diff --git a/content/03.01.01.Field_Book.md b/content/03.01.01.Field_Book.md index 9d8e808..3ebb045 100644 --- a/content/03.01.01.Field_Book.md +++ b/content/03.01.01.Field_Book.md @@ -3,6 +3,6 @@ Phenotypic data collection underpins scientific crop research and plant breeding. Knowledge gained from collected data and its analysis, alongside data visualizations, inform further phenotypic trials and ideally support research hypotheses. The importance of accuracy and efficiency in the collection of this data as well as the infrastructure to facilitate the flow of data from the field to a knowledge base cannot be underestimated. -Historically, gathering data in the field was done with pen and paper, or perhaps some version of a digital spreadsheet. The abundance and prevalence of smart phones has allowed the Field Book mobile app [@doi:10.2135/cropsci2013.08.0579] to enhance data collection. Field Book can create well-formed digital observation records from the moment they are taken. This can improve the efficiency of data collection and reduce human error. +[Field Book](fieldbook.phenoapps.org/) [@doi:10.2135/cropsci2013.08.0579] is an android based mobile app for collecting field data. Historically, gathering data in the field was done with pen and paper, or perhaps some version of a digital spreadsheet. The abundance and prevalence of smart phones has allowed Field Book to enhance data collection. Field Book can create well-formed digital observation records from the moment they are taken. This can improve the efficiency of data collection and reduce human error. In 2018, BrAPI was introduced into Field Book, allowing for the automated the flow of data from the mobile app into a central, BrAPI compatible, database server. This workflow allows data collection and storage to be expedited, removing the need of the user to export and transfer data files manually. Since Field Book’s adoption of BrAPI, many community servers have been integrated to simplify data storage. In this work flow, data is collected and stored completely digitally with little-to-no human involvement. diff --git a/content/03.01.03.ClimMob.md b/content/03.01.03.ClimMob.md index a541c88..3f5984f 100644 --- a/content/03.01.03.ClimMob.md +++ b/content/03.01.03.ClimMob.md @@ -2,6 +2,6 @@ -ClimMob [@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. +[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. 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. diff --git a/content/03.01.04.Image_Breed.md b/content/03.01.04.Image_Breed.md index e2c63dd..479b4a4 100644 --- a/content/03.01.04.Image_Breed.md +++ b/content/03.01.04.Image_Breed.md @@ -1,5 +1,5 @@ #### ImageBreed -High-throughput phenotyping has been gaining significant traction lately as a way to collect lots of data very quickly. Image collection from unmanned aerial and ground vehicles (UAVs and UGVs) are a great way to collect a lot of raw data all at once, then analyze it later. ImageBreed [@doi:10.1002/ppj2.20004] is an image collection pipeline tool to support regular use of UAVs and UGVs. +[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. diff --git a/content/03.01.05.PHIS.md b/content/03.01.05.PHIS.md index a91a6a3..524933e 100644 --- a/content/03.01.05.PHIS.md +++ b/content/03.01.05.PHIS.md @@ -1,7 +1,7 @@ #### PHIS -The Hybrid Phenotyping Information System ([PHIS](http://www.phis.inrae.fr/) [@doi:10.1111/nph.15385]), based on the [OpenSILEX](https://github.com/OpenSILEX/) framework, is an ontology-driven information system based on semantic web technologies. PHIS is deployed in several field and greenhouse platforms of the French national [PHENOME](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, 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 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 [FAIDARE](https://urgi.versailles.inrae.fr/faidare/) [@https://urgi.versailles.inrae.fr/faidare]. diff --git a/content/03.01.07.Trait_Selector_BrAPP.md b/content/03.01.07.Trait_Selector_BrAPP.md index b28c47d..7de5f58 100644 --- a/content/03.01.07.Trait_Selector_BrAPP.md +++ b/content/03.01.07.Trait_Selector_BrAPP.md @@ -2,10 +2,10 @@ -BrAPPs are simple tools developed by the BrAPI community that are entirely reliant on BrAPI for their data requirements. Often, they are JavaScript based applications or visualizations that fit on a single web page. This means a single BrAPP can be easily shared and used by many organizations and systems, as long as those organizations have the standard BrAPI endpoints available. + -The Trait Selector BrAPP is used to search and select useful traits, using a visual aid to help the user find exactly what they need. Instead of searching through a long list of possible traits, the user is presented with a cartoon image of a species. They can then click on pieces of the image to show traits associated to that part of the plant. For a breeder, they might use it to quickly find specific traits to study. For a genebank user, they might use it to find varieties that have a specific trait they are interested in. +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. -Due to the nature of BrAPPs, the Trait Selector can be integrated into any website or system, assuming there is a BrAPI compatible data source available to connect to. A breeding database would need to only implement the BrAPI endpoints for Traits, Observations, and Variables, while a genebank would require Traits and Germplasm Attributes. Any BrAPI server with either of these sets of endpoints implemented could use this BrAPP. CassavaBase and MGIS are two successful examples of the Trait Selector BrAPP in use. +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. diff --git a/content/03.02.02.DArTView.md b/content/03.02.02.DArTView.md index 899d4f5..6d22358 100644 --- a/content/03.02.02.DArTView.md +++ b/content/03.02.02.DArTView.md @@ -1,6 +1,6 @@ #### DArTView -DArTView 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 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. 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. diff --git a/content/03.02.03.DivBrowse.md b/content/03.02.03.DivBrowse.md index e6df263..6f89cf0 100644 --- a/content/03.02.03.DivBrowse.md +++ b/content/03.02.03.DivBrowse.md @@ -1,7 +1,7 @@ #### DivBrowse -DivBrowse [@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 huge genotyping studies. The software can be run standalone or integrated as a plugin into existing data web portals. It provides a powerful interactive visualization of variant call matrices with hundreds of millions of variants and thousands of samples. It enables easy data import and export by using well established, standardized, bioinformatics file formats. At its core, DivBrowse combines the convenience of a genome browser with features tailored to the diversity analysis of germplasm. It is able to display genomic features such as nucleotide sequence, associated gene models, and short genomic variants. DivBrowse provides visual access to large VCF files obtained through genotyping experiments. In addition, 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. diff --git a/content/03.02.04.GIGWA.md b/content/03.02.04.GIGWA.md index 07926c8..3172ede 100644 --- a/content/03.02.04.GIGWA.md +++ b/content/03.02.04.GIGWA.md @@ -1,7 +1,7 @@ #### Gigwa -Gigwa is a Java EE web application providing a means to centralize, share, finely filter, and visualize high-throughput genotyping data [@doi:10.1093/gigascience/giz051]. Built on top of MongoDB, it is scalable and can support working smoothly with datasets containing billions of genotypes. It is installable as a Docker image or as an all-in-one bundle archive. It is straightforward to deploy on servers or local computers and has thus been adopted by numerous research institutes from around the world. Notably, Gigwa serves as a collaborative management tool and a portal for exposing public data for genebanks and breeding programs at some CGIAR centers [@doi:10.1002/ppp3.10187]. The total amount of data hosted and made widely accessible using this system has continued to grow over the last few years. +[Gigwa](https://southgreen.fr/content/gigwa) is a Java EE web application providing a means to centralize, share, finely filter, and visualize high-throughput genotyping data [@doi:10.1093/gigascience/giz051]. Built on top of MongoDB, it is scalable and can support working smoothly with datasets containing billions of genotypes. It is installable as a Docker image or as an all-in-one bundle archive. It is straightforward to deploy on servers or local computers and has thus been adopted by numerous research institutes from around the world. Notably, Gigwa serves as a collaborative management tool and a portal for exposing public data for genebanks and breeding programs at some CGIAR centers [@doi:10.1002/ppp3.10187]. The total amount of data hosted and made widely accessible using this system has continued to grow over the last few years. The Gigwa development team has been involved in the BrAPI community since 2016 and took part in designing the genotype-related section of the BrAPI standard. Gigwa's first BrAPI-compliant features were designed for compatibility with the Flapjack visualization tool [@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)). diff --git a/content/03.02.05.PHG.md b/content/03.02.05.PHG.md index 081ab0a..14b5786 100644 --- a/content/03.02.05.PHG.md +++ b/content/03.02.05.PHG.md @@ -1,6 +1,6 @@ #### PHG -The Practical Haplotype Graph (PHG) is a graph-based computational framework that represents large-scale genetic variation and is optimized for plant breeding and genetics. Using a pangenome approach, each PHG stores haplotypes (the sequence of part of an individual chromosome) to represent the collected genes of a species. This allows for a simplified approach for dealing with large scale variation in plant genomes. The PHG pipeline provides support for a range of genomic analyses and allows for the use of graph data to impute complete genomes from low density sequence or variant data. +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. 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/03.03.02.AGENT_Portal.md b/content/03.03.01.AGENT.md similarity index 100% rename from content/03.03.02.AGENT_Portal.md rename to content/03.03.01.AGENT.md diff --git a/content/03.03.01.MGIS.md b/content/03.03.02.MGIS.md similarity index 100% rename from content/03.03.01.MGIS.md rename to content/03.03.02.MGIS.md diff --git a/content/03.04.01.DeltaBreed.md b/content/03.04.01.DeltaBreed.md index f75e7e3..b420652 100644 --- a/content/03.04.01.DeltaBreed.md +++ b/content/03.04.01.DeltaBreed.md @@ -1,7 +1,7 @@ #### DeltaBreed -DeltaBreed is an open-source breeding data management system designed and developed by Breeding Insight to support USDA-ARS specialty crop and animal breeders. DeltaBreed differs from other related systems in that is customizable to small breeding teams and generalized enough to support the workflows of diverse niche species. DeltaBreed is a unified system that connects a variety of BrAPI applications (see list below). BrAPI integration allows the complexity underlying interoperability to be hidden, shielding users from multifactorial differences between various applications. DeltaBreed, adhering to the BrAPI model, establishes data standards and validations for users and provides a singular framework for data management and user training. +[DeltaBreed](https://app.breedinginsight.net/) is an open-source breeding data management system designed and developed by Breeding Insight to support USDA-ARS specialty crop and animal breeders. DeltaBreed differs from other related systems in that is customizable to small breeding teams and generalized enough to support the workflows of diverse niche species. DeltaBreed is a unified system that connects a variety of BrAPI applications (see list below). BrAPI integration allows the complexity underlying interoperability to be hidden, shielding users from multifactorial differences between various applications. DeltaBreed, adhering to the BrAPI model, establishes data standards and validations for users and provides a singular framework for data management and user training. 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. diff --git a/content/03.04.03.Breedbase.md b/content/03.04.03.Breedbase.md index f719d58..69531e7 100644 --- a/content/03.04.03.Breedbase.md +++ b/content/03.04.03.Breedbase.md @@ -1,6 +1,6 @@ #### Breedbase -Breedbase is a comprehensive breeding data management system [@doi:10.1093/g3journal/jkac078;@doi:10.1371/journal.pone.0240059] that implements a digital ecosystem for all breeding data, including trial data, phenotypic data, and genotypic data. Data acquisition is supported through data collection apps such as Fieldbook [@doi:10.2135/cropsci2013.08.0579], Coordinate, and InterCross, as well as through drone imagery, Near Infra-Red Spectroscopy (NIRS), and other technologies. Search functions, such as the Search Wizard interface, provide powerful query capabilities. Various breeding-centric analysis tools are available, including mixed models, heritability, stability, PCA, and various clustering algorithms. The original impetus for creating Breedbase was the advent of new breeding paradigms based on genomic information such as genomic prediction algorithms [@doi:10.1093/genetics/157.4.1819] and the accompanying data management challenges. Thus, complete genomic prediction workflow is integrated in the system. +[Breedbase](https://breedbase.org/) is a comprehensive breeding data management system [@doi:10.1093/g3journal/jkac078;@doi:10.1371/journal.pone.0240059] that implements a digital ecosystem for all breeding data, including trial data, phenotypic data, and genotypic data. Data acquisition is supported through data collection apps such as Fieldbook [@doi:10.2135/cropsci2013.08.0579], Coordinate, and InterCross, as well as through drone imagery, Near Infra-Red Spectroscopy (NIRS), and other technologies. Search functions, such as the Search Wizard interface, provide powerful query capabilities. Various breeding-centric analysis tools are available, including mixed models, heritability, stability, PCA, and various clustering algorithms. The original impetus for creating Breedbase was the advent of new breeding paradigms based on genomic information such as genomic prediction algorithms [@doi:10.1093/genetics/157.4.1819] and the accompanying data management challenges. Thus, complete genomic prediction workflow is integrated in the system. The BrAPI interface is crucial for Breedbase. Breedbase uses BrAPI to connect with the data collection apps, other projects such as CLIMMOB [@doi:10.1016/j.compag.2023.108539], and native BrAPPs built into the Breedbase webpage. Users also appreciate the ability to connect to Breedbase instances using packages such as [QBMS](https://icarda-git.github.io/QBMS) [@doi:10.5281/zenodo.10791627] for data import into R for custom analyses. The Breedbase team has been part of the BrAPI community since its inception, and has continuously adopted and contributed to the BrAPI standard. diff --git a/content/03.04.04.BIMS.md b/content/03.04.04.BIMS.md index cce50aa..4172423 100644 --- a/content/03.04.04.BIMS.md +++ b/content/03.04.04.BIMS.md @@ -1,6 +1,6 @@ #### BIMS -BIMS (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. +[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. 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. diff --git a/content/03.05.02.Mr_Bean.md b/content/03.05.02.Mr_Bean.md index 04a1ed5..748ca27 100644 --- a/content/03.05.02.Mr_Bean.md +++ b/content/03.05.02.Mr_Bean.md @@ -1,6 +1,6 @@ #### Mr.Bean -Mr.Bean [@doi:10.3389/fpls.2023.1290078] is a graphical user interface designed to assist breeders, statisticians, and individuals involved in plant breeding programs with the analysis of field trials. By utilizing innovative methodologies such as SpATS for modeling spatial trends, and autocorrelation models to address spatial variability, Mr.Bean proves highly practical and powerful in facilitating faster and more effective decision-making. Modeling Genotype-by-environment interaction poses its challenges, but Mr.Bean offers the capability to explore various variance-covariance matrices, including Factor Analytic, compound symmetry, and heterogeneous variances. This aids in the assessment of genotype performance across diverse environments. +[Mr.Bean](https://apariciojohan.github.io/MrBeanApp/) [@doi:10.3389/fpls.2023.1290078] is a graphical user interface designed to assist breeders, statisticians, and individuals involved in plant breeding programs with the analysis of field trials. By utilizing innovative methodologies such as SpATS for modeling spatial trends, and autocorrelation models to address spatial variability, Mr.Bean proves highly practical and powerful in facilitating faster and more effective decision-making. Modeling Genotype-by-environment interaction poses its challenges, but Mr.Bean offers the capability to explore various variance-covariance matrices, including Factor Analytic, compound symmetry, and heterogeneous variances. This aids in the assessment of genotype performance across diverse environments. Mr.Bean boasts flexibility in importing different file types, yet for users managing their data within data management systems (DMS), the process of downloading from their DMS and importing it into Mr.Bean can be cumbersome. To address this issue, QBMS was integrated into the back-end. This feature prompts users to input the URL of a BrAPI compatible server, enter their credentials (if necessary), and select the specific trial they wish to analyze. Subsequently, users can seamlessly access their dataset through BrAPI and utilize it across the entire Mr.Bean interface. diff --git a/content/03.05.05.SCT.md b/content/03.05.05.SCT.md index a878b76..b0d6d8f 100644 --- a/content/03.05.05.SCT.md +++ b/content/03.05.05.SCT.md @@ -1,7 +1,7 @@ #### SCT -The Sugarcane Crossing Tool (SCT) is a lightweight RShiny dashboard application designed to receive, process, and visualize data from a linked BreedBase [@doi:10.1093/g3journal/jkac078] instance. This application is being developed collaboratively with members of the [Sugarcane Integrated Breeding System](https://www.amscl.org/sugarcane-integrated-breeding-system/), who have advocated for an application that assists them in designing crosses based on queried information from a list of available accessions. By leveraging existing community resources, the team has been able to develop a simple, BrAPI-enabled, application without possessing extensive programming knowledge or experience. The SCT is presented as an inspiration for similarly positioned scientists to consider developing custom applications for specific tasks. +The [Sugarcane Crossing Tool](https://github.com/USDA-ARS-GBRU/SugarcaneCrossingTool) (SCT) is a lightweight RShiny dashboard application designed to receive, process, and visualize data from a linked BreedBase [@doi:10.1093/g3journal/jkac078] instance. This application is being developed collaboratively with members of the [Sugarcane Integrated Breeding System](https://www.amscl.org/sugarcane-integrated-breeding-system/), who have advocated for an application that assists them in designing crosses based on queried information from a list of available accessions. By leveraging existing community resources, the team has been able to develop a simple, BrAPI-enabled, application without possessing extensive programming knowledge or experience. The SCT is presented as an inspiration for similarly positioned scientists to consider developing custom applications for specific tasks. diff --git a/content/03.06.01.MIAPPE_MIRA.md b/content/03.06.01.MIAPPE_MIRA.md index 14572d9..c0337d9 100644 --- a/content/03.06.01.MIAPPE_MIRA.md +++ b/content/03.06.01.MIAPPE_MIRA.md @@ -3,4 +3,4 @@ In the plant phenotyping community, [MIAPPE](https://www.miappe.org/) (Minimal Information About a Plant Phenotyping Experiment) [@doi:10.1111/nph.16544] is an established standard for phenotyping experiments. It is commonly serialized into the ISA-Tab file type. [@doi:10.1038/ng.1054] Although ISA-Tab is easy to read for non-technical experts due to its file-based approach, it lacks programmatic accessibility, particularly for web applications. BrAPI, which is aligned with MIAPPE, can help solve this problem. -MIRA is a tool that enables the automatic deployment of a BrAPI server on a MIAPPE-compliant dataset in ISA-Tab format. It can be deployed from a Docker image with the dataset mounted. By utilizing the mapping between MIAPPE, ISA-Tab, and BrAPI, there is no need for parsing or manual mapping of datasets that are already compliant with (meta-)data standards. By gaining programmatic access through BrAPI to these datasets, it facilitates the integration of phenotyping datasets into web applications. +[MIRA](https://github.com/USDA-ARS-GBRU/SugarcaneCrossingTool) is a tool that enables the automatic deployment of a BrAPI server on a MIAPPE-compliant dataset in ISA-Tab format. It can be deployed from a Docker image with the dataset mounted. By utilizing the mapping between MIAPPE, ISA-Tab, and BrAPI, there is no need for parsing or manual mapping of datasets that are already compliant with (meta-)data standards. By gaining programmatic access through BrAPI to these datasets, it facilitates the integration of phenotyping datasets into web applications. diff --git a/content/03.06.03.BrAPIMapper.md b/content/03.06.03.BrAPIMapper.md index c9ac0e3..0119170 100644 --- a/content/03.06.03.BrAPIMapper.md +++ b/content/03.06.03.BrAPIMapper.md @@ -1,4 +1,4 @@ #### BrAPIMapper -BrAPIMapper is a full BrAPI implementation designed to be a convenient wrapper for any breeding related data source. BrAPIMapper is provided as a Docker application that can connect to a variety of external data sources including mySQL or PostgreSQL databases, generic REST services, flat files (XML, JSON, CSV/TSV/GFF3/VCF, YAML), or any combination of these. It provides an administration user interface to map BrAPI data models to external data sources. The interface allows administrators to select the BrAPI specification versions to use and which endpoints to enable. Data mapping configuration import and export features simplify upgrades to future BrAPI versions; administrators only have to map missing fields or make minor adjustments. BrAPIMapper supports the primary BrAPI features including paging, deferred search results, user lists, and authentication. Access restrictions to specific endpoints can be managed through the administration interface as well. This tool aims to accelerate BrAPI services deployment while ensuring specification compliance. +[BrAPIMapper](https://github.com/plantbreeding/BrAPIMapper) is a full BrAPI implementation designed to be a convenient wrapper for any breeding related data source. BrAPIMapper is provided as a Docker application that can connect to a variety of external data sources including mySQL or PostgreSQL databases, generic REST services, flat files (XML, JSON, CSV/TSV/GFF3/VCF, YAML), or any combination of these. It provides an administration user interface to map BrAPI data models to external data sources. The interface allows administrators to select the BrAPI specification versions to use and which endpoints to enable. Data mapping configuration import and export features simplify upgrades to future BrAPI versions; administrators only have to map missing fields or make minor adjustments. BrAPIMapper supports the primary BrAPI features including paging, deferred search results, user lists, and authentication. Access restrictions to specific endpoints can be managed through the administration interface as well. This tool aims to accelerate BrAPI services deployment while ensuring specification compliance. diff --git a/content/04.discussion.md b/content/04.discussion.md index db95643..bae60f1 100644 --- a/content/04.discussion.md +++ b/content/04.discussion.md @@ -12,16 +12,16 @@ ### BrAPI for Breeders and Scientists -The BrAPI technical specification document is meant to be read and used by software developers. However, the purpose of the specification, and the community around it, is to make things faster, easier, and cheaper for the breeders and scientists working on breeding and other agricultural projects. BrAPI offers a convenient path to automation, interoperability, and data integration for software tools in the breeding domain. All of the example use cases described above can be achieved with manual effort, moving and editing data files by hand. However, when the basic structure and flow of data becomes automated, breeders and scientists can spend less time on data management and more time focusing on the science. For many, the ultimate goal is the development of a digital ecosystem: a collection of software tools and applications that can all work together seamlessly. In this digital ecosystem, data is collected digitally from the beginning, reducing as much human error as possible. The data is checked by quality control and stored automatically, then it can be sent to any internal tool or external lab for further analysis with just the click of a button. This idea might sound too good to be true, but as more tools start sharing a universal data standard, automating data flow becomes easier, and the community gets closer to total interoperability. +The BrAPI technical specification document is meant to be read and used by software developers. However, the purpose of the specification, and the community around it, is to make things faster, easier, and cheaper for the breeders and scientists working on breeding and other agricultural projects. BrAPI offers a convenient path to automation, interoperability, and data integration for software tools in the breeding domain. All of the software described above could be made interoperable with manual effort, moving and editing data files by hand from tool to tool. However, when the basic structure and flow of data becomes automated, breeders and scientists can spend less time on data management and more time focusing on the science. For many, the ultimate goal is the development of a digital ecosystem: a collection of software tools and applications that can all work together seamlessly. In this digital ecosystem, data is collected digitally from the beginning, reducing as much human error as possible. The data is checked by quality control and stored automatically, then it can be sent to any internal tool or external lab for further analysis with just the click of a button. This idea might sound too good to be true, but as more tools start sharing a universal data standard, automating data flow becomes easier, and the community gets closer to total interoperability. ### Looking Ahead -The BrAPI specification will continue to grow, enabling more use cases and new types of data. These new use cases might include newer scientific techniques and technologies. Things like drone imaging data, spectroscopy, LIDAR, metabolomics, transcriptomics, high-throughput phenotyping, and machine learning analysis. All of these technologies can open new avenues for research and development of new crop varieties. All of these technologies also generate more data, and require data sharing between different software applications and data repositories. The BrAPI project leadership and community is committed to building the standards to support these new use cases as they arrive and become accepted by the scientific community. In fact, small groups within the BrAPI community have already start building generic data models and proposed communication standards for many of the technologies listed above. These community efforts will eventually become part of the BrAPI standard in a future version of the specification document. +The BrAPI specification will continue to grow, enabling more use cases and new types of data. These new use cases might include newer scientific techniques and technologies. Things like drone imaging data, spectroscopy, LIDAR, metabolomics, transcriptomics, high-throughput phenotyping, pan genomes, and machine learning analysis. All of these technologies can open new avenues for research and development of new crop varieties. All of these technologies also generate more data, and require data sharing between different software applications and data repositories. The BrAPI project leadership and community is committed to building the standards to support these new use cases as they arrive and become accepted by the scientific community. In fact, small groups within the BrAPI community have already start building generic data models and proposed communication standards for many of the technologies listed above. These community efforts will eventually become part of the BrAPI standard in a future version of the specification document. -Expanding the BrAPI specification is important for the community, however it is just as important not to reinvent or compete with existing functional standards. Additions to the BrAPI specification are reviewed thoroughly by the community to make sure BrAPI is compliant with existing standards and data structures. For example, BrAPI is compliant with MIAPPE, MCPD, and VCF, adding pieces of these existing popular data structures into the overall standard. In some cases, BrAPI will reference other standards instead of including them in the specification. For example, the NOAA CDO standard for weather data, or the Galaxy Analytics API for analytics pipeline controls and information. These standards are perfectly adequate on their own, and recreating them in the BrAPI standard would be redundant. +Expanding the BrAPI specification is important for the community, however it is just as important not to reinvent or compete with existing functional standards. Additions to the BrAPI specification are reviewed thoroughly by the community to make sure BrAPI is compliant with existing standards and data structures. For example, the community has requested compliance with the GFF3 standard for genomic data and the GeoTIFF standard for aerial image data. Pieces of these existing popular data structures might be integrated into the overall BrAPI standard documentation. In some cases, BrAPI will only reference other standards instead of including them in the specification. For example, there have been community discussions around developing connections with the NOAA CDO standard for weather data, or the Galaxy Analytics API for analytics pipeline controls and information. These standards are perfectly adequate on their own, recreating them in the BrAPI standard would be redundant and outside the main scope of the project. ### Conclusion -The BrAPI project only exists because of the community of breeders, software engineers, and 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). diff --git a/content/images/BrAPI_Application_Chart.xlsx b/content/images/BrAPI_Application_Chart.xlsx index f978186..c0cbed3 100644 Binary files a/content/images/BrAPI_Application_Chart.xlsx and b/content/images/BrAPI_Application_Chart.xlsx differ diff --git a/content/images/BrAPI_Application_Chart_2.log b/content/images/BrAPI_Application_Chart_2.log new file mode 100644 index 0000000..26483b8 --- /dev/null +++ b/content/images/BrAPI_Application_Chart_2.log @@ -0,0 +1,11 @@ +%%[ ProductName: Distiller ]%% +Aptos-Narrow-Bold not found, using Courier. +%%[ Error: invalidfont; OffendingCommand: yshow ]%% + +Stack: +[90 71 74 71 47 70 45 35 73 74 0] +( ) + + +%%[ Flushing: rest of job (to end-of-file) will be ignored ]%% +%%[ Warning: PostScript error. 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