Isabelle Alic
+
+
+0000-0002-8961-6068
+·
+Isabelle-inrae
+
+
+INRAE
+· Funded by {‘Short version’: ‘This work was supported by the project Phenome-ANR-11-INBS-0012’}; {‘OR Long version’: “This work was supported by the Agence Nationale de la Recherche, programme Investissements d’avenir, ANR-11-INBS-0012 (Phenome)”}
+
Cyril Pommier
+
+
+0000-0002-9040-8733
+·
+cpommier
+
+
+Université Paris-Saclay, INRAE, Bioinformatics, Plant Bioinformatics Facility, Versailles, France; Université Paris-Saclay, INRAE, URGI, Versailles, France
+· Funded by {‘Short version’: ‘This work was supported by the project Phenome-ANR-11-INBS-0012’}; {‘OR Long version’: “This work was supported by the Agence Nationale de la Recherche, programme Investissements d’avenir, ANR-11-INBS-0012 (Phenome)”}
+
Sook Jung
+
+
+0000-0003-3968-2769
+
+
+Department of Horticulture, Washington State University
+
+
Taein Lee
+
+
+0000-0002-2000-2586
+·
+leetaei
+
+
+Department of Horticulture, Washington State University
+
+
Josh Lamos-Sweeney
+
+·
+jlamossweeney
+
+
+Cornell University
+
+
Francisco López
+
+
+International Treaty on Plant Genetic Resources for Food and Agriculture, FAO
+
+
Marco Marsella
+
+
+0000-0003-0334-8785
+
+
+International Treaty on Plant Genetic Resources for Food and Agriculture, FAO
+
+
Matthias Lange
+
+
+0000-0002-4316-078X
+·
+langeipk
+
+
+Leibniz Institute of Plant Genetics and Crop Plant Research
+
+
Guilhem Sempéré
+
+
+0000-0001-7429-2091
+·
+GuilhemSempere
+
+
+CIRAD (french agricultural research and international cooperation organization); South Green Platform
+
+
Stephan Weise
+
+
+0000-0003-4031-9131
+
+
+Leibniz Institute of Plant Genetics and Crop Plant Research
+
Manuel Feser
+
+
+0000-0001-6546-1818
+·
+feserm
+
+
+Leibniz Institute of Plant Genetics and Crop Plant Research; Graduate School DILS, Bielefeld Institute for Bioinformatics Infrastructure (BIBI)
+
+
Gouripriya Davuluri
+
+
+Leibniz Institute of Plant Genetics and Crop Plant Research
+
Clarysabel Tovar
+
+
+Integrated Breeding Platform; Leafnode LLC
+
+
Sebastian Beier
+
+
+0000-0002-2177-8781
+·
+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
+
+
Valentin Guignon
+
+
+0000-0003-0903-6811
+·
+guignonv
+
+
+Bioversity International, Parc Scientifique Agropolis II, 34397 Montpellier, France
+
+
Mathieu Rouard
+
+
+0000-0003-0284-1885
+·
+mrouard
+
+
+Bioversity International, Parc Scientifique Agropolis II, 34397 Montpellier, France
+
+
Asis Hallab
+
+
+0000-0002-2421-5431
+·
+https://github.com/asishallab
+
+
+Jülich research center, Institute of Bio- and Geosciences (IBG), Bioinformatics (IBG-4) and Bingen Technical University of Applied Sciences, Germany
+
Andrzej Kilian
+
+
+Diversity Arrays Technology (DArT)
+
+
+
+
✉ — 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, genomics technologies and genomic prediction approaches have provided a boost in genetic gain in breeding, but has also created a flood of data that needs careful management to be fully harnessed.
+The Breeding API (BrAPI) project is an international, grass-roots effort to enable more efficient data management by enabling interoperability among plant breeding databases and tools, using a standardized RESTful web service API specification for communicating plant breeding data.
+This community driven standard is free to be used by anyone interested in plant breeding data management, including trial data, phenotypic data and genotyping data management.
+This manuscript describes advances in implementations of BrAPI in different breeding tools, and outlook for the current version of BrAPI.
+
Introduction
+
+
To address consequences of climate change and population growth, plant and animal breeding need to become more efficient and data driven to produce bigger, better, healthier, more sustainable crops. Modern breeding techniques require large amounts of high quality data to be effective, requiring digital methods for data collection, management, and analysis. Interoperability between breeding 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 to build more accurate computational models and produce more accurate predictions and improved selections.
+
Plant and animal breeding is an incredibly important part of today’s society. Almost every country in the world has some kind of breeding program supporting the agricultural community to produce bigger, better, healthier, and more sustainable crops. Climate change, population growth, disease mitigation, and nutritional health are all major challenges facing humanity, and the ability to breed better crops is a critical part of the solution. Modern breeding techniques require large amounts of high quality data to be effective. In the digital age, that breeding data is being collected, managed, and analyzed with computer software. Interoperability between breeding software tools, systems, and databases can substantially increase the efficiency of a breeding program. The ability to share tools gives each program a boost in computational power. The ability to share data allows the community to leverage access to larger, more complete, datasets; similarly, the community can build more accurate computational models and produce more accurate predictions.
+
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 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.
+
This manuscript will present BrAPI in its latest iteration. This includes a 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 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 Application Programming Interface (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 Representational State Transfer (REST) web service is a type of API commonly used in today’s modern web infrastructure. REST is a technical architecture that describes the stateless transmission of data between applications. Typically, REST systems are implemented using the standard HTTP protocol that most of the modern internet is built upon. REST implementations also 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 the 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 the existing database. Client application developers can take advantage of this standardization by building tools that can easily integrate with all other 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.
+
Project Updates
+
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 original 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 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.
+
+
+
+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.
+
+
+
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 version of the model to be used in some automated development 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, the three new resources list pages on brapi.org advertise the other BrAPI compatible software available in the community. The BrAPPs list displays the 10 standalone, plug-and-play, applications available to the community. The servers list displays the 27 registered public data servers, their current status, and a form for registering additional servers. The compatible software list shows the 31 software applications that are BrAPI compliant, and again, a form for registering additional applications. The process for registering new items for these lists is completely voluntary, so the totals represent a lower bound for the number of BrAPPs, data repositories, and applications available in the the community.
+
Community Growth
+
The international BrAPI Community consists of software developers, breeders, and related scientists working on BrAPI related projects and data sources. This community is what sustains the BrAPI project, building implementations, maintaining development tools, and providing 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 nine years, the community has grown to between 200 and 250 members. The community mailing list has 208 members, and the BrAPI Slack workspace has 234 members. The project leadership uses the mailing list to broadcast newsletters, announcements, and updates to the community. The BrAPI Slack workspace allows members of the community to discuss specific topics and collaborate directly with each other.
+
The BrAPI Hackathons are a major staple of the BrAPI community. 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 have time to 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. The typical virtual hackathon has about twice as many registered attendees compared to an in-person hackathon. However, attendees have reported much more productive work time during the in-person events. As a compromise, 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.
+
As the project has matured, a formal project leadership structure became increasingly important. The project governance is divided into two groups. The Project Management team is responsible for the day to day operations of the project. The PI and Co-PI are responsible for the project funds, and responsible for hiring the BrAPI Project Coordinator who is paid by the project funds. The Advisory Board is a group of elected officials representing the community. The board is responsible for long term planning of the project, as well as quick decision making on behalf of the community. The two groups meet quarterly to report on progress and stay synchronized.
+
Success Stories
+
+
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.
+
Phenotyping
+
Phenotyping is the basis for all breeding efforts. All the downstream analysis and decision making procedures rely on a foundation of accurate, high-quality, phenotypic data. The BrAPI specification supports phenotypic data as it passes through the entire breeding pipeline, as it is published, and as it is archived. The community has developed tools that use BrAPI during the data collection process, to get the data into a standard format as soon as possible. There are BrAPI compatible systems to store and curate phenotypic data, and tools to manage trait meta data. The community has also begun developing BrAPI compatible tools for managing images and other high throughput phenotyping techniques.
+
Field Book
+
+
Phenotypic data collection is an essential part of the breeding process. Historically, gathering data in the field was done with pen and paper, or perhaps some version of a digital spreadsheet. The abundance and prevalence of smart phones has allowed the Field Book mobile app [2] to enhance data collection. Field Book can create well-formed digital observation records from the moment they are taken. This can improve the efficiency of data collection and reduce human error.
+
In 2018, BrAPI was introduced into Field Book; specifically, the Core and Phenotyping modules. BrAPI was able to take things a step further by automating the flow of data from the Field Book mobile app to a central database server. This workflow allows data collection and storage to be expedited, removing the need of the user to transfer export files manually. Since Field Book’s adoption of BrAPI, many community servers have been integrated to simplify data storage. In this work flow, data is collected and stored completely digitally with little-to-no human involvement.
+
GridScore
+
+
+
+
Phenotypic data collection underpins scientific crop research and plant breeding. Knowledge gained from collected data and its analysis alongside data visualizations inform further phenotypic trials and ideally support research hypotheses. The importance of accuracy and efficiency in the collection of this data as well as the infrastructure to facilitate the flow of data from the field to a knowledge base cannot be underestimated. GridScore[3] is a modern mobile application for phenotypic observations that harnesses technological advancements in the area of mobile devices to enrich the data collection process.
+
BrAPI has further increased the value of GridScore by integrating it into the overarching workflow from trial creation, data collection, and its ultimate data storage for further processing. Specifically, trial designs as well as trait definitions can be imported into GridScore using BrAPI and a finalized trial can ultimately be exported via BrAPI to any compatible database.
+
ClimMob
+
ClimMob [4] is a software suite that turns the research paradigm in experimental agriculture. The platform supports experiment design, data collection through mobile apps, and data analysis to provide actionable insights. Instead of a few researchers designing complicated trials to compare several agricultural technologies in search of the best solutions for the target environment, ClimMob enables many participants to carry out reasonably simple experiments that taken together can offer even more information. It applies the priciples of citizen science and choice experiments to scale the data collection process, mostly in the format of rankings. Although this data may not be as detailed as from a centralized experiment, it can be very useful to inform decisions to a wide range of locations and environments with increased external validity. ClimMob applications include testing crop varieties, evaluating agronomic practices, and investigating climate resilience strategies.
+
During a crop varieties trial, all farmer-collected data is stored in ClimMob. Upon data collectection completion, the raw data is automatically uploaded via BrAPI to a central breeding database for long-term storage. To facilitate this synchronization, ClimMob uses BrAPI to retrieve curated germplasm information from breeding databases when designing a trial, significantly enhancing data quality. Additionally, a process has been developed to push analyzed data from ClimMob to the breeding databases, providing breeders with insights into the potential adoption of the tested crop varieties.
+
ImageBreed
+
High-throughput phenotyping has been gaining significant traction lately as a way to collect lots of data very quickly. Image collection from unmanned aerial and ground vehicles (UAVs and UGVs) are a great way to collect a lot of raw data all at once, then analyze it later. ImageBreed [5] is an image collection pipeline tool to support regular use of UAVs and UGVs.
+
When the raw images have been processed through the standardization pipelines in ImageBreed, useful phenotypes can be extracted from the images. The BrAPI standard is used to push these phenotypes back to a central breeding database where they can be analyzed with other data. In addition to this, ImageBreed has the ability to use BrAPI to upload the raw images to the central breeding database, or any other BrAPI compatible long term storage service. In the current version of the standard (V2.1), the BrAPI data models for images are rudimentary, but effective. The ImageBreed team has put in some work into enhancing the BrAPI image data standards, collaborating with others in the community.
+
PHIS
+
+
The Hybrid Phenotyping Information System (PHIS[6]), based on the OpenSILEX framework, is an ontology-driven information system based on semantic web technologies. PHIS is deployed in several field and greenhouse platforms of the French national PHENOME and European EMPHASIS infrastructures. It manages and collects data from Phenotyping and High Throughput Phenotyping experiments on a day to day basis. PHIS unambiguously identifies all the objects and traits in an experiment, and establishes their types and relationships via ontologies and semantics.
+
PHIS has been designed to be BrAPI-compliant. PHIS adheres to the standards and protocols specified by BrAPI and implements various services aligning with the BrAPI standards, encompassing the Core, Phenotyping, and Germplasm modules. This enables integration and compatibility with BrAPI-compliant systems and platforms. This prerequisite served as the basis for formalizing the data model, while also facilitating compatibility with other standards, such as the Minimal Information About a Plant Phenotyping Experiment (MIAPPE[7]). 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. The fact that data within a PHIS instance can be queried through BrAPI services makes the indexing of PHIS in FAIDARE very easy to implement.
+
Furthermore, as PHIS offers BrAPI-compliant Web Services, it simplifies the integration and data exchange with other European information systems that handle phenotyping data. The adherence to BrAPI standards ensures a common interface and compatibility, facilitating communication and collaboration between PHIS and other systems in the European context. This interoperability not only eases data sharing, but also 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.
+
PIPPA
+
+
PIPPA is a data management system used for collecting data from the WIWAM range of automated high throughput phenotyping platforms. These platforms have been deployed at different research institutes and commercial breeders across Europe. They can be setup 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 to pots (genotype, growth media, manual treatments), importing data, exporting data, and visualizing data. It also supports the integration of image analysis scripts and connections to a compute cluster for job submission.
+
To share the phenotype data of the experiments linked to publications, an implementation of BrAPI v1.3 was developed on a separate public PIPPA server, which allowed read only access to the data in the BrAPI standardized format. This server was registered on FAIDARE which allows the data to be found alongside data from other BrAPI compatible repositories.
+
As the BrAPI ecosystem has matured, it has created a clear path for the further development of PIPPA. THe BrAPI specification demonstrates how to share data in a manner consistent with the FAIR principles, [8] which are becoming best practices in plant research data management. The BrAPI technical standard, in combination with the MIAPPE[7] scientific standard, have served as guidelines in the current development effort of the PIPPA project. This development is focused on delivering a public BrAPI v2.1 endpoint and making more high throughput datasets publicly available via BrAPI.
+
Trait Selector BrAPP
+
+
+
BrAPPs are simple tools developed by the BrAPI community that are entirely reliant on BrAPI for their data requirements. Often, they are JavaScript based applications or visualizations that fit on a single web page. This means a single BrAPP can be easily shared and used by many organizations and systems, as long as those organizations have the standard BrAPI endpoints available.
+
The Trait Selector BrAPP is used to search and select useful traits, using a visual aid to help the user find exactly what they need. Instead of searching through a long list of possible traits, the user is presented with a cartoon image of a species. They can then click on pieces of the image to show traits associated to that part of the plant. For a breeder, they might use it to quickly find specific traits to study. For a genebank user, they might use it to find varieties that have a specific trait they are interested in.
+
Due to the nature of BrAPPs, the Trait Selector can be integrated into any website or system, assuming there is a BrAPI compatible data source available to connect to. A breeding database would need to only implement the BrAPI endpoints for Traits, Observations, and Variables, while a genebank would require Traits and Germplasm Attributes. Any BrAPI server with either of these sets of endpoints implemented could use this BrAPP. CassavaBase and MGIS are two successful examples of the Trait Selector BrAPP in use.
+
+
Genotyping
+
Genotyping has become a cornerstone of most breeding processes, but the data can be difficult to manage. BrAPI supports genotypic data, relying on some existing standards including VCF and the GA4GH Variants standards. The BrAPI Community has built BrAPI compatible tools for storing, searching, visualizing, and analyzing genotypic data.
+
Flapjack
+
Flapjack[9] is a multi-platform desktop application for data visualization and breeding analysis (eg, pedigree verification, marker-assisted backcrossing and forward breeding) using high-throughput genotype data. Data can be easily 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, which 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 and work is underway to determine the best methods to exchange analysis results using future versions of the API.
+
DArTView
+
+
DArTView is a desktop application for visualizing genotype variant data and looking for trends or correlations. It is newly BrAPI compatible and can use BrAPI as an input data source.
+
DivBrowse
+
+
DivBrowse [10] is a web platform for exploratory data analysis of huge genotyping studies. The software can be run standalone or integrated as a plugin into existing data web portals. It provides a powerful interactive visualization of variant call matrices with hundreds of millions of variants and thousands of samples. It enables easy data import and export by using well established, standardized, bioinformatics file formats.
+
At its core, DivBrowse combines the convenience of a genome browser with features tailored to the diversity analysis of germplasm. It is able to display genomic features such as nucleotide sequence, associated gene models, and short genomic variants. DivBrowse provides visual access to large VCF files obtained through genotyping experiments. In addition to visualizing variant calls per variant and genotype, DivBrowse also calculates and displays variant statistics such as minor allele frequencies, proportion of heterozygous calls, and proportion missing variant calls. Dynamic Principal Component Analyses (PCA) can be performed on a user specified genomic area to provide information on local genomic diversity.
+
Parts of the BrAPI Genotyping module are implemented in DivBrowse. There is a server-side component which provides genotypic data that the DivBrowse database can consume. There is also a client-side GUI component which can visualize genotypic data via any external BrAPI endpoint. In addition to BrAPI, DivBrowse has an internal API to control the tool from a hosting web portal (e.g. to control the list of genotypes to be displayed and the reference genome). 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 external information systems.
+
GIGWA
+
+
Gigwa is a Java EE web application providing a means to centralize, share, finely filter, and visualize high-throughput genotyping data [11]. 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 centers [12]. The total amount of data hosted and made widely accessible using this system has continued to grow over the last few years.
+
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 tool [9]. 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).
+
Additional use-cases required Gigwa to also consume data from other BrAPI servers. This led to the implementation of BrAPI client features within Gigwa. A close collaboration was established with the Integrated Breeding Platform team developing the 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.
+
Community members typically write adhoc scripts federating data from multiple BrAPI sources using BrAPI client libraries available for R, python, and other programming languages. For instance, phenotypes from one data source and genotypes from another in order to run various kinds of analyses such as GWAS, genomic selection or phylogenetic investigations. The most generic and widely-used of those pipelines are at least publicly distributed, and possibly web-interfaced using solutions like R-Shiny. This provides new, excitingly useful, online services, based on Gigwa-hosted data.
+
PHG
+
+
The Practical Haplotype Graph (PHG) is a graph-based computational framework that represents large-scale genetic variation and is optimized for plant breeding and genetics. 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.
+
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.
+
DArT Sample Submission
+
+
The DArT genotyping lab is heavily used world wide when it comes to plant genotyping. With over 1200 available organisms and species, client base on every continent and already many million 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. Whole system is tightly integrated with DArTdb - DArT’s custom LIMS oprational system, which is driving laboratory, quality and analytical processes.
+
Diversity Arrays Technology was 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 of them 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 en external lab and also allows for an automated process of the samples batch transfers. Beyond sample submission, current implementation allows for an order status verification, automated data discovery and downloads. Data are delivered as standard data packages with self-describing metadata.
+
Current BrAPI implementation at DArt has a production status and it is compatible with the newest BrAPI specification. Further details about DArT’s ordering system can be found at DArTOrderingSystem and also at DArTHelp
+
Germplasm Management
+
The vast quantity of new accessions, variants, and lines being created each year means germplasm data management is critical to keeping things organized. This is true at the level of a single breeding program, on a national scale, and on the international scale. BrAPI supports the transmission of germplasm passport data, as well as pedigree trees and crossing metadata. The BrAPI community has developed BrAPI compliant tools for storing, searching, and visualizing this metadata. There are even some plans in place to build federated networks of genebank data, connected via BrAPI.
+
MGIS
+
+
The Musa Germplasm Information System, MGIS, serves as a comprehensive community portal dedicated to banana diversity, a crop critical to global food security [13]. MGIS offers detailed information on banana germplasm, focusing on the collections held by the CGIAR International Banana Genebank (ITC) [14]. It is built on the Drupal/Tripal technology, like BIMS and Florilège.
+
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 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 BrAPI germplasm module implementation. For genotyping data, MGIS incorporates GIGWA [11], 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 Ontology [15]. MGIS is integrated with the Trait Selector BrAPP, and there are use cases implemented to interlink genebank and breeding data between MGIS and the breeding database MusaBase.
+
+
AGENT Portal
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In the global system for ex situ conservation of plant genetic resources (PGR) [16], a total of ~5.8 million accessions are conserved in 1750 ex situ genebanks [17]. Unique and permanent identifiers in the form of DOIs are available for more than 1.7 million accessions [18]. Each DOI is linked to some basic descriptive data that facilitates the use of these resources. Many DOIs are also linked to additional data from different domains or will be in the future. However, a data space beyond the most basic information is needed that includes genotypic and phenotypic data. This space will help answer questions on the global biological diversity of a plant species, on duplicate detection, on provenance tracking for the identification of genetic integrity, on the selection of the most suitable material for various purposes, and to support further applications in data mining or AI. In this context, the aim of the AGENT project (https://www.agent-project.eu/), funded by the European Commission, is to develop a concept for the digital exploitation and activation of this PGR data space, [8] and to test it in practice using two important crops, barley and wheat. In two work packages, standards and technology for data interoperability will be developed to establish a genetic resources infrastructure, which regulates data acquisition of genotypic and phenotypic data, integrates and archives them and makes them accessible according to FAIR principles. To this end, 13 European genebanks and 5 bioinformatics centers are cooperating and have agreed on standards and protocols for the data flow and data formats [19] for central archiving of genotypic and phenotypic data.
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The BrAPI specification is one of the agreed standards. The implemented BrAPI interface enables AGENT to mine current and historic genotypic and phenotypic information. This will drive the discovery of genes, traits, and knowledge for future missions. It will also complement existing information for wheat and barley to foster an improved management of PGR data for other crop species across European genebanks. The AGENT database backend aggregates curated passport data, phenotypic data, and genotypic data about wheat and barley accessions of 18 project partners. This data is integrated via BrAPI endpoints (https://github.com/AGENTproject/BrAPI) and explorable in a web portal (https://agent.ipk-gatersleben.de). Genotyping data uses the DivBrowse [10] storage engine and its BrAPI interface. API endpoints for sample data are implemented using the AGENT database SQL to BrAPI broker service.
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To integrate those BrAPI endpoint provider into a single service and URL scheme, we work on their integration in a BrAPI proxy service. As next steps, we will expand BrAPI implementation to enable the integration of analysis pipelines in the AGENT portal, e.g. for genebank mining tools such as the FIGS+ pipeline developed by AGENT partner ICARDA [20]. Another perspective is to integrate the data collected in the AGENT project into the European Search Catalogue for Plant Genetic Resources (EURISCO) [21] and to implement BrAPI endpoints to make data on PGR collections in European genebanks programmatically accessible.
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Helium
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Helium (https://helium.hutton.ac.uk) [22] 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 exist [23]. The accurate representation of pedigrees, and the ability to pull pedigree data from different data sources is therefore 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 (https://github.com/cardinalb/helium-docs/wiki), Helium has developed into a web-based visualization platform implementing BrAPI calls to allow users to import data from other BrAPI compliant databases (https://helium.hutton.ac.uk). 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 data [24] it has also found utility in other non-plant projects [25] highlighting its broad utility. This also allows Helium users 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 Hutton to allow users to test the system and features it offers.
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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 data. 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 International Treaty and the Governing Body 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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FLORILÈGE (Gateway to French Plant Genetic Resources)
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Designed primarily for the general public, Florilège provides access to public collections of all French plant biological resources centers. This web portal allows a user to browse available plant genetic resource accessions and allows the user to order some seeds or plant material for cultivation. It includes plant genetic resources of around 50 plant genus from 19 genebanks.
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Florilège retrieves accession information from different BrAPI-compliant systems. They include OLGA, a genebank accessions information management system, and GnpIS, [26,27] an INRAE data repository for plant genetic resources, phenomics, and genetics. Using BrAPI to gather data from these systems reduced the efforts and enabled standardized data retrieval. As a consequence, BrAPI is the de facto standard for exchanging data within the French plant genetic resources community. The Florilège team also requested several updates to the BrAPI specifications to better serve this use case, such as Collection or improved external references. Florilège is developed in Drupal 10, and uses xnttbrapi module to easily connect to BrAPI compliant external databases.
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FAIDARE
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FAIDARE (https://urgi.versailles.inrae.fr/faidare/) is a data discovery portal providing a biologist friendly search system over a global federation of 33 plant research databases. It allows a user 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. For instance, it is possible to search for the text “wheat drought”, then to refine the search to the “Triticum aestivum” taxon and yield component traits such as “Thousand Grain Weight”. The indexed data types are very 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 most generic one provides basic search functionalities and relies on five fields : name, link back URL, data type, species, and exhaustive description. The filtering is directly tied to some of those fields. Therefore, to provide more advanced filtering, FAIDARE also provides a second stage indexation mechanism by taking advantage of BrAPI endpoints to get more detailed metadata on genotyping and phenotyping studies. In parallel, FAIDARE provides a pre-visualization of germplasm and studies using dedicated cards on the user interface.
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The indexation mechanism relies on a dedicated public software (https://github.com/elixir-europe/plant-brapi-etl-faidare) that allows data resource managers to request the indexation of their database using pull requests. This BrAPI client is able to extract data from any BrAPI 1.3 and 1.2 endpoint. The development of BrAPI 2.x indexation will be initiated in 2025. Since not all databases are willing to implement BrAPI endpoints, we also provide the possibility to generate metadata as BrAPI compliant json files, hence using the standard as a file exchange format.
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The FAIDARE architecture has been designed by elaborating on the GnpIS Software Architecture [26]. As a consequence, BrAPI is at the core of its data model, and in particular the JSON data files served by the Elasticsearch NoSQL engine are enriched versions of the BrAPI JSON data model. FAIDARE also includes a BrAPI endpoint that serves all indexed metadata. FAIDARE 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, allowing to increase both the portal and the BrAPI adoption.
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Breeding Data Management
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While specialty data management is important for some use cases, often breeders want a central repository of critical data. General breeding data management systems support some level 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 pros and cons.
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DeltaBreed
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DeltaBreed is an open-source data management system designed and developed by Breeding Insight to support USDA-ARS specialty crop and animal breeders. DeltaBreed is a unified system for managing breeding data 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 diverse 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 has not been fully implemented yet. Users can expect DeltaBreed observation handling to become more seamless with future development.
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Below is a list of BrAPI enabled applications that are integrated with DeltaBreed.
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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 Gigwa which is often deployed together with the BMS 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), by enabling the sharing of germplasm and trials across BrAPI-enabled systems. This tool 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 utilizing the External References field to maintain links to the origin IDs of each entity it transmits. This not only retains the original context of the data but also 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 system [28][29] 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 Fieldbook [2], 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 algorithms [30] and the accompanying data management challenges. Thus, complete genomic prediction workflow is integrated in the system. The first instance of Breedbase was created for the NextGen Cassava project in 2012 as the Cassavabase (https://cassavabase.org/) database. Databases for other CGIAR root, tuber and banana (RTB) crops followed, with database for yam (https://yambase.org/), sweet potato (https://sweetpotatobase.org/), and banana (https://musabase.org/), as well as instances for individual labs and companies.
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The BrAPI interface [1] is crucial for Breedbase. Breedbase communicates via BrAPI with the data collection tablets, connection to other projects such as CLIMMOB [4], and many native tools use the BrAPI interface for accessing data. Users also appreciate the ability to connect to Breedbase instances using packages such as QBMS (https://icarda-git.github.io/QBMS/) 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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BIMS
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BIMS (Breeding Information Management System) [31] is a free, secure, and 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, data analysis, and data archiving for their germplasm, phenotype, genotype, and image data. BIMS is currently implemented in five community databases, the Genome Database for Rosaceae [32], CottonGEN [33], the Citrus Genome Database, the Pulse Crop Database, and the Genome Database for Vaccinium. BIMS in these five community databases enables individual breeders to import publicly available data so that they can utilize public data in their breeding program.
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Right now, BIMS primarily utilizes BrAPI to connect with the Field Book Android App, enabling seamless data transfer between BIMS and the app. Data transfer through BrAPI between BIMS and other resources such as BreedBase, GIGWA, and Breeder Genomics Hub is on the way. Hopefully, the BIMS development team can easily reuse some of the solved use cases and workflows created by others in the BrAPI community.
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Germinate
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Germinate[34] is an open-source plant genetic resources database that combines and integrates various kinds of plant breeding data including genotypic data, phenotypic trials data, passport data, images, geographic information and climate data into a single repository. Germinate is tightly linked to the BrAPI specification and supports a majority of BrAPI endpoints for querying, filtering, and submission.
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Germinate integrates and 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. Thanks to the interoperability provided by BrAPI the need for manual data handling becomes a rarity with the direct benefit of faster data processing, fewer to no human errors, data security, and data integrity.
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Analytics
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While other tools listed above have the capability to do specialized analytics on certain types of data, general analytics tools can cover a wide range of data types and analytical models. 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.
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QBMS
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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.
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The QBMS[35] R package eliminates technical barriers scientists experience when using the BrAPI specification in their analysis scripts and pipelines. This barrier arises from the complexity of managing API backend processes, such as authentication, tokens, TCP/IP protocol, JSON format, pagination, stateless calls, asynchronous communication, database IDs, and more. To bridge this gap, the QBMS package abstracts the technical complexities, providing breeders with stateful functions familiar to them when navigating their GUI systems. It enables them to query and extract data into a standard data frame structure, consistent with their use of R language, one of the most common statistical tools in the breeding community.
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Since its release on the official CRAN repository in October 2021, the QBMS R package has garnered over 9400 downloads. Several tools, such as MrBean, rely on the QBMS package as their source data adapter. Moreover, the community has started building extended solutions on top of it. QBMS can serve as a cornerstone in the breeding modernization revolution by providing access to actionable data and by enabling the creation of dashboards to reduce the time between harvest and decision-making for the next breeding cycle.
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Mr.Bean
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Mr.Bean [36] 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.
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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.
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G-Crunch
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G-Crunch is an upcoming user-facing analysis tool that attempts to fill the space of simple, user driven analytics requests, with a generic user interface and the ability to swap out data sources and analysis tools. G-Crunch hopes to streamline repeatable, debuggable, simple analytic requests and results.
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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 (BrAPI-compliant) existing network of tools. This lowers the barrier to entry for adoption, and makes analysis pipelines easily repeatable.
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General Infrastructure
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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 here is always 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.
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MIAPPE ISA to BrAPI service
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Phenotyping is crucial in the breeding process as it enables the identification of desirable traits, selection of breeding lines, and evaluation of breeding success. In the plant community, MIAPPE (Minimal Information About a Plant Phenotyping Experiment) [37] is an established standard for phenotyping experiments. It is commonly serialized as a ISA Tab file type. [38] 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. BrAPI, which is aligned with MIAPPE, can help solve this problem.
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MIRA is a tool that enables the automatic deployment of a BrAPI server on a MIAPPE-compliant dataset in ISA Tab format. It can be deployed from a Docker image with the dataset mounted. By utilizing the mapping between MIAPPE, ISA, and BrAPI, there is no need for parsing or manual mapping of datasets that are already compliant with (meta-)data standards. By gaining programmatic access through BrAPI to these datasets, it facilitates the integration of phenotyping datasets into web applications.
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MIAPPE “BrAPI to ISA” service
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Since the release of BrAPI 1.3, efforts have been made to incorporate support for the MIAPPE standard into the specification [37]. This integration was finalized in BrAPI 2.0, resulting in full compatibility between the two standards. Consequently, BrAPI now encompasses all attributes necessary for MIAPPE compliance, adhering to standardized descriptions in accordance with MIAPPE guidelines. Leveraging BrAPI as a standardized RESTful web service API specification, we employ the ISA standard for storing metadata and phenotyping data in a standardized manner. This data is structured in the ISA-TAB file format and subjected to validation using the MIAPPE ISA configuration. The “BrAPI to ISA” service functions as a converter between BrAPI RESTful endpoints and ISA-TAB, facilitating the archiving of metadata and data and thereby enhancing data preservation and accessibility. The BrAPI2ISA tool is designed to be compatible with BrAPI 1.3, and we invite contributions from the community to extend support for the latest versions of BrAPI.
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BrAPIMapper
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BrAPIMapper is a full BrAPI implementation designed to be a wrapper for any data source missing BrAPI implementation. BrAPIMapper is provided as a docker application that can get its external data sources from 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 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 export and import features simplify upgrades to future BrAPI specifications changes as administrators would 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.
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BrAPI plug and play GraphQL based data-warehouse
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Using the “Zendro” set of automatic software code generators (zendro-dev.github.io), 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.
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The GraphQL server is particularly rich in features. Records are paginated using the highly efficient cursor based pagination model as proposed in the GraphQL standard. Logical filters allow for exhaustive search queries, whose structure is highly intuitive and based around logical triplets. A data model field is validated using an operator and a value, e.g. “Study name equals ‘my_study’”. 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. Access security is implemented with the OAuth2 user authentication standard (datatracker.ietf.org/doc/html/rfc6749). 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.
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The Zendro data-warehouse is capable of forming an efficient cloud of data servers. This is achieved simply by linking (URLs) other Zendro based warehouses that expose the same GraphQL API to the same data models, or a subset of data models. Any network of such Zendro GraphQL servers can be set up using this configuration approach. The code generated then exposes full access to all data records stored on any node of the network, while maintaining full security control at each node. Importantly, the warehouses are programmed in such a way that any number of data servers can be joined without loss of efficiency. Only the network connection speed and size of requested record sets influence the performance.
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Discussion
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BrAPI for Breeders and Scientists
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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 to make the world a better place. BrAPI offers a convenient path to automation and data integration for software tools in the breeding domain. All of the example use cases described above can be achieved with manual effort, moving and editing data files by hand. 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, doing what they do best. 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 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 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.
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Looking Ahead
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The BrAPI specification will continue to grow, enabling more use cases and new types of data. These new use cases might include newer scientific techniques and technologies. Things like drone imaging data, spectroscopy, LIDAR, metabolomics, transcriptomics, high-throughput phenotyping, and machine learning analysis. All of these technologies can open new avenues for research and development of new crop varieties. All of these technologies also generate more 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 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.
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Conclusions and Impact
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High level summary of the project/consortium
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BrAPI is fitting into this gap, it doesn’t need to fit these other gaps
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Call to action - Join us!
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References
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1.
BrAPI—an application programming interface for plant breeding applications
Peter Selby, Rafael Abbeloos, Jan Erik Backlund, Martin Basterrechea Salido, Guillaume Bauchet, Omar E Benites-Alfaro, Clay Birkett, Viana C Calaminos, Pierre Carceller, Guillaume Cornut, …
ClimMob: Software to support experimental citizen science in agriculture
Carlos Quirós, Kauê de Sousa, Jonathan Steinke, Brandon Madriz, Marie-Angélique Laporte, Elizabeth Arnaud, Rhys Manners, Berta Ortiz-Crespo, Anna Müller, Jacob van Etten
Enabling reusability of plant phenomic datasets with MIAPPE 1.1
Evangelia A Papoutsoglou, Daniel Faria, Daniel Arend, Elizabeth Arnaud, Ioannis N Athanasiadis, Inês Chaves, Frederik Coppens, Guillaume Cornut, Bruno V Costa, Hanna Ćwiek‐Kupczyńska, … Cyril Pommier
The FAIR Guiding Principles for scientific data management and stewardship
Mark D Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, Jan-Willem Boiten, Luiz Bonino da Silva Santos, Philip E Bourne, … Barend Mons
Safeguarding and using global banana diversity: a holistic approach
Ines Van den houwe, Rachel Chase, Julie Sardos, Max Ruas, Els Kempenaers, Valentin Guignon, Sebastien Massart, Sebastien Carpentier, Bart Panis, Mathieu Rouard, Nicolas Roux
Multifunctional crop trait ontology for breeders' data: field book, annotation, data discovery and semantic enrichment of the literature
Rosemary Shrestha, Elizabeth Arnaud, Ramil Mauleon, Martin Senger, Guy F Davenport, David Hancock, Norman Morrison, Richard Bruskiewich, Graham McLaren
A Critical Review of the Current Global Ex Situ Conservation System for Plant Agrobiodiversity. I. History of the Development of the Global System in the Context of the Political/Legal Framework and Its Major Conservation Components
Recommendations for the formatting of Variant Call Format (VCF) files to make plant genotyping data FAIR
Sebastian Beier, Anne Fiebig, Cyril Pommier, Isuru Liyanage, Matthias Lange, Paul J Kersey, Stephan Weise, Richard Finkers, Baron Koylass, Timothee Cezard, … Uwe Scholz
Unlocking the genetic diversity and population structure of the newly introduced two-row spring European HerItage Barley collecTion (ExHIBiT)
Villő Bernád, Nadia Al-Tamimi, Patrick Langan, Gary Gillespie, Timothy Dempsey, Joey Henchy, Mary Harty, Luke Ramsay, Kelly Houston, Malcolm Macaulay, … Sónia Negrão
Admixture and reproductive skew shape the conservation value of ex situ populations of the Critically Endangered eastern black rhino
Franziska Elsner-Gearing, Petra Kretzschmar, Susanne Shultz, Mark Pilgrim, Deborah Ann Dawson, Gavin John Horsburgh, Jírí Hruby, Jane Hopper, Tony King, Catherine Walton
Breedbase: a digital ecosystem for modern plant breeding
Nicolas Morales, Alex C Ogbonna, Bryan J Ellerbrock, Guillaume J Bauchet, Titima Tantikanjana, Isaak Y Tecle, Adrian F Powell, David Lyon, Naama Menda, Christiano C Simoes, … Lukas A Mueller
From bits to bites: Advancement of the Germinate platform to support prebreeding informatics for crop wild relatives
Sebastian Raubach, Benjamin Kilian, Kate Dreher, Ahmed Amri, Filippo M Bassi, Ousmane Boukar, Douglas Cook, Alan Cruickshank, Christian Fatokun, Noureddine El Haddad, …
Enabling reusability of plant phenomic datasets with MIAPPE 1.1
Evangelia A Papoutsoglou, Daniel Faria, Daniel Arend, Elizabeth Arnaud, Ioannis N Athanasiadis, Inês Chaves, Frederik Coppens, Guillaume Cornut, Bruno V Costa, Hanna Ćwiek‐Kupczyńska, … Cyril Pommier
Susanna-Assunta Sansone, Philippe Rocca-Serra, Dawn Field, Eamonn Maguire, Chris Taylor, Oliver Hofmann, Hong Fang, Steffen Neumann, Weida Tong, Linda Amaral-Zettler, … Winston Hide
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