From 58d78bf291e342f897f620085a97c3fe143ce920 Mon Sep 17 00:00:00 2001 From: Jenna Hershberger Date: Tue, 9 Jul 2024 13:51:42 -0400 Subject: [PATCH 01/21] Add Jenna Hershberger to author list --- content/metadata.yaml | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/content/metadata.yaml b/content/metadata.yaml index 5bc6746..f122984 100644 --- a/content/metadata.yaml +++ b/content/metadata.yaml @@ -217,6 +217,13 @@ authors: email: a.hallab@fz-juelich.de affiliations: - Jülich research center, Institute of Bio- and Geosciences (IBG), Bioinformatics (IBG-4) and Bingen Technical University of Applied Sciences, Germany + - name: Jenna Hershberger + initials: JH + github: jmh579 + orcid: 0000-0002-3147-6867 + email: jmhersh@clemson.edu + affiliations: + - Clemson University - name: Puthick Hok initials: PH github: puthick From 66f9ae6f1a1d802d9584eda96927b80f282f728b Mon Sep 17 00:00:00 2001 From: Jenna Hershberger Date: Mon, 15 Jul 2024 12:22:22 -0400 Subject: [PATCH 02/21] ImageBreed edits JH --- content/03.01.04.Image_Breed.md | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/content/03.01.04.Image_Breed.md b/content/03.01.04.Image_Breed.md index 479b4a4..bc9abca 100644 --- a/content/03.01.04.Image_Breed.md +++ b/content/03.01.04.Image_Breed.md @@ -1,5 +1,7 @@ #### ImageBreed -[ImageBreed](https://imagebreed.org/) [@doi:10.1002/ppj2.20004] is an image collection pipeline tool to support regular use of UAVs and UGVs. 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. - -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. +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. +[ImageBreed](https://imagebreed.org/) [@doi:10.1002/ppj2.20004] 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. From e9431bf8804d3a68bba67809964c32bd5aaa6755 Mon Sep 17 00:00:00 2001 From: Jenna Hershberger Date: Mon, 15 Jul 2024 15:04:46 -0400 Subject: [PATCH 03/21] GridScore edits JH --- content/03.01.02.GridScore.md | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/content/03.01.02.GridScore.md b/content/03.01.02.GridScore.md index 0d9086d..19fd618 100644 --- a/content/03.01.02.GridScore.md +++ b/content/03.01.02.GridScore.md @@ -1,9 +1,13 @@ #### GridScore - + -[GridScore](https://ics.hutton.ac.uk/get-gridscore/) [@doi:10.1186/s12859-022-04755-2] is a modern mobile application for phenotypic observations that harnesses technological advancements in the area of mobile devices to enrich the data collection process. GridScore focuses on user experience by closely mirroring the look and feel of printed field plans. It also enriches the experience with a wide range of functionalities including data validation, data visualizations, georeferencing, cross-platform support, and data synchronization across multiple devices. GridScore is a multi-platform tool which works on any reasonably modern device with a web browser including laptops, PCs, tablets and phones. Once created, trials can be transferred to collection devices using a Quick Response (QR) code. Its approach towards data collection uses a top-down view onto the trial and offers field navigation mechanisms using barcodes, QR codes, or guided walks which take the data collector through the field in one of 16 pre-defined orders. +[GridScore](https://ics.hutton.ac.uk/get-gridscore/) [@doi:10.1186/s12859-022-04755-2] 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. -BrAPI has further increased the value of GridScore by integrating it into the overarching workflow, from trial creation, through data collection, and to 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 be exported via BrAPI to any compatible database. +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. From e4620b4e5619c4fe07acc78e1c2d35f703788efe Mon Sep 17 00:00:00 2001 From: Jenna Hershberger Date: Mon, 15 Jul 2024 17:53:24 -0400 Subject: [PATCH 04/21] FAIDARE edits JH --- content/03.04.05.FAIDARE.md | 18 +++++++++++++++--- 1 file changed, 15 insertions(+), 3 deletions(-) diff --git a/content/03.04.05.FAIDARE.md b/content/03.04.05.FAIDARE.md index b9ed794..f85d3af 100644 --- a/content/03.04.05.FAIDARE.md +++ b/content/03.04.05.FAIDARE.md @@ -1,10 +1,22 @@ #### FAIDARE -[FAIDARE](https://urgi.versailles.inrae.fr/faidare/) [@https://hal.inrae.fr/hal-04638310] is a data discovery portal providing a biologist friendly search system over a global federation of 40 plant research databases at the time of writing. 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 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 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. +[FAIDARE](https://urgi.versailles.inrae.fr/faidare/) [@https://hal.inrae.fr/hal-04638310] 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 indexation mechanism relies on a [public software package](https://github.com/elixir-europe/plant-brapi-etl-faidare) that allows data resource managers to request the indexation of their database. 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, it is possible to generate metadata as static BrAPI compliant JSON files, using the BrAPI standard as a file exchange format. +The FAIDARE indexation mechanism relies on a public software package [@https://github.com/elixir-europe/plant-brapi-etl-faidare] 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 mixed with the GnpIS Software Architecture [@doi:10.34133/2019/1671403]. 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, allowing to increase both the portal and the BrAPI adoption. +The FAIDARE architecture has been designed by elaborating on the BrAPI data model in combination with the GnpIS Software Architecture [@doi:10.34133/2019/1671403]. +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. From e4f471a9fc1dabd18d76d7ece01204804004f039 Mon Sep 17 00:00:00 2001 From: Jenna Hershberger Date: Mon, 15 Jul 2024 18:00:46 -0400 Subject: [PATCH 05/21] BIMS edits JH --- content/03.04.01.BIMS.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/content/03.04.01.BIMS.md b/content/03.04.01.BIMS.md index 4172423..d18cf03 100644 --- a/content/03.04.01.BIMS.md +++ b/content/03.04.01.BIMS.md @@ -1,6 +1,9 @@ #### BIMS -[BIMS](https://wwww.breedwithbims.org) (Breeding Information Management System) [@doi:10.1093/database/baab054] 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 [@doi:10.1093/nar/gky1000], CottonGEN [@doi:10.3390/plants10122805], 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. BIMS is also implemented in the public database [breedwithbims.org](https://wwww.breedwithbims.org) that any crop breeder can use. + [The Breeding Information Management System (BIMS)](https://wwww.breedwithbims.org) [@doi:10.1093/database/baab054] 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 Rosaceae [@doi:10.1093/nar/gky1000], CottonGEN [@doi:10.3390/plants10122805], 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](https://wwww.breedwithbims.org) that any crop breeder can use. -Right now, BIMS primarily utilizes BrAPI to connect with the Field Book Android App [@doi:10.2135/cropsci2013.08.0579], enabling seamless data transfer between BIMS and the app. Data transfer through BrAPI between BIMS and other resources such as BreedBase[@doi:10.1093/g3journal/jkac078], GIGWA[@doi:10.1093/gigascience/giz051], 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. +BIMS primarily utilizes BrAPI to connect with Field Book [@doi:10.2135/cropsci2013.08.0579], enabling seamless data transfer between data collection and subsequent managament in BIMS. Data transfer through BrAPI between BIMS and other resources such as Breedbase [@doi:10.1093/g3journal/jkac078], GIGWA [@doi:10.1093/gigascience/giz051], and Breeder Genomics Hub is under development. From 5c98faac93376bbf75f18b6287d0bc5f7b885403 Mon Sep 17 00:00:00 2001 From: Jenna Hershberger Date: Mon, 15 Jul 2024 18:43:35 -0400 Subject: [PATCH 06/21] Trait_Selector_BrAPP edits JH --- content/03.01.07.Trait_Selector_BrAPP.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/content/03.01.07.Trait_Selector_BrAPP.md b/content/03.01.07.Trait_Selector_BrAPP.md index 7de5f58..48e32bf 100644 --- a/content/03.01.07.Trait_Selector_BrAPP.md +++ b/content/03.01.07.Trait_Selector_BrAPP.md @@ -4,8 +4,11 @@ -The [Trait Selector BrAPP](https://github.com/solgenomics/BrAPI-Trait-selector) 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. +The [Trait Selector BrAPP](https://github.com/solgenomics/BrAPI-Trait-selector) 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. -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. +The Trait Selector BrAPP has been successfully added to Cassavabase and MGIS, 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. From c44f3de99b86b9137f22420c52c67fb8957781c8 Mon Sep 17 00:00:00 2001 From: Jenna Hershberger Date: Mon, 15 Jul 2024 18:47:07 -0400 Subject: [PATCH 07/21] PHIS edits JH --- content/03.01.05.PHIS.md | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/content/03.01.05.PHIS.md b/content/03.01.05.PHIS.md index 57d915a..d714597 100644 --- a/content/03.01.05.PHIS.md +++ b/content/03.01.05.PHIS.md @@ -1,8 +1,14 @@ #### PHIS -[PHIS](http://www.phis.inrae.fr/) [@doi:10.1111/nph.15385], the Hybrid Phenotyping Information System, is an ontology-driven information system based on semantic web technologies, based on the [OpenSILEX](https://github.com/OpenSILEX/) framework. PHIS is deployed in several field and greenhouse platforms of the French national [PHENOME](https://www.phenome-emphasis.fr/) and European [EMPHASIS](https://emphasis.plant-phenotyping.eu/) infrastructures. It manages and collects data from basic 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](http://www.phis.inrae.fr/) [@doi:10.1111/nph.15385], the Hybrid Phenotyping Information System, is an ontology-driven information system based on semantic web technologies and the [OpenSILEX](https://github.com/OpenSILEX/) framework. +PHIS is deployed in several field and greenhouse platforms of the French national [PHENOME](https://www.phenome-emphasis.fr/) and European [EMPHASIS](https://emphasis.plant-phenotyping.eu/) 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. -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, such as OLGA, a genebank accessions management system, to retrieve accession information. 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](https://www.miappe.org/) [@doi:10.1111/nph.16544]). 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 enables indexing of PHIS in the [FAIDARE](https://urgi.versailles.inrae.fr/faidare/) data portal [@https://hal.inrae.fr/hal-04638310]. +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. +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 ([MIAPPE](https://www.miappe.org/) [@doi:10.1111/nph.16544]) and enables the indexing of PHIS in [FAIDARE](https://urgi.versailles.inrae.fr/faidare/) [@https://urgi.versailles.inrae.fr/faidare]. +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. -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 adhesion 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. From b36edecb859aae487178ab0acbc6debff7c2d525 Mon Sep 17 00:00:00 2001 From: Jenna Hershberger Date: Mon, 15 Jul 2024 18:52:45 -0400 Subject: [PATCH 08/21] PIPPA edits JH --- content/03.01.06.PIPPA.md | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/content/03.01.06.PIPPA.md b/content/03.01.06.PIPPA.md index 923a87d..f7c8068 100644 --- a/content/03.01.06.PIPPA.md +++ b/content/03.01.06.PIPPA.md @@ -1,8 +1,14 @@ #### PIPPA -[PIPPA](https://pippa.psb.ugent.be) [@https://pippa.psb.ugent.be] is a data management system used for collecting data from the [WIWAM](https://www.wiwam.be/) [@https://www.wiwam.be] 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. +[PIPPA](https://pippa.psb.ugent.be) [@https://pippa.psb.ugent.be] is a data management system used for collecting data from the Weighing, Imaging and Watering Machines ([WIWAM](https://www.wiwam.be/)) [@https://www.wiwam.be] 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. -To share the phenotype data of the 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 [FAIDARE](https://urgi.versailles.inrae.fr/faidare/) [@https://hal.inrae.fr/hal-04638310] which allows the data to be found alongside data from other BrAPI compatible repositories. +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 [FAIDARE](https://urgi.versailles.inrae.fr/faidare/) [@https://urgi.versailles.inrae.fr/faidare], allowing 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, [@doi:10.1038/sdata.2016.18] which are becoming best practices in plant research data management. The BrAPI technical standard, in combination with the [MIAPPE](https://www.miappe.org/) [@doi:10.1111/nph.16544] 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. +Throughout its development, the PIPPA project has adhered to guidelines set forth by BrAPI and the [MIAPPE](https://www.miappe.org/) [@doi:10.1111/nph.16544] 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. \ No newline at end of file From 78080a54f40c8614e9c9c669cb22b77eb09ca0e5 Mon Sep 17 00:00:00 2001 From: Jenna Hershberger Date: Tue, 16 Jul 2024 09:47:56 -0400 Subject: [PATCH 09/21] DivBrowse edits JH --- content/03.02.03.DivBrowse.md | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/content/03.02.03.DivBrowse.md b/content/03.02.03.DivBrowse.md index 6f89cf0..c0c02f5 100644 --- a/content/03.02.03.DivBrowse.md +++ b/content/03.02.03.DivBrowse.md @@ -1,8 +1,13 @@ #### DivBrowse -[DivBrowse](https://divbrowse.ipk-gatersleben.de/) [@doi:10.1093/gigascience/giad025] 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. +[DivBrowse](https://divbrowse.ipk-gatersleben.de/) [@doi:10.1093/gigascience/giad025] 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. +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. -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, 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. 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. +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. \ No newline at end of file From 1a65dc950a0cb662dda48f884f56c79709855410 Mon Sep 17 00:00:00 2001 From: Jenna Hershberger Date: Tue, 16 Jul 2024 09:56:12 -0400 Subject: [PATCH 10/21] Flapjack edits JH --- content/03.02.04.Flapjack.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/content/03.02.04.Flapjack.md b/content/03.02.04.Flapjack.md index d04ae06..56c1f0b 100644 --- a/content/03.02.04.Flapjack.md +++ b/content/03.02.04.Flapjack.md @@ -1,3 +1,7 @@ #### Flapjack -[Flapjack](https://ics.hutton.ac.uk/flapjack) [@doi:10.1093/bioinformatics/btq580] 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](https://github.com/cropgeeks/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. +[Flapjack](https://ics.hutton.ac.uk/flapjack) [@doi:10.1093/bioinformatics/btq580] 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](https://github.com/cropgeeks/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. From a7c3f6735b4b20baee3551dcd2b49becb0f4a3a7 Mon Sep 17 00:00:00 2001 From: Jenna Hershberger Date: Tue, 16 Jul 2024 10:20:12 -0400 Subject: [PATCH 11/21] ClimMob edits JH --- content/03.01.03.ClimMob.md | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/content/03.01.03.ClimMob.md b/content/03.01.03.ClimMob.md index 3f5984f..e3ad22f 100644 --- a/content/03.01.03.ClimMob.md +++ b/content/03.01.03.ClimMob.md @@ -2,6 +2,12 @@ -[ClimMob](https://climmob.net/) [@doi:10.1016/j.compag.2023.108539] is a software suite for a different research paradigm in experimental agriculture. In traditional breeding, a few researchers design complicated trials in search of the best solutions for a few target environments. ClimMob enables many participants to carry out reasonably simple experiments across many environments. Taken together, this data across many environments can be very informative. It applies the principles 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. The platform supports experiment design, data collection through mobile apps, and data analysis to provide actionable insights. +In traditional breeding, a small number of researchers design complicated trials in search of the best solutions for few target environments. +[ClimMob](https://climmob.net/) [@doi:10.1016/j.compag.2023.108539] is a software suite for an alternative research paradigm in experimental agriculture. +ClimMob applies the principles of citizen science and choice experiments to scale the data collection process, mostly in the format of 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. -During a crop trial, all farmer-collected data is stored in ClimMob. When data collection is complete, the raw data is automatically uploaded via BrAPI to a central breeding database for long-term storage and analysis. 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. +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. From 6b30579600df7117b8a21d84994f1eb545fc301e Mon Sep 17 00:00:00 2001 From: Jenna Hershberger Date: Wed, 17 Jul 2024 13:10:18 -0400 Subject: [PATCH 12/21] QBMS edits JH --- content/03.05.02.QBMS.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/content/03.05.02.QBMS.md b/content/03.05.02.QBMS.md index 4566968..f74dea7 100644 --- a/content/03.05.02.QBMS.md +++ b/content/03.05.02.QBMS.md @@ -2,6 +2,10 @@ -The [QBMS](https://icarda-git.github.io/QBMS) [@doi:10.5281/zenodo.10791627] 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 the R language, one of the most common statistical tools in the breeding community. +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](https://icarda-git.github.io/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 systems [@doi:10.5281/zenodo.10791627]. +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. -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. +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. \ No newline at end of file From d8f5f0b94193fbcfd275d0a26c72df83ce6d0b00 Mon Sep 17 00:00:00 2001 From: Jenna Hershberger Date: Wed, 17 Jul 2024 13:31:53 -0400 Subject: [PATCH 13/21] Germinate edits JH --- content/03.04.06.Germinate.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/content/03.04.06.Germinate.md b/content/03.04.06.Germinate.md index 57d3325..9aa1ea3 100644 --- a/content/03.04.06.Germinate.md +++ b/content/03.04.06.Germinate.md @@ -1,6 +1,9 @@ #### Germinate -[Germinate](https://ics.hutton.ac.uk/get-germinate/) [@doi:10.1002/csc2.20248;@doi:10.2135/cropsci2016.09.0814] 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. +[Germinate](https://ics.hutton.ac.uk/get-germinate/) [@doi:10.1002/csc2.20248;@doi:10.2135/cropsci2016.09.0814] 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 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. +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. From dc13605d0acf4b9e800fa2da33f2ebbfd8f6ffeb Mon Sep 17 00:00:00 2001 From: Peter Selby <32845555+BrapiCoordinatorSelby@users.noreply.github.com> Date: Wed, 17 Jul 2024 13:22:05 -0400 Subject: [PATCH 14/21] Update manubot.yaml --- .github/workflows/manubot.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/manubot.yaml b/.github/workflows/manubot.yaml index 1ef5e98..4e93327 100644 --- a/.github/workflows/manubot.yaml +++ b/.github/workflows/manubot.yaml @@ -45,6 +45,7 @@ jobs: # Set SPELLCHECK to true/false for whether to check spelling in this action. # For workflow dispatch jobs, this SPELLCHECK setting will be overridden by the user input. SPELLCHECK: true + BUILD_DOCX: true defaults: run: shell: bash --login {0} From dd7675b07ce6363e14b30241a01c687df7658041 Mon Sep 17 00:00:00 2001 From: Peter Selby <32845555+BrapiCoordinatorSelby@users.noreply.github.com> Date: Wed, 17 Jul 2024 13:51:18 -0400 Subject: [PATCH 15/21] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index d59814b..8f6c7aa 100644 --- a/README.md +++ b/README.md @@ -27,5 +27,6 @@ If you would like to be recognized as a co-author, please add your details to th + [Markdown Formatting Cheat Sheet](content/formatting_help) + [HTML manuscript](https://plantbreeding.github.io/BrAPI-Manuscript2/) + [PDF manuscript](https://plantbreeding.github.io/BrAPI-Manuscript2/manuscript.pdf) ++ [MS DOCX manuscript](https://github.com/plantbreeding/BrAPI-Manuscript2/raw/output/manuscript.docx) + [Manubot README](Manubot-README.md) + [Manubot Usage Instructions](USAGE.md) From 316779772113db5c379fd2b10de82a4a5ab40ba9 Mon Sep 17 00:00:00 2001 From: Peter Selby Date: Wed, 17 Jul 2024 15:27:23 -0400 Subject: [PATCH 16/21] general small edits --- content/01.abstract.md | 2 +- content/03.01.05.PHIS.md | 7 +++---- content/03.01.06.PIPPA.md | 2 +- content/03.04.04.DeltaBreed.md | 4 +--- content/03.04.05.FAIDARE.md | 5 ++--- 5 files changed, 8 insertions(+), 12 deletions(-) diff --git a/content/01.abstract.md b/content/01.abstract.md index 2ea36da..df7f936 100644 --- a/content/01.abstract.md +++ b/content/01.abstract.md @@ -3,4 +3,4 @@ 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 current version of BrAPI, the substantial growth of the project, and a wide variety of open source plant genetics research tools with active BrAPI implementations. +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. diff --git a/content/03.01.05.PHIS.md b/content/03.01.05.PHIS.md index d714597..d49f025 100644 --- a/content/03.01.05.PHIS.md +++ b/content/03.01.05.PHIS.md @@ -6,9 +6,8 @@ PHIS is deployed in several field and greenhouse platforms of the French nationa 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. -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. +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](https://crb-plantes-olga.fr/public/frontend/auth/login), and is indexed by the [FAIDARE](https://urgi.versailles.inrae.fr/faidare/) data portal [@https://urgi.versailles.inrae.fr/faidare]. 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 ([MIAPPE](https://www.miappe.org/) [@doi:10.1111/nph.16544]) and enables the indexing of PHIS in [FAIDARE](https://urgi.versailles.inrae.fr/faidare/) [@https://urgi.versailles.inrae.fr/faidare]. +BrAPI compliance also ensures that PHIS is compatible with other standards such as the Minimal Information About a Plant Phenotyping Experiment ([MIAPPE](https://www.miappe.org/) [@doi:10.1111/nph.16544]). 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. - diff --git a/content/03.01.06.PIPPA.md b/content/03.01.06.PIPPA.md index f7c8068..0245939 100644 --- a/content/03.01.06.PIPPA.md +++ b/content/03.01.06.PIPPA.md @@ -11,4 +11,4 @@ To share the phenotypic data from PIPPA experiments linked to publications, an i This server was registered on [FAIDARE](https://urgi.versailles.inrae.fr/faidare/) [@https://urgi.versailles.inrae.fr/faidare], allowing the data to be found alongside data from other BrAPI-compatible repositories. Throughout its development, the PIPPA project has adhered to guidelines set forth by BrAPI and the [MIAPPE](https://www.miappe.org/) [@doi:10.1111/nph.16544] 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. \ No newline at end of file +Current efforts are focused on delivering a public BrAPI v2.1 endpoint and increasing the availability of public high-throughput datasets via BrAPI. diff --git a/content/03.04.04.DeltaBreed.md b/content/03.04.04.DeltaBreed.md index b420652..635e533 100644 --- a/content/03.04.04.DeltaBreed.md +++ b/content/03.04.04.DeltaBreed.md @@ -5,7 +5,5 @@ 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. -Breeding Insight integrated several BrAPI applications to support 2021 phenotypic data collection by USDA-ARS blueberry breeders. DeltaBreed was used to create traits in Breedbase, and Field Book was used to pull studies and traits from Breedbase. The workflow also permitted users to push Field Book observations back to Breedbase via BrAPI. This effort served as a successful proof of concept for multi-application BrAPI integration, but highlighted limitations to the process of accepting BrAPI observations from Field Book. The Breeding Insight team is actively working with the rest of the BrAPI community to correct these limitations in future versions of the specification. - -DeltaBreed is integrated with many other BrAPI community projects and tools. There is a BrAPI enabled connection, either in development or production, with all of the following tools: [BrAPI Java Test Server](https://test-server.brapi.org/brapi/v2/), [BreedBase](https://breedbase.org/), [Field Book](https://play.google.com/store/apps/details?id=com.fieldbook.tracker), [Gigwa](https://gigwa.southgreen.fr/gigwa/), [QBMS](https://icarda-git.github.io/QBMS), [Mr Bean](https://github.com/AparicioJohan/MrBeanApp), [Helium](https://helium.hutton.ac.uk/#/) and the [Pedigree Viewer](https://github.com/solgenomics/BrAPI-Pedigree-Viewer) BrAPP. +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](https://test-server.brapi.org/brapi/v2/), [BreedBase](https://breedbase.org/), [Field Book](https://play.google.com/store/apps/details?id=com.fieldbook.tracker), [Gigwa](https://gigwa.southgreen.fr/gigwa/), [QBMS](https://icarda-git.github.io/QBMS), [Mr Bean](https://github.com/AparicioJohan/MrBeanApp), [Helium](https://helium.hutton.ac.uk/#/) and the [Pedigree Viewer](https://github.com/solgenomics/BrAPI-Pedigree-Viewer) BrAPP. diff --git a/content/03.04.05.FAIDARE.md b/content/03.04.05.FAIDARE.md index f85d3af..012a07a 100644 --- a/content/03.04.05.FAIDARE.md +++ b/content/03.04.05.FAIDARE.md @@ -1,12 +1,11 @@ #### FAIDARE -[FAIDARE](https://urgi.versailles.inrae.fr/faidare/) [@https://hal.inrae.fr/hal-04638310] is a data discovery portal providing a biologist-friendly search system over a global federation of 40 plant research databases. +[FAIDARE](https://urgi.versailles.inrae.fr/faidare/) [@https://hal.inrae.fr/hal-04425516] 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. +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 package [@https://github.com/elixir-europe/plant-brapi-etl-faidare] that allows data resource managers to request the indexation of their database. From 5bbf52f4ba6dfd65772e426a2fc4a262ad9e3acc Mon Sep 17 00:00:00 2001 From: Peter Selby Date: Wed, 17 Jul 2024 16:54:08 -0400 Subject: [PATCH 17/21] today I learned how to use a hyphen properly... --- content/03.01.--.HEADER.Phenotyping.md | 4 ++-- content/03.01.03.ClimMob.md | 4 ++-- content/03.01.05.PHIS.md | 2 +- content/03.02.--.HEADER.Genotyping.md | 2 +- content/03.03.02.FLORILEGE.md | 2 +- content/03.03.04.Helium.md | 2 +- content/03.04.05.FAIDARE.md | 4 ++-- content/03.05.01.G-Crunch.md | 2 +- content/03.06.03.Zendro.md | 2 +- content/04.discussion.md | 2 +- 10 files changed, 13 insertions(+), 13 deletions(-) diff --git a/content/03.01.--.HEADER.Phenotyping.md b/content/03.01.--.HEADER.Phenotyping.md index 8e78575..e2f83dc 100644 --- a/content/03.01.--.HEADER.Phenotyping.md +++ b/content/03.01.--.HEADER.Phenotyping.md @@ -2,9 +2,9 @@ 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 to ensure successful outcomes. +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 accurate and efficient collection and storage of phenotypic data, BrAPI compatible tools simplify the conversion of phenotypes into insights that are necessary to help digitize and boost modern breeding and genetics research programs. +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. diff --git a/content/03.01.03.ClimMob.md b/content/03.01.03.ClimMob.md index e3ad22f..622dbdb 100644 --- a/content/03.01.03.ClimMob.md +++ b/content/03.01.03.ClimMob.md @@ -2,9 +2,9 @@ -In traditional breeding, a small number of researchers design complicated trials in search of the best solutions for few target environments. [ClimMob](https://climmob.net/) [@doi:10.1016/j.compag.2023.108539] is a software suite for an alternative research paradigm in experimental agriculture. -ClimMob applies the principles of citizen science and choice experiments to scale the data collection process, mostly in the format of rankings. +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. diff --git a/content/03.01.05.PHIS.md b/content/03.01.05.PHIS.md index d49f025..e427a26 100644 --- a/content/03.01.05.PHIS.md +++ b/content/03.01.05.PHIS.md @@ -6,7 +6,7 @@ PHIS is deployed in several field and greenhouse platforms of the French nationa 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. -Since its inception, PHIS has been designed to be BrAPI-compliant, encompassing the Core, Phenotyping, and Germplasm BrAPI modules. +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](https://crb-plantes-olga.fr/public/frontend/auth/login), and is indexed by the [FAIDARE](https://urgi.versailles.inrae.fr/faidare/) data portal [@https://urgi.versailles.inrae.fr/faidare]. 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 ([MIAPPE](https://www.miappe.org/) [@doi:10.1111/nph.16544]). diff --git a/content/03.02.--.HEADER.Genotyping.md b/content/03.02.--.HEADER.Genotyping.md index 2e45f16..7c8c354 100644 --- a/content/03.02.--.HEADER.Genotyping.md +++ b/content/03.02.--.HEADER.Genotyping.md @@ -1,4 +1,4 @@ ### Genotyping -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 VCF [@doi:10.1093/bioinformatics/btr330] and the GA4GH Variants schema [@https://github.com/ga4gh-metadata/SchemaBlocks]. 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. +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 VCF [@doi:10.1093/bioinformatics/btr330] and the GA4GH Variants schema [@https://github.com/ga4gh-metadata/SchemaBlocks]. 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. diff --git a/content/03.03.02.FLORILEGE.md b/content/03.03.02.FLORILEGE.md index e674db5..65e7ed6 100644 --- a/content/03.03.02.FLORILEGE.md +++ b/content/03.03.02.FLORILEGE.md @@ -3,6 +3,6 @@ [Florilège](https://florilege.arcad-project.org/) 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. -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 genetics [@doi:10.34133/2019/1671403;@doi:10.1007/978-1-4939-6658-5_5]. 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. +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 genetics [@doi:10.34133/2019/1671403;@doi:10.1007/978-1-4939-6658-5_5]. 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. diff --git a/content/03.03.04.Helium.md b/content/03.03.04.Helium.md index 365abf5..4820349 100644 --- a/content/03.03.04.Helium.md +++ b/content/03.03.04.Helium.md @@ -3,4 +3,4 @@ [Helium](https://helium.hutton.ac.uk) [@doi:10.1186/1471-2105-15-259] 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 [@doi:10.1111/j.1365-2052.2011.02183.x]. 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. -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. 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 [@doi:10.3389/fpls.2024.1268847] it has also found utility in other non-plant projects [@doi:10.1007/s10592-024-01611-z] 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. +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. 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 [@doi:10.3389/fpls.2024.1268847] it has also found utility in other non-plant projects [@doi:10.1007/s10592-024-01611-z] 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. diff --git a/content/03.04.05.FAIDARE.md b/content/03.04.05.FAIDARE.md index 012a07a..5c0345e 100644 --- a/content/03.04.05.FAIDARE.md +++ b/content/03.04.05.FAIDARE.md @@ -8,9 +8,9 @@ The indexed data types are broad and include genomic features, selected bibliogr 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 package [@https://github.com/elixir-europe/plant-brapi-etl-faidare] that allows data resource managers to request the indexation of their database. +The FAIDARE indexation mechanism relies on a [public software package](https://github.com/elixir-europe/plant-brapi-etl-faidare) [@https://github.com/elixir-europe/plant-brapi-etl-faidare] 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. +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 Architecture [@doi:10.34133/2019/1671403]. It uses an Elasticsearch NoSQL engine that searches and serves enriched versions of the BrAPI JSON data model. diff --git a/content/03.05.01.G-Crunch.md b/content/03.05.01.G-Crunch.md index 83c3e64..aa546a4 100644 --- a/content/03.05.01.G-Crunch.md +++ b/content/03.05.01.G-Crunch.md @@ -3,4 +3,4 @@ G-Crunch is an upcoming user-facing analysis tool that integrates genomic and phenotypic data to fulfill the need for simple, user driven analytics requests. It includes a generic user interface and the ability to swap out data sources and analysis tools. The G-Crunch team hopes to streamline repeatable, debuggable, simple analytic requests and results. -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. +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. diff --git a/content/03.06.03.Zendro.md b/content/03.06.03.Zendro.md index c52963e..4521dc7 100644 --- a/content/03.06.03.Zendro.md +++ b/content/03.06.03.Zendro.md @@ -1,7 +1,7 @@ #### GraphQL Data-warehouse -Using the [Zendro](https://zendro-dev.github.io) 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. +Using the [Zendro](https://zendro-dev.github.io) 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. 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 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 [@https://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. diff --git a/content/04.discussion.md b/content/04.discussion.md index f6e534d..ae5e326 100644 --- a/content/04.discussion.md +++ b/content/04.discussion.md @@ -22,6 +22,6 @@ Expanding the BrAPI specification is important for the community, however it is ### 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. +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](https://brapi.org). From de4baf3e5d0eefdf87ace30aed62d65cfddad936 Mon Sep 17 00:00:00 2001 From: Peter Selby Date: Fri, 19 Jul 2024 13:59:55 -0400 Subject: [PATCH 18/21] general edits --- content/03.01.07.Trait_Selector_BrAPP.md | 5 +++-- content/03.02.01.DArT.md | 4 ++-- content/03.02.02.DArTView.md | 4 ++-- content/03.02.03.DivBrowse.md | 4 ++-- content/03.02.05.GIGWA.md | 2 +- content/03.02.06.PHG.md | 2 +- content/03.03.01.AGENT.md | 2 +- 7 files changed, 12 insertions(+), 11 deletions(-) diff --git a/content/03.01.07.Trait_Selector_BrAPP.md b/content/03.01.07.Trait_Selector_BrAPP.md index 48e32bf..30fe84b 100644 --- a/content/03.01.07.Trait_Selector_BrAPP.md +++ b/content/03.01.07.Trait_Selector_BrAPP.md @@ -4,11 +4,12 @@ -The [Trait Selector BrAPP](https://github.com/solgenomics/BrAPI-Trait-selector) 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. +The [Trait Selector BrAPP](https://github.com/solgenomics/BrAPI-Trait-selector) 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. -The Trait Selector BrAPP has been successfully added to Cassavabase and MGIS, and it can be integrated into any website or system with a BrAPI-compatible data source. +The Trait Selector BrAPP has been successfully added to Cassavabase[@doi:10.1093/g3journal/jkac078] and MGIS[@doi:10.1093/database/bax046], 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. diff --git a/content/03.02.01.DArT.md b/content/03.02.01.DArT.md index 61b578a..f02c885 100644 --- a/content/03.02.01.DArT.md +++ b/content/03.02.01.DArT.md @@ -1,8 +1,8 @@ #### 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. The ordering system is tightly integrated with DArTdb - DArT's custom LIMS operational system, which drives laboratory, quality, and analytical processes. +The [Diversity Arrays Technology (DArT)](https://www.diversityarrays.com/) 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. -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 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. Beyond sample submission, 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. +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. 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](https://ordering.diversityarrays.com) and also at [DArT Help](https://help.diversityarrays.com/docs/ordering). diff --git a/content/03.02.02.DArTView.md b/content/03.02.02.DArTView.md index 6d22358..d8ae68b 100644 --- a/content/03.02.02.DArTView.md +++ b/content/03.02.02.DArTView.md @@ -1,6 +1,6 @@ #### DArTView -[DArTView](https://software.kddart.com/kdxplore/dartview/dartviewdocs/KDXplore-DartView.html) is a desktop application for marker data curation via metadata filtering. DArTView enables genotype variant data visualization and users can easily identify trends or correlations within their data using the tool. Its primary goal 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. Users can now use any BrAPI compatible server as an input data source. BrAPI provides a consistent data standard across databases and data resources. DArTView's compatibility with BrAPI also ensures easy integration with other tools and pipelines that would use DArTView for marker filtering and exploration. +[DArTView](https://software.kddart.com/kdxplore/dartview/dartviewdocs/KDXplore-DartView.html) 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. -Initially developed by Diversity Arrays Technology (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. +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. diff --git a/content/03.02.03.DivBrowse.md b/content/03.02.03.DivBrowse.md index c0c02f5..d3bd6c9 100644 --- a/content/03.02.03.DivBrowse.md +++ b/content/03.02.03.DivBrowse.md @@ -4,10 +4,10 @@ [DivBrowse](https://divbrowse.ipk-gatersleben.de/) [@doi:10.1093/gigascience/giad025] 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. +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. 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. \ No newline at end of file +The modular structure of DivBrowse allows developers to configure and easily embed links to other external information systems. diff --git a/content/03.02.05.GIGWA.md b/content/03.02.05.GIGWA.md index 3172ede..9d8fa54 100644 --- a/content/03.02.05.GIGWA.md +++ b/content/03.02.05.GIGWA.md @@ -5,6 +5,6 @@ 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 [@doi:10.1093/bioinformatics/btq580]. 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](https://webtools.southgreen.fr/BrAPI/Beegmac/), [SnpClust](https://github.com/jframi/snpclust), [QBMS](https://github.com/icarda-git/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. +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. diff --git a/content/03.02.06.PHG.md b/content/03.02.06.PHG.md index 14b5786..f1661d8 100644 --- a/content/03.02.06.PHG.md +++ b/content/03.02.06.PHG.md @@ -1,6 +1,6 @@ #### PHG -The [Practical Haplotype Graph](https://www.maizegenetics.net/phg) (PHG) is a graph-based computational framework that represents large-scale genetic variation and is optimized for plant breeding and genetics [@doi:10.1093/bioinformatics/btac410]. 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. +The [Practical Haplotype Graph (PHG)](https://www.maizegenetics.net/phg) is a graph-based computational framework that represents large-scale genetic variation and is optimized for plant breeding and genetics [@doi:10.1093/bioinformatics/btac410]. 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. diff --git a/content/03.03.01.AGENT.md b/content/03.03.01.AGENT.md index 09d95f8..600b14b 100644 --- a/content/03.03.01.AGENT.md +++ b/content/03.03.01.AGENT.md @@ -3,7 +3,7 @@ Suggested Authors: Matthias Lange, Patrick König, Stephan Weise, Gouripriya Dav #### AGENT -In the global system for ex situ conservation of plant genetic resources (PGR) [@doi:10.3390/plants10081557], material is being conserved in about 1750 collections [@doi:10.2135/cropsci2017.01.0014] 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 System [@{https://glis.fao.org/glis/}] 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. 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 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](https://www.agent-project.eu/), 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 principles [@doi:10.1038/sdata.2016.18] 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 formats [@doi:10.12688/f1000research.109080.2] for the collection, integration, and archiving of genotypic and phenotypic data. +In the global system for ex situ conservation of plant genetic resources (PGR) [@doi:10.3390/plants10081557], material is being conserved in about 1750 collections [@doi:10.2135/cropsci2017.01.0014] 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 System [@{https://glis.fao.org/glis/}] 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](https://www.agent-project.eu/), 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 principles [@doi:10.1038/sdata.2016.18] 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 formats [@doi:10.12688/f1000research.109080.2] for the collection, integration, and archiving of genotypic and phenotypic data. The BrAPI specification is one of the agreed standards, that are detailed in the AGENT guidelines for dataflow [@doi:10.5281/zenodo.12625360]. 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](https://github.com/AGENTproject/BrAPI) and explorable in a [web portal](https://agent.ipk-gatersleben.de). Genotyping data uses the DivBrowse [@doi:10.1093/gigascience/giad025] 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 ICARDA [@doi:10.22004/AG.ECON.266624]. There is also a plan to integrate the data collected by the AGENT project into the European Search Catalogue for Plant Genetic Resources (EURISCO) [@doi:10.1093/nar/gkac852]. From 5ea70bbc42976bbec1cbfe4a8c8bf3084c824dac Mon Sep 17 00:00:00 2001 From: Asis Hallab Date: Thu, 18 Jul 2024 17:58:22 -0600 Subject: [PATCH 19/21] Added description and link to a BrAPI-Zendro datawarehouse with public Cassava Omics data --- content/03.06.03.Zendro.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/content/03.06.03.Zendro.md b/content/03.06.03.Zendro.md index 4521dc7..9cd68d3 100644 --- a/content/03.06.03.Zendro.md +++ b/content/03.06.03.Zendro.md @@ -5,6 +5,8 @@ Using the [Zendro](https://zendro-dev.github.io) set of automatic software code 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 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 [@https://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. +The Zendro data-warehouse is capable of forming an efficient cloud of data servers. This is achieved simply by linking 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. + -The Zendro data-warehouse is capable of forming an efficient cloud of data servers. This is achieved simply by linking 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. +We used parts of the public [CassavaBase](https://www.cassavabase.org/) data [@doi:10.1093/nar/gku1195] to create and populate a fully BrAPI compliant example data-warehouse based on Zendro. The warehouse is publically available [@https://brapi-gui.zendro-dev.org] and offers full read access both in the graphical user interface as well as through the GraphQL API. Three interactive scientific example plots comprise a boxplot comparing Cassava harvest indices measured for four different experiments. 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. From 26018ab6e3ff076aee82d88c4fc2c9a98d071f50 Mon Sep 17 00:00:00 2001 From: Peter Selby Date: Fri, 19 Jul 2024 13:59:55 -0400 Subject: [PATCH 20/21] general edits --- content/03.01.05.PHIS.md | 6 ++++++ content/03.01.07.Trait_Selector_BrAPP.md | 4 ++++ 2 files changed, 10 insertions(+) diff --git a/content/03.01.05.PHIS.md b/content/03.01.05.PHIS.md index e427a26..5f3531d 100644 --- a/content/03.01.05.PHIS.md +++ b/content/03.01.05.PHIS.md @@ -6,8 +6,14 @@ PHIS is deployed in several field and greenhouse platforms of the French nationa 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. +<<<<<<< HEAD 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](https://crb-plantes-olga.fr/public/frontend/auth/login), and is indexed by the [FAIDARE](https://urgi.versailles.inrae.fr/faidare/) data portal [@https://urgi.versailles.inrae.fr/faidare]. 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 ([MIAPPE](https://www.miappe.org/) [@doi:10.1111/nph.16544]). 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. +======= +PHIS was designed to be BrAPI compliant since its inception. 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, such as the genebank accessions management system [OLGA](https://crb-plantes-olga.fr/public/frontend/auth/login), to retrieve accession information. 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)](https://www.miappe.org/) [@doi:10.1111/nph.16544]. 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 enables indexing of PHIS in the [FAIDARE](https://urgi.versailles.inrae.fr/faidare/) data portal [@https://hal.inrae.fr/hal-04425516]. + +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 adhesion 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. +>>>>>>> 0b95ffa (general edits) diff --git a/content/03.01.07.Trait_Selector_BrAPP.md b/content/03.01.07.Trait_Selector_BrAPP.md index 30fe84b..c0394ff 100644 --- a/content/03.01.07.Trait_Selector_BrAPP.md +++ b/content/03.01.07.Trait_Selector_BrAPP.md @@ -4,10 +4,14 @@ +<<<<<<< HEAD The [Trait Selector BrAPP](https://github.com/solgenomics/BrAPI-Trait-selector) 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. +======= +The [Trait Selector BrAPP](https://github.com/solgenomics/BrAPI-Trait-selector) is used to search and select meaningful traits, with 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. +>>>>>>> 0b95ffa (general edits) The Trait Selector BrAPP has been successfully added to Cassavabase[@doi:10.1093/g3journal/jkac078] and MGIS[@doi:10.1093/database/bax046], 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. From 15881d644460f24d5529f3a5b3c6195e1b844194 Mon Sep 17 00:00:00 2001 From: Peter Selby Date: Mon, 22 Jul 2024 10:33:58 -0400 Subject: [PATCH 21/21] Zendro edits --- content/03.01.07.Trait_Selector_BrAPP.md | 4 ---- content/03.06.03.Zendro.md | 8 +++----- 2 files changed, 3 insertions(+), 9 deletions(-) diff --git a/content/03.01.07.Trait_Selector_BrAPP.md b/content/03.01.07.Trait_Selector_BrAPP.md index c0394ff..30fe84b 100644 --- a/content/03.01.07.Trait_Selector_BrAPP.md +++ b/content/03.01.07.Trait_Selector_BrAPP.md @@ -4,14 +4,10 @@ -<<<<<<< HEAD The [Trait Selector BrAPP](https://github.com/solgenomics/BrAPI-Trait-selector) 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. -======= -The [Trait Selector BrAPP](https://github.com/solgenomics/BrAPI-Trait-selector) is used to search and select meaningful traits, with 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. ->>>>>>> 0b95ffa (general edits) The Trait Selector BrAPP has been successfully added to Cassavabase[@doi:10.1093/g3journal/jkac078] and MGIS[@doi:10.1093/database/bax046], 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. diff --git a/content/03.06.03.Zendro.md b/content/03.06.03.Zendro.md index 9cd68d3..d2eede8 100644 --- a/content/03.06.03.Zendro.md +++ b/content/03.06.03.Zendro.md @@ -1,12 +1,10 @@ #### GraphQL Data-warehouse -Using the [Zendro](https://zendro-dev.github.io) 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. +Using the [Zendro](https://zendro-dev.github.io) 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. 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 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 [@https://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. - -The Zendro data-warehouse is capable of forming an efficient cloud of data servers. This is achieved simply by linking 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. +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. -We used parts of the public [CassavaBase](https://www.cassavabase.org/) data [@doi:10.1093/nar/gku1195] to create and populate a fully BrAPI compliant example data-warehouse based on Zendro. The warehouse is publically available [@https://brapi-gui.zendro-dev.org] and offers full read access both in the graphical user interface as well as through the GraphQL API. Three interactive scientific example plots comprise a boxplot comparing Cassava harvest indices measured for four different experiments. 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. +An [example data warehouse](https://brapi-gui.zendro-dev.org) 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](https://www.cassavabase.org/) data [@doi:10.1093/nar/gku1195] 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.