. +To provide more advanced filtering, the second stage indexation mechanism takes advantage of BrAPI endpoints to get more detailed metadata on germplasm, genotyping studies and phenotyping studies.

+

The FAIDARE indexation mechanism relies on a public software package45 that allows data resource managers to request the indexation of their database. +This BrAPI client is currently able to extract data from any BrAPI 1.3 and 1.2 endpoint, and the development of BrAPI 2.x indexation will be initiated in 2025. +Since not all databases are willing to implement BrAPI endpoints, it is possible to generate metadata as static BrAPI-compliant JSON files, using the BrAPI standard as a file exchange format.

+

The FAIDARE architecture has been designed by elaborating on the BrAPI data model in combination with the GnpIS Software Architecture30. +It uses an Elasticsearch NoSQL engine that searches and serves enriched versions of the BrAPI JSON data model. +FAIDARE also includes a BrAPI endpoint using all indexed metadata. +It has been adopted by several communities including the ELIXIR and EMPHASIS European infrastructures, and the WheatIS of the Wheat-Initiative. +Several databases are added each year to the FAIDARE global federation, adding to both the portal and BrAPI adoption.

+ +

Germinate

+ +

Germinate46,47 is an open-source plant genetic resources database that combines and integrates various types of plant breeding data including genotypic, phenotypic, passport, image, geographic, and climate data into a single repository. +Germinate is tightly linked to the BrAPI specification and supports the majority of BrAPI endpoints for querying, filtering, and submission.

+

Germinate connects with other BrAPI-enabled tools such as GridScore for phenotypic data collection, Flapjack for genotypic data visualization, and Helium for pedigree visualization. +Additionally, due to the nature of BrAPI, Germinate can act as a data repository for any BrAPI-compatible tool. +The interoperability provided by BrAPI reduces the need for manual data handling, providing the direct benefits of faster data processing, fewer human errors, and improved data security and integrity.

+

Analytics

+

Modern breeding programs can utilize data management systems to maintain both phenotypic and genotypic data. Numerous systems are available for adoption. To fully leverage the benefits of digitalization in this ecosystem, breeders need to utilize data from different sources to make efficient data-driven decisions. With increased computational power at their disposal, scientists can construct more advanced analysis pipelines by combining various data sources.

+

The tools developed by the BrAPI community can pull in data from multiple BrAPI compatible data sources and provide enhanced analytical functionality. In many cases, there is no longer a need to import and export large data files to a local computational environment just to run standard analytical models. These tools are able to extract the data they need from a data source without much human intervention or human error.

+

G-Crunch

+ +

G-Crunch is an upcoming user-facing tool to make simple, repeatable analysis requests. The lightweight UI can be used to specify and filter incoming data, select specific analysis criteria, and trigger any analytics pipeline that is built into the specific framework instance. G-Crunch is currently built on top of the open source Analytics Framework project, and can run pipelines using tools such as sommer and ASREML. Each piece fo the data and pipeline can be separately specified, which can allow flexibility when running complex analysis. A ‘test’ analysis can be run on small data sets with small or local analytics engine, then quickly re-direct G-Crunch to a larger dataset and a larger computational framework. This mitigates the complications of moving data around and introducing errors from manually triggering the analysis steps.

+

G-Crunch relies on BrAPI endpoints to access phenotypic and genotypic data sources, as well as an API currently implemented in the Analytics Framework to start and track processes. G-Crunch, as a tool, couldn’t feasibly exist without BrAPI. The support of BrAPI interfaces allows G-Crunch to use one unified request method, and adapt to the user’s existing network of BrAPI-compliant tools. This lowers the barrier to entry for adoption, and makes analysis pipelines easily repeatable.

+

QBMS

+ +

Many plant breeders and geneticists analyze their datasets using the R statistical programming language, but this requires the import of data into an R environment. +BrAPI enables access to pull datasets into R from compatible databases, but API backend processes, such as authentication, tokens, TCP/IP protocol, JSON format, pagination, stateless calls, asynchronous communication, and database IDs are complex for users to navigate. +The QBMS R package eliminates technical barriers scientists experience when using the BrAPI specification in their analysis scripts and pipelines by providing breeders with stateful functions familiar to them when navigating their GUI systems43. +QBMS enables users to query and extract data into a dataframe, a common structure in the R language, providing an intuitive connection with breeding data management systems.

+

The community has built extended solutions on top of QBMS, incorporating the package into R Shiny BrAPPs such as Mr.Bean. +QBMS is open-source and available on the official CRAN repository, where it has garnered over 9400 downloads.

+

Mr.Bean

+ +

Mr.Bean48 is a graphical user interface designed to assist breeders, statisticians, and individuals involved in plant breeding programs with the analysis of field trials. By utilizing innovative methodologies such as SpATS for modeling spatial trends, and autocorrelation models to address spatial variability, Mr.Bean proves highly practical and powerful in facilitating faster and more effective decision-making. Modeling Genotype-by-environment interaction poses its challenges, but Mr.Bean offers the capability to explore various variance-covariance matrices, including Factor Analytic, compound symmetry, and heterogeneous variances. This aids in the assessment of genotype performance across diverse environments.

+

Mr.Bean boasts flexibility in importing different file types, yet for users managing their data within data management systems (DMS), the process of downloading from their DMS and importing it into Mr.Bean can be cumbersome. To address this issue, QBMS was integrated into the back-end. This feature prompts users to input the URL of a BrAPI compatible server, enter their credentials (if necessary), and select the specific trial they wish to analyze. Subsequently, users can seamlessly access their dataset through BrAPI and utilize it across the entire Mr.Bean interface.

+

SCT

+ +

The Sugarcane Crossing Tool (SCT) is a lightweight R-Shiny dashboard application designed to receive, process, and visualize data from a linked BreedBase13 instance. This application is being developed collaboratively with members of the Sugarcane Integrated Breeding System, who have advocated for an application that assists them in designing crosses based on queried information from a list of available accessions. By leveraging existing community resources, the team has been able to develop a simple, BrAPI-enabled, application without possessing extensive programming knowledge or experience. The SCT is presented as an inspiration for similarly positioned scientists to consider developing custom applications for specific tasks.

+ + +

The crossing tool utilizes a modified version of the BrAPI-R library to access a compliant database, and it employs standard R/JavaScript packages to aggregate and visualize data. Modules within the application allow breeders to query the database (through BrAPI) for information relevant to their decision-making process. This includes the number and sex of flowering accessions, deep pedigree and relatedness information, summarized trial data, and the prior frequency and success of potential cross combinations. Future versions of this tool will provide additional decision support (e.g. ranked potential crosses) to enhance the accuracy and efficiency of crossing.

+

ShinyBrAPPs

+

The ShinyBrAPPs code repository contains a number of useful tools, built using the R-Shiny framework and the BrAPI R open source library. The R-Shiny framework allows user communities to quickly prototype and produce applications that are finely tailored to their needs, thus improving adoption and daily use of data management systems. An international collaboration of developers from CIRAD and the IBP have been working together as part of the IAVAO breeders community to develop these ShinyBrAPPs, in support of national breeding programs in western Africa. These applications are typically connected to BMS and/or Gigwa and provide tools for specific use cases. BrAPI compliance offers these systems the opportunity to add functionalities in a modular way through the development of external plugin applications that can quickly fulfill specific needs for this group of breeders and scientists.

+

So far, four applications have been developed covering the fields of trial data quality control, single trial statistical analysis, breeding decision support, and raw genotyping data visual inspection. The “BMS trial data explorer” retrieves data from a single multi-location trial and displays data counts and summary box-plot for all variables measured in different studies. It also provides an interactive distribution plot to easily select observations that require curation and a report of candidate issues that needs to be addressed by the breeder. The “STABrAPP” tool is an application for single trial mixed model analysis. It basically provides a GUI to the StatGen-STA R package. The “DSBrAPP” tool is a decision support tool helping breeders to select germplasm according to their various characteristics and save this germplasm list into BMS. Finally, the “snpclust” tool enables a user to check and manually correct the clustering of fluorescence based SNP genotyping data.

+

General Infrastructure

+

Adopting BrAPI compatibility into an existing system can be difficult sometimes. The BrAPI Community has developed several tools to make adoption easier. This includes things like pre built code libraries, connectors to other technology standards, and mappers to alternate data types or data files. The goal is to lower the barrier to entry for the BrAPI community, making it easier for other groups to get started and connect their existing data to the standard.

+

BrAPIMapper

+ +

BrAPIMapper is a full BrAPI implementation designed to be a convenient wrapper for any breeding related data source. BrAPIMapper is provided as a Docker application that can connect to a variety of external data sources including mySQL or PostgreSQL databases, generic REST services, flat files (XML, JSON, CSV/TSV/GFF3/VCF, YAML), or any combination of these. It provides an administration user interface to map BrAPI data models to external data sources. The interface allows administrators to select the BrAPI specification versions to use and which endpoints to enable. Data mapping configuration import and export features simplify upgrades to future BrAPI versions; administrators only have to map missing fields or make minor adjustments. BrAPIMapper supports the primary BrAPI features including paging, deferred search results, user lists, and authentication. Access restrictions to specific endpoints can be managed through the administration interface as well. This tool aims to accelerate BrAPI services deployment while ensuring specification compliance.

+

MIRA and BrAPI2ISA

+ +

Since the release of BrAPI 1.3, efforts have been made to incorporate support for the MIAPPE (Minimal Information About a Plant Phenotyping Experiment)10 standard into the specification, achieving full compatibility in BrAPI 2.0. Consequently, BrAPI now includes all attributes necessary for MIAPPE compliance, adhering to standardized descriptions in accordance with MIAPPE guidelines. In some communities and projects, phenotyping data and metadata are archived and published as structured ISA-Tab files, validated using the MIAPPE ISA configuration49. Although ISA-Tab is easy to read for non-technical experts due to its file-based approach, it lacks programmatic accessibility, particularly for web applications.

+

MIRA enables the automatic deployment of a BrAPI server on a MIAPPE-compliant dataset in ISA-Tab format, facilitating programmatic access to these datasets. It is deployable from a Docker image with the dataset mounted. The tool leverages the mapping between MIAPPE, ISA-Tab, and BrAPI, eliminating the need for parsing or manual mapping of datasets compliant with (meta-)data standards. By providing programmatic access through BrAPI, MIRA facilitates the integration of phenotyping datasets into web applications.

+

The BrAPI2ISA service functions as a converter between a BrAPI-compatible server and the ISA-Tab format. The tool simplifies, automates, and facilitates the archiving of data, thereby enhancing data preservation and accessibility. The BrAPI2ISA tool is compatible with BrAPI 1.3 and welcomes community contributions to support the latest versions of BrAPI.

+

GraphQL Data-warehouse

+ +

Using the Zendro set of automatic software code generators, a fully functional, efficient, and cloud-capable BrAPI data-warehouse has been created for the current version of the BrAPI data models. Unlike most BrAPI-compliant data sources, this data-warehouse supports a GraphQL API rather than a RESTful API. This API provides secure access to data read and write functions for all BrAPI data models. It provides create, read, update, and delete (CRUD) functions that are standardized and accept the same parameters for all data models. Zendro supports a large number of underlying database systems, allowing flexibility during installation and integration.

+

The GraphQL server is particularly rich in features. Logical filters allow for exhaustive search queries, whose structure is highly intuitive and based around logical triplets. A large collection of operators is available and triplets can be combined to logical search trees using “and” or “or” operators. Searches can be extended over relationships between data models, thus enabling a user to query the warehouse for exactly the required data. Authorization is based on user roles and can be configured differently for each single data model read or write function. The generated graphical interface allows for the integration of interactive scientific plots and analysis tools written in JavaScript or WebAssembly.

+ +

An example data warehouse is publicly available and offers full read access in the graphical user interface and through the GraphQL API. The example warehouse is populated with public CassavaBase data50 to create fully BrAPI-compliant example based on Zendro. Three interactive scientific example plots are available to explore the data. The first is a boxplot comparing Cassava harvest indices measured for four different experiments. Next, an interactive raincloud plot provides an alternative visualization of the same data. Finally, a scatterplot shows how Cassava fresh root yield and plant height are correlated based on data from a single study.

+

Discussion

+ +

BrAPI for Breeders and Scientists

+

The BrAPI technical specification document is meant to be read and used by software developers. However, the purpose of the specification, and the community around it, is to make things faster, easier, and cheaper for the breeders and scientists working on breeding, genetics, phenomics and other agricultural projects. BrAPI offers a convenient path to automation, interoperability, and data integration for software tools in those domains. All of the software described above could be made interoperable with manual effort, moving and editing data files by hand from tool to tool. However, when the basic structure and flow of data becomes automated, breeders and scientists can spend less time on data management and more time focusing on the science. For many, the ultimate goal is the development of a digital ecosystem: a collection of software tools and applications that can all work together seamlessly. In this ideal digital ecosystem, data is collected digitally from the beginning, reducing as much human error as possible. The data is checked by quality control and stored automatically, then it can be sent to any internal tool or external lab for further analysis with just the click of a button. This idea might sound too good to be true, but as more tools start sharing a universal data standard, automating data flow becomes easier, and the community gets closer to total interoperability.

+

Looking Ahead

+

The BrAPI specification will continue a controlled and necessary growth. It will enable more use cases and new types of data that might include newer scientific techniques and technologies. There will be careful evaluation of the relevance of using REST web service approaches, and in particular BrAPI, for things like drone imaging data, spectroscopy, LIDAR, metabolomics, transcriptomics, high-throughput phenotyping, pan genomes, and machine learning analysis. All of these technologies can open new avenues for research and development of new crop varieties. However, they will also generate more data, more types of data, and require data sharing between different software applications and data repositories. The BrAPI project leadership and community is committed to building the standards to support these new use cases as they arrive and become accepted by the scientific community. In fact, small groups within the BrAPI community have already start building generic data models and proposed communication standards for many of the technologies listed above. These community efforts will eventually become part of the BrAPI standard in a future version of the specification document.

+

Expanding the BrAPI specification is important for the community, however it is just as important not to reinvent or compete with existing functional standards. Additions to the BrAPI specification are reviewed thoroughly by the community to make sure BrAPI is compliant with existing standards and data structures. For example, the community has requested compliance with the GFF3 standard for genomic data and the GeoTIFF standard for aerial image data. Pieces of these existing popular data structures might be integrated into the overall BrAPI standard documentation. In some cases, BrAPI will only reference other standards instead of including them in the specification. For example, there have been community discussions around developing connections with the NOAA CDO standard for weather data, or the Galaxy Analytics API for analytics pipeline controls and information. These standards are perfectly adequate on their own, recreating them in the BrAPI standard would be redundant and outside the main scope of the project.

+

Conclusion

+

The BrAPI project only exists because of the community of software engineers, biologists, and other scientists who support and use it. While there were many tools and use cases presented here, it is not an exhaustive list of all BrAPI-compliant systems. As long as the standard continues to be supported, there is potential for exponential growth of the community. As more groups make their tools BrAPI compliant, these tools can be shared with the community. As more BrAPI-compliant tools are shared with the community, more groups can see the value in implementing BrAPI in their own tools. This feedback loop will allow the community to strengthen and grow.

+

If this manuscript is your first introduction to the BrAPI project, the authors invite you to join the community. More information is always available at brapi.org.

+

Methods

+

The BrAPI Project day-to-day operations are run by the BrAPI Project Coordinator, overseen by the grant PI and Co-PI. The BrAPI Project Coordinator is responsible for the general maintenance of the project infrastructure, as well as coordinating updates to the standard. They organize community events and encourage collaboration between community groups. Long term planning and organization is handled by the BrAPI Advisory board. This is a board of 6 community members who are elected by the community to represent their interest in the project.

+

The standard documentation is stored and maintained in a GitHub repository. The core documentation is written using the OpenAPI 3 documentation standard, and can be easily transformed into other documentation standards as needed. Copies of the core documentation is published on the API documentation sites Apiary and SwaggerHub for maximum accessability by the community. All these online locations are public, and the specification has a standard MIT open source license.

+

New versions of the BrAPI standard are developed periodically based on community demand. Problems and enhancements are collected by the community over time, tracked in GitHub issues, then implemented together to create a new stable version. This ensures the standard stays up-to-date and relevant to the community, but maintains a certain level of stability over time. Minor version updates are designed to be backward compatible to further enhance the stability of the standard.

+

As discussed above, hackathon events are the primary way the BrAPI community stays together. One in-person and one virtual event per year maintain project momentum and social comradery. The hackathons provide a dedicated time to discuss issues and enhancements with the specification, particular use cases, and generally how the community is run and organized.

+

Data Availability Statement

+

No data was generated from this project.

+

Code Availability Statement

+

The all of software and documentation generated by the BrAPI project is free, public, and open source. Everything is available under the MIT open source license, and can be accessed through brapi.org. Most of the other software projects referenced and discussed here are also open source and the text contain links to their repositories or project pages. However, the authors take no responsibility for their availability or licensing.

+

Competing Interests Statement

+

The authors declare no competing interests.

+

Acknowledgements

+

The authors would like to acknowledge the following funding sources:

+
    +
  • The BrAPI Project is funded by the USDA grant NIFA-DSFAS 2022-67021-37024.
  • +
  • Innovation Lab for Crop Improvement Cornell, ILCI: 7200AA19LE00005, FFAR: CA20-SS-0000000103
  • +
  • The AGENT project is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 862613.
  • +
  • USDA National Institute of Food and Agriculture National Research Support Project 10
  • +
  • USDA NIFA: 2022-51181-38449
  • +
  • This work was supported by the Agence Nationale de la Recherche, programme Investissements d’avenir, ANR-11-INBS-0012 (Phenome)
    +
  • +
  • U.S. Department of Agriculture, under agreement numbers [8062-21000-043-004-A, 8062-21000-052-002-A, and 8062-21000-052-003-A]
  • +
  • The Bill and Malinda Gates Foundation in cooperation with the Excellence in Breeding Platform of the CGIAR
  • +
  • This work was funded by ELIXIR, the research infrastructure for life science data.
  • +
  • This work was supported by ELIXIR, the research infrastructure for life science data.
  • +
  • The French Networks of Biological Resource Centres for Agricultural, Environmental and Life Sciences, doi: 10.15454/b4ec-tf49
  • +
+

Author Contributions

+
    +
  • Peter Selby: Peter wrote and edited a significant portion of the manuscript. Peter has been the BrAPI Project Coordinator since 2017, managing the day-to-day operations of the BrAPI project.
  • +
  • Rafael Abbeloos: Rafael wrote the section on the PIPPA project, and has made significant contributions to the BrAPI compatibility of the PIPPA project.
  • +
  • Anne-Francoise Adam-Blondon: Anne-Francoise has made significant contributions to the Florilège, FAIDARE and AGENT projects.
  • +
  • Francisco J. Agosto-Pérez: Francisco wrote the section on the ImageBreed project, and has made significant contributions to the BrAPI compatibility of the ImageBreed project.
  • +
  • Michael Alaux: Michael has made significant contributions to the AGENT project.
  • +
  • Isabelle Alic: Isabelle wrote the section on the PHIS project, and has made significant contributions to the BrAPI compatibility of the PHIS project.
  • +
  • Khaled Al-Shamaa: Khaled wrote the section on the QBMS project, and has made significant contributions to the BrAPI compatibility of the QBMS project.
  • +
  • Johan Steven Aparicio: Johan wrote the section on the Mr.Bean project, and has made significant contributions to the BrAPI compatibility of the Mr.Bean project.
  • +
  • Jan Erik Backlund: Jan Erik has made significant contributions to the BMS project.
  • +
  • Aldrin Batac: Aldrin has made significant contributions to the BMS project.
  • +
  • Sebastian Beier: Sebastian wrote the section on the DivBrowse project, and has made significant contributions to the BrAPI compatibility of the DivBrowse project.
  • +
  • Gabriel Besombes: Gabriel has made significant contributions to the PHIS project.
  • +
  • Alice Boizet: Alice wrote the section on the Florilège projects, and has made significant contributions to the BrAPI compatibility of the Florilège, Gigwa and ShinyBrAPPs projects.
  • +
  • Matthijs Brouwer: Matthijs has made significant contributions to the AGENT project.
  • +
  • Terry Casstevens: Terry has made significant contributions to the PHG project.
  • +
  • Arnaud Charleroy: Arnaud has made significant contributions to the PHIS project.
  • +
  • Keo Corak: Keo wrote the section on the SCT project, and has made significant contributions to the BrAPI compatibility of the SCT project.
  • +
  • Chaney Courtney: Chaney wrote the section on the Field Book project, and has made significant contributions to the BrAPI compatibility of the Field Book project.
  • +
  • Mariano Crimi: Mariano has made significant contributions to the BMS project.
  • +
  • Gouripriya Davuluri: Gouripriya has made significant contributions to the AGENT project.
  • +
  • Kauê de Sousa: Kauê wrote the section on the ClimMob project, and has made significant contributions to the BrAPI compatibility of the ClimMob project.
  • +
  • Jeremy Destin: Jeremy has made significant contributions to the FAIDARE project.
  • +
  • Stijn Dhondt: Stijn has made significant contributions to the PIPPA project.
  • +
  • Ajay Dhungana: Ajay wrote the section introductions for the Phenotyping, Genotyping, and Germplasm topics.
  • +
  • Bert Droesbeke: Bert wrote the section on the BrAPI2ISA project, and has made significant contributions to the BrAPI compatibility of the BrAPI2ISA project.
  • +
  • Manuel Feser: Manuel wrote the section on the MIRA project, and has made significant contributions to the BrAPI compatibility of the MIRA project.
  • +
  • Mirella Flores-Gonzalez: Mirella wrote the section on the Trait Selector BrAPP projects, and has made significant contributions to the BrAPI compatibility of the Trait Selector BrAPP and Breedbase projects.
  • +
  • Valentin Guignon: Valentin wrote the section on the BrAPI Mapper and MGIS projects, and has made significant contributions to the BrAPI compatibility of the BrAPI Mapper, MGIS, and the Trait Selector BrAPP projects.
  • +
  • Corina Habito: Corina wrote the section on the BMS project, and has made significant contributions to the BrAPI compatibility of the BMS project.
  • +
  • Asis Hallab: Asis wrote the section on the Zendro project, and has made significant contributions to the BrAPI compatibility of the Zendro project.
  • +
  • Jenna Hershberger: Jenna did a significant amount of proofreading and editing to most sections of the manuscript.
  • +
  • Puthick Hok: Puthick has made significant contributions to the DArT Sample Submission project.
  • +
  • Lynn Carol Johnson: Lynn wrote the section on the PHG project, and has made significant contributions to the BrAPI compatibility of the PHG project.
  • +
  • Sook Jung: Sook wrote the section on the BIMS project, and has made significant contributions to the BrAPI compatibility of the BIMS project.
  • +
  • Paul Kersey: Paul has made significant contributions to the AGENT project.
  • +
  • Andrzej Kilian: Andrzej has made significant contributions to the DArT Sample Submission and DArTView projects.
  • +
  • Patrick König: Patrick has made significant contributions to the AGENT project.
  • +
  • Suman Kumar: Suman has made significant contributions to the AGENT project.
  • +
  • Josh Lamos-Sweeney: Josh wrote the section on the G-Crunch project, and has made significant contributions to the BrAPI compatibility of the G-Crunch project.
  • +
  • Laszlo Lang: Laszlo has made significant contributions to the Zendro project.
  • +
  • Matthias Lange: Matthias wrote the section on the AGENT project, and has made significant contributions to the BrAPI compatibility of the AGENT project.
  • +
  • Marie-Angélique Laporte: Marie-Angélique wrote the section on the ClimMob project, and has made significant contributions to the BrAPI compatibility of the ClimMob project.
  • +
  • Taein Lee: Taein has made significant contributions to the BIMS project.
  • +
  • Erwan Le-Floch: Erwan has made significant contributions to the AGENT project.
  • +
  • Francisco López: Francisco has made significant contributions to the GLIS project.
  • +
  • Brandon Madriz: Brandon wrote the section on the ClimMob project, and has made significant contributions to the BrAPI compatibility of the ClimMob project.
  • +
  • Dorrie Main: Dorrie has made significant contributions to the BIMS project.
  • +
  • Marco Marsella: Marco wrote the section on the GLIS project, and has made significant contributions to the BrAPI compatibility of the GLIS project.
  • +
  • Maud Marty: Maud has made significant contributions to the FAIDARE project.
  • +
  • Célia Michotey: Célia has made significant contributions to the Florilège and FAIDARE projects.
  • +
  • Zachary Miller: Zachary has made significant contributions to the PHG project.
  • +
  • Iain Milne: Iain has made significant contributions to the Flapjack, Gridscore, and Germinate projects.
  • +
  • Lukas A. Mueller: Lukas wrote the section on the Breedbase project, and has made significant contributions to the BrAPI compatibility of the Breedbase project. Lukas is also Co-PI on the current BrAPI Grant and has supported the BrAPI project since its inception.
  • +
  • Moses Nderitu: Moses wrote the section on the DArTView project, and has made significant contributions to the BrAPI compatibility of the DArTView project.
  • +
  • Pascal Neveu: Pascal has made significant contributions to the PHIS project.
  • +
  • Nick Palladino: Nick has made significant contributions to the DeltaBreed project.
  • +
  • Tim Parsons: Tim has made significant contributions to the DeltaBreed project.
  • +
  • Cyril Pommier: Cyril wrote the section on the FAIDARE projects, and has made significant contributions to the BrAPI compatibility of the Florilège, FAIDARE, and AGENT projects. Cyril has also made significant contributions to the Germplasm and Phenotyping modules of the BrAPI specification.
  • +
  • Jean-François Rami: Jean-François wrote the section on the ShinyBrAPPs project, and has made significant contributions to the BrAPI compatibility of the ShinyBrAPPs project.
  • +
  • Sebastian Raubach: Sebastian wrote the section on the GridScore and Germinate projects, and has made significant contributions to the BrAPI compatibility of the GridScore, Germinate, and Flapjack projects.
  • +
  • Trevor Rife: Trevor wrote the section on the Field Book project, and has made significant contributions to the BrAPI compatibility of the Field Book project.
  • +
  • Kelly Robbins: Kelly is the PI for the BrAPI Project grant, and has supported all technical and scientific aspects of the project.
  • +
  • Mathieu Rouard: Mathieu wrote the section on the MGIS and GIGWA projects, and has made significant contributions to the BrAPI compatibility of the MGIS and GIGWA projects.
  • +
  • Joseph Ruff: Joseph has made significant contributions to the AGENT project.
  • +
  • Guilhem Sempéré: Guilhem wrote the section on the Gigwa project, and has made significant contributions to the BrAPI compatibility of the Gigwa project.
  • +
  • Romil Mayank Shah: Romil has made significant contributions to the SCT project.
  • +
  • Paul Shaw: Paul wrote the section on the Helium project, and has made significant contributions to the BrAPI compatibility of the Helium project.
  • +
  • Becky Smith: Becky has made significant contributions to the Flapjack project.
  • +
  • Nahuel Soldevilla: Nahuel has made significant contributions to the BMS project.
  • +
  • Anne Tireau: Anne has made significant contributions to the PHIS project.
  • +
  • Clarysabel Tovar: Clarysabel has made significant contributions to the BMS project.
  • +
  • Grzegorz Uszynski: Grzegorz wrote the section on the DArT Sample Submission project, and has made significant contributions to the BrAPI compatibility of the DArT Sample Submission project.
  • +
  • Vivian Bass Vega: Vivian has made significant contributions to the Zendro project.
  • +
  • Stephan Weise: Stephan has made significant contributions to the AGENT project.
  • +
  • Shawn C. Yarnes: Shawn wrote the section on the DeltaBreed project, and has made significant contributions to the BrAPI compatibility of the DeltaBreed project.
  • +
+

References

+ +
+
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