guignonv
-The Alliance of Bioversity International and CIAT (CGIAR)
+Bioversity International, Parc Scientifique Agropolis II, 34397 Montpellier, France
+
+
Mathieu Rouard
+
+
+0000-0003-0284-1885
+·
+mrouard
+
+
+Bioversity International, Parc Scientifique Agropolis II, 34397 Montpellier, France
Asis Hallab
@@ -694,6 +708,9 @@
Germinate
Germinate[8] 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 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, but, 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 integrity.
+
MGIS
+
+
The Musa Germplasm information system, MGIS, serves as a comprehensive community portal dedicated to banana diversity, a crop critical to global food security [9]. MGIS offers detailed information on banana germplasm, focusing on the collections held by the CGIAR International Banana Genebank (ITC) [10]. It is built on the Build on the Drupal/Tripal technology, like BIMS and Florilège. Since its inception, MGIS developers have actively participated in the Breeding API (BrAPI) community, pushing for the integration of Multicrop Passport Data (MCPD) into Germplasm module call of the API. MGIS thus provides passport data information on ITC banana genebank accessions (with GLIS DOI), synchronized with Genesys, but also enriches it by incorporating additional data from other germplasm collections worldwide. All those germplasm data are available through BrAPI germplasm module calls implementations. For genotyping data, MGIS incorporates GIGWA [11], which provides tailored implementations for BrAPI genotyping module calls. Furthermore, MGIS supports the implementation of a set of BrAPI phenotyping module calls, facilitating the exposing of morphological descriptors and trait information supported by ontologies like the Crop Ontology [12]. It is integrated with the Trait Selector BrAPP, developed as part of a project involving Breedbase [13]. Uses cases between the Musa implementation of Breedbase, MusaBase, and MGIS to interlink genebank and breeding data.
Federated Data Management Infrastructures
AGENT Portal
-
In the global system for ex situ conservation of plant genetic resources (PGR) [9], a total of ~5.8 million accessions are conserved in 1750 ex situ genebanks [10]. Unique and permanent identifiers in the form of DOIs are available for more than 1.7 million accessions [doi:Food? and Agriculture Organization (FAO) The Global Information System for PGRFA]. Each DOI is linked to some basic descriptive data that facilitates the use of these resources. Many DOIs are also linked to additional data from different domains or will be in the future. In order to answer questions on the global biological diversity of a plant species, on duplicate detection, on provenance tracking for the identification of genetic integrity, on the selection of the most suitable material for various purposes, including breeding and research, and to support further applications in data mining or AI, a data space beyond the most basic information is needed that includes genotypic and phenotypic data. 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 GenRes data space via European ex situ genebanks according to the FAIR criteria [11] and to test it in practice using two important crops, barley and wheat. In two work packages, standards and technology for data interoperability will be developed to establish a genetic resources infrastructure, which regulates data acquisition of genotypic and phenotypic data, integrates and archives them and makes them accessible according to FAIR principles. To this end, 13 European genebanks and 5 bioinformatics centers are cooperating and have agreed on standards and protocols for (i) the data flow (see figure 3) and data formats [12] for central archiving of genotypic and phenotypic data.
+
In the global system for ex situ conservation of plant genetic resources (PGR) [14], a total of ~5.8 million accessions are conserved in 1750 ex situ genebanks [15]. Unique and permanent identifiers in the form of DOIs are available for more than 1.7 million accessions [doi:Food? and Agriculture Organization (FAO) The Global Information System for PGRFA]. Each DOI is linked to some basic descriptive data that facilitates the use of these resources. Many DOIs are also linked to additional data from different domains or will be in the future. In order to answer questions on the global biological diversity of a plant species, on duplicate detection, on provenance tracking for the identification of genetic integrity, on the selection of the most suitable material for various purposes, including breeding and research, and to support further applications in data mining or AI, a data space beyond the most basic information is needed that includes genotypic and phenotypic data. 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 GenRes data space via European ex situ genebanks according to the FAIR criteria [16] and to test it in practice using two important crops, barley and wheat. In two work packages, standards and technology for data interoperability will be developed to establish a genetic resources infrastructure, which regulates data acquisition of genotypic and phenotypic data, integrates and archives them and makes them accessible according to FAIR principles. To this end, 13 European genebanks and 5 bioinformatics centers are cooperating and have agreed on standards and protocols for (i) the data flow (see figure 3) and data formats [17] for central archiving of genotypic and phenotypic data.
@@ -720,16 +737,19 @@
AGENT Portal
Figure 4: Figure AGENT Portal
-
The AGENT database backend aggregates curated and integrated passport data, phenotypic and genotypic data about wheat and barley accessions of 18 project partners are harmonized and integrated via BrAPI endpoints (https://github.com/AGENTproject/BrAPI) and explorable in a web portal (https://agent.ipk-gatersleben.de). The BrAPI endpoints were made available by scattered implementation. Genotyping data use DivBrowse [13] storage engine and BrAPI interface. Endpoints for sample data are implemented using AGENT database SQL to BrAPI broker service.
-To integrate those BrAPI endpoint provider into a single service and URL scheme, we work on their integration in a BrAPI proxy service. As next steps, we will expand BrAPI implementation to enable the integration of analysis pipelines in the AGENT portal, e.g. for genebank mining tools such as the FIGS+ pipeline developed by AGENT partner ICARDA [14]. Another perspective is to integrate the data collected in the AGENT project into the European Search Catalogue for Plant Genetic Resources (EURISCO) [15] and to implement BrAPI endpoints to make data on PGR collections in European genebanks programmatically accessible.
+
The AGENT database backend aggregates curated and integrated passport data, phenotypic and genotypic data about wheat and barley accessions of 18 project partners are harmonized and integrated via BrAPI endpoints (https://github.com/AGENTproject/BrAPI) and explorable in a web portal (https://agent.ipk-gatersleben.de). The BrAPI endpoints were made available by scattered implementation. Genotyping data use DivBrowse [18] storage engine and BrAPI interface. Endpoints for sample data are implemented using AGENT database SQL to BrAPI broker service.
+To integrate those BrAPI endpoint provider into a single service and URL scheme, we work on their integration in a BrAPI proxy service. As next steps, we will expand BrAPI implementation to enable the integration of analysis pipelines in the AGENT portal, e.g. for genebank mining tools such as the FIGS+ pipeline developed by AGENT partner ICARDA [19]. Another perspective is to integrate the data collected in the AGENT project into the European Search Catalogue for Plant Genetic Resources (EURISCO) [20] and to implement BrAPI endpoints to make data on PGR collections in European genebanks programmatically accessible.
IPK-Genebank
Agrosystem Integration of germplasm collections in context of data trustee models among private economy and public research, integration of ex-situ genebanks (EU H2020 projects AGENT, INCREASING), integrated agrosystems and plant research infrastructure
MIAPPE ISA to BrAPI service
-
Phenotyping is crucial in the breeding process as it enables the identification of desirable traits, selection of breeding lines, and evaluation of breeding success. In the plant community, MIAPPE (Minimal Information About a Plant Phenotyping Experiment) [16] is the established standard for phenotyping experiments and is commonly serialized as ISA Tab [17]. Although ISA Tab is easy to read for non-technical experts due to its file-based approach, it lacks programmatic access, particularly for web applications. BrAPI, which is aligned with MIAPPE, can help solve this problem.
+
Phenotyping is crucial in the breeding process as it enables the identification of desirable traits, selection of breeding lines, and evaluation of breeding success. In the plant community, MIAPPE (Minimal Information About a Plant Phenotyping Experiment) [21] is the established standard for phenotyping experiments and is commonly serialized as ISA Tab [22]. Although ISA Tab is easy to read for non-technical experts due to its file-based approach, it lacks programmatic access, 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, 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.
MIAPPE “BrAPI to ISA” service
-
Since the release of BrAPI 1.3, efforts have been made to incorporate support for the Minimum Information About Plant Phenotyping Experiments (MIAPPE) standard into the specification [16]. This integration was finalized in BrAPI 2.0, resulting in full compatibility between the two standards. Consequently, BrAPI now encompasses all attributes necessary for MIAPPE compliance, adhering to standardized descriptions in accordance with MIAPPE guidelines. Leveraging BrAPI as a standardized RESTful web service API specification, we employ the ISA standard for storing metadata and phenotyping data in a standardized manner. This data is structured in the ISA-TAB file format and subjected to validation using the MIAPPE ISA configuration. The “BrAPI to ISA” service functions as a converter between BrAPI RESTful endpoints and ISA-TAB, facilitating the archiving of metadata and data and thereby enhancing data preservation and accessibility. The BrAPI2ISA tool is designed to be compatible with BrAPI 1.3, and we invite contributions from the community to extend support for the latest versions of BrAPI.
+
Since the release of BrAPI 1.3, efforts have been made to incorporate support for the Minimum Information About Plant Phenotyping Experiments (MIAPPE) standard into the specification [21]. This integration was finalized in BrAPI 2.0, resulting in full compatibility between the two standards. Consequently, BrAPI now encompasses all attributes necessary for MIAPPE compliance, adhering to standardized descriptions in accordance with MIAPPE guidelines. Leveraging BrAPI as a standardized RESTful web service API specification, we employ the ISA standard for storing metadata and phenotyping data in a standardized manner. This data is structured in the ISA-TAB file format and subjected to validation using the MIAPPE ISA configuration. The “BrAPI to ISA” service functions as a converter between BrAPI RESTful endpoints and ISA-TAB, facilitating the archiving of metadata and data and thereby enhancing data preservation and accessibility. The BrAPI2ISA tool is designed to be compatible with BrAPI 1.3, and we invite contributions from the community to extend support for the latest versions of BrAPI.
+
BrAPIMapper
+
+
BrAPIMapper is a full BrAPI implementation of all calls for any data source missing BrAPI implementation or compliance with some BrAPI versions. BrAPIMapper is provided as a docker application that can get its external data sources from mySQL or PostgreSQL databases (with a dedicated interface for Chado database schema), generic REST services (with a dedicated interface for BrAPI endpoints), flat files (XML, JSON, CSV/TSV/GFF3/VCF, YAML) or any combination of any of those. It provides an administration interface to map BrAPI data models to external data sources. The interface allows administrators to select the BrAPI specification versions to use and the calls to enable. Data mapping configuration export and import features simplify upgrades to future BrAPI specifications changes as administrators would only have to map missing fields or make minor adjustments. Amongst others, it supports paging, search calls, either by providing direct results or using deferred results with a search identifier, lists, authentication and manages access restrictions to calls that can be setup through the administration interface as well. This tool aims to accelerate BrAPI services deployment while ensuring specification compliance.
Data visualization
Flapjack
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Flapjack[18] is a multi-platform desktop application for data visualization and breeding analysis (eg, pedigree verification, marker-assisted backcrossing and forward breeding) using high-throughput genotype data. Data can be easily imported into Flapjack from any BrAPI compatible data source with genotype data available. Flapjack Bytes is a smaller, lightweight and fully web-based counterpart to Flapjack, which can be easily embedded into a database website to provide similar visualizations online. Traditionally supporting its own text-based data formats, Flapjack’s use of BrAPI has streamlined the end-user experience for data import and work is underway to determine the best methods to exchange analysis results using future versions of the API.
+
Flapjack[23] is a multi-platform desktop application for data visualization and breeding analysis (eg, pedigree verification, marker-assisted backcrossing and forward breeding) using high-throughput genotype data. Data can be easily imported into Flapjack from any BrAPI compatible data source with genotype data available. Flapjack Bytes is a smaller, lightweight and fully web-based counterpart to Flapjack, which can be easily embedded into a database website to provide similar visualizations online. Traditionally supporting its own text-based data formats, Flapjack’s use of BrAPI has streamlined the end-user experience for data import and work is underway to determine the best methods to exchange analysis results using future versions of the API.
Helium
-
Helium (https://helium.hutton.ac.uk) [19] 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 (Paterson 2011). The accurate representation of pedigrees, and the ability to pull pedigree data from different data sources is therefore important in plant breeding and genetics and 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 (https://helium.hutton.ac.uk). The ability to pull data from BrAPI compliant data sources has significantly expanded Helium’s capability and utility within the community. Helium is used in projects ranging in size from tens to tens of thousands of lines and across a wide variety of crops and species. While originally designed for plant data [20] it has also found utility in other non-plant projects [21] highlighting its broad utility. This also allows Helium users to provide direct dataset links to collaborators allowing the original data to be held with the data provider and utilising Helium for its visualization functionality. Our current Helium deployment includes example BrAPI calls to a barley dataset at Hutton to allow users to test the system and features it offers.
+
Helium (https://helium.hutton.ac.uk) [24] 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 (Paterson 2011). The accurate representation of pedigrees, and the ability to pull pedigree data from different data sources is therefore important in plant breeding and genetics and 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 (https://helium.hutton.ac.uk). The ability to pull data from BrAPI compliant data sources has significantly expanded Helium’s capability and utility within the community. Helium is used in projects ranging in size from tens to tens of thousands of lines and across a wide variety of crops and species. While originally designed for plant data [25] it has also found utility in other non-plant projects [26] highlighting its broad utility. This also allows Helium users to provide direct dataset links to collaborators allowing the original data to be held with the data provider and utilising Helium for its visualization functionality. Our current Helium deployment includes example BrAPI calls to a barley dataset at Hutton to allow users to test the system and features it offers.
Tassel
I don’t know much about Tassel or its BrAPI compliance. This is filler text for the layout of the manuscript.
@@ -753,7 +773,7 @@
DArTView
DArTView is a desktop application for visualizing genotype variant data and looking for trends or correlations. It is newly BrAPI compatible and can use BrAPI as an input data source.
DivBrowse
-
DivBrowse [13] 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 and enables easy data import and export by using standardized and established bioinformatics file formats.
+
DivBrowse [18] 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 and enables easy data import and export by using standardized and established bioinformatics file formats.
At its core, DivBrowse combines the convenience of a genome browser and adds features tailored to the diversity analysis of germplasm. It is able to display genomic features such as nucleotide sequence, associated gene models and short genomic variants. DivBrowse provides visual access to large VCF files obtained through genotyping experiments. In addition to visualizing variant calls per variant and genotype, DivBrowse also calculates and displays variant statistics such as minor allele frequencies, proportion of heterozygous calls or missing variant calls for each visualized genomic window. In addition, dynamic Principal Component Analyses (PCAs) can be performed on a user specified genomic area to provide information on local genomic diversity.
DivBrowse has a Javascript API to control the tool from a hosting web portal (e.g. to control the list of genotypes to be displayed and the reference genome). DivBrowse has an interface to BLAST, which can be used to directly access genes or other genomic features. The modular structure of DivBrowse also allows developers to configure and easily embed links to external information systems. Furthermore, parts of BrAPI are implemented to provide genotypic data via its server-side component and is also able to consume and visualize genotypic data via an external BrAPI endpoint through the client-side GUI.
Analytics
@@ -784,12 +804,9 @@
Samples and Genotypes
DArT Sample Submission
The DArT genotyping lab is heavily used world wide when it comes to plant genotyping. Developers at DArT have worked with the BrAPI community to establish a standard API for sending sample metadata to the lab before genotyping. This eliminates much of the human error involved with sending samples to en external lab.
-
MGIS
-
-
MGIS has germplasm and genotype data stored for many musa accessions. Through BrAPI, users are able to access this data directly from MusaBase, for use in specific experiments.
GIGWA
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Gigwa is a JEE web application providing means to centralize, share, finely filter, and visualize high-throughput genotyping data [22]. Built on top of MongoDB, it is scalable and can support working smoothly with datasets containing billions of genotypes. Installable from docker images or all-in-one bundle archives, it is pretty 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/or a portal for exposing the data for genebanks and breeding programs for some CGIAR centers [23]. Thus, the amount of data hosted and made widely accessible using this system has kept growing over the last few years.
-
Gigwa developers have been involved in the BrAPI community since 2016 and took part in designing the genotype-related part of the API’s specifications. Its first BrAPI-compliant features were designed for compatibility with the Flapjack visualization tool [18] and thus primarily turned it into a BrAPI datasource. Consequently, over time, Gigwa being the first and most reliable application implementing BrAPI-Genotyping server calls, local collaborators and even external partners used it as a reference solution to design a number of tools taking advantage of those features (e.g., BeegMac, SnpClust, QBMS). But further use-cases also required Gigwa to be able to consume data from other BrAPI servers, which led to also implement API-client features into the system. Thanks to all this work, a close collaboration was progressively established with the Integrated Breeding Platform team developing the widely used Breeding Management System, that ended up in both applications now being frequently deployed together, Gigwa pulling germplasm or sample metadata from BMS, and BMS displaying Gigwa-hosted genotypes within its own UI.
+
Gigwa is a JEE web application providing means to centralize, share, finely filter, and visualize high-throughput genotyping data [11]. Built on top of MongoDB, it is scalable and can support working smoothly with datasets containing billions of genotypes. Installable from docker images or all-in-one bundle archives, it is pretty 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/or a portal for exposing the data for genebanks and breeding programs for some CGIAR centers [27]. Thus, the amount of data hosted and made widely accessible using this system has kept growing over the last few years.
+
Gigwa developers have been involved in the BrAPI community since 2016 and took part in designing the genotype-related part of the API’s specifications. Its first BrAPI-compliant features were designed for compatibility with the Flapjack visualization tool [23] and thus primarily turned it into a BrAPI datasource. Consequently, over time, Gigwa being the first and most reliable application implementing BrAPI-Genotyping server calls, local collaborators and even external partners used it as a reference solution to design a number of tools taking advantage of those features (e.g., BeegMac, SnpClust, QBMS). But further use-cases also required Gigwa to be able to consume data from other BrAPI servers, which led to also implement API-client features into the system. Thanks to all this work, a close collaboration was progressively established with the Integrated Breeding Platform team developing the widely used Breeding Management System, that ended up in both applications now being frequently deployed together, Gigwa pulling germplasm or sample metadata from BMS, and BMS displaying Gigwa-hosted genotypes within its own UI.
Client BrAPI libraries being available for R, community members typically write ad-hoc scripts syndicating data from multiple BrAPI sources (for instance phenotypes from a datasource and genotypes from another) in order to run various kinds of analyses such as GWAS, genomic selection or phylogenetic investigations. As a perspective, we may expect the most generic and widely-used of those pipelines to be at least publicly distributed, and possibly web-interfaced using solutions like R-Shiny in order to provide new, excitingly useful online services, based on Gigwa-hosted data.
PHG
@@ -871,50 +888,62 @@
References
8.
From bits to bites: Advancement of the Germinate platform to support prebreeding informatics for crop wild relatives
Sebastian Raubach, Benjamin Kilian, Kate Dreher, Ahmed Amri, Filippo M Bassi, Ousmane Boukar, Douglas Cook, Alan Cruickshank, Christian Fatokun, Noureddine El Haddad, …
Safeguarding and using global banana diversity: a holistic approach
Ines Van den houwe, Rachel Chase, Julie Sardos, Max Ruas, Els Kempenaers, Valentin Guignon, Sebastien Massart, Sebastien Carpentier, Bart Panis, Mathieu Rouard, Nicolas Roux
Multifunctional crop trait ontology for breeders' data: field book, annotation, data discovery and semantic enrichment of the literature
Rosemary Shrestha, Elizabeth Arnaud, Ramil Mauleon, Martin Senger, Guy F Davenport, David Hancock, Norman Morrison, Richard Bruskiewich, Graham McLaren
Breedbase: a digital ecosystem for modern plant breeding
Nicolas Morales, Alex C Ogbonna, Bryan J Ellerbrock, Guillaume J Bauchet, Titima Tantikanjana, Isaak Y Tecle, Adrian F Powell, David Lyon, Naama Menda, Christiano C Simoes, … Lukas A Mueller
A Critical Review of the Current Global Ex Situ Conservation System for Plant Agrobiodiversity. I. History of the Development of the Global System in the Context of the Political/Legal Framework and Its Major Conservation Components
A Critical Review of the Current Global Ex Situ Conservation System for Plant Agrobiodiversity. I. History of the Development of the Global System in the Context of the Political/Legal Framework and Its Major Conservation Components
The FAIR Guiding Principles for scientific data management and stewardship
Mark D Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, Jan-Willem Boiten, Luiz Bonino da Silva Santos, Philip E Bourne, … Barend Mons
The FAIR Guiding Principles for scientific data management and stewardship
Mark D Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, Jan-Willem Boiten, Luiz Bonino da Silva Santos, Philip E Bourne, … Barend Mons
Recommendations for the formatting of Variant Call Format (VCF) files to make plant genotyping data FAIR
Sebastian Beier, Anne Fiebig, Cyril Pommier, Isuru Liyanage, Matthias Lange, Paul J Kersey, Stephan Weise, Richard Finkers, Baron Koylass, Timothee Cezard, … Uwe Scholz
Recommendations for the formatting of Variant Call Format (VCF) files to make plant genotyping data FAIR
Sebastian Beier, Anne Fiebig, Cyril Pommier, Isuru Liyanage, Matthias Lange, Paul J Kersey, Stephan Weise, Richard Finkers, Baron Koylass, Timothee Cezard, … Uwe Scholz
Enabling reusability of plant phenomic datasets with MIAPPE 1.1
Evangelia A Papoutsoglou, Daniel Faria, Daniel Arend, Elizabeth Arnaud, Ioannis N Athanasiadis, Inês Chaves, Frederik Coppens, Guillaume Cornut, Bruno V Costa, Hanna Ćwiek‐Kupczyńska, … Cyril Pommier
Enabling reusability of plant phenomic datasets with MIAPPE 1.1
Evangelia A Papoutsoglou, Daniel Faria, Daniel Arend, Elizabeth Arnaud, Ioannis N Athanasiadis, Inês Chaves, Frederik Coppens, Guillaume Cornut, Bruno V Costa, Hanna Ćwiek‐Kupczyńska, … Cyril Pommier
Susanna-Assunta Sansone, Philippe Rocca-Serra, Dawn Field, Eamonn Maguire, Chris Taylor, Oliver Hofmann, Hong Fang, Steffen Neumann, Weida Tong, Linda Amaral-Zettler, … Winston Hide
Susanna-Assunta Sansone, Philippe Rocca-Serra, Dawn Field, Eamonn Maguire, Chris Taylor, Oliver Hofmann, Hong Fang, Steffen Neumann, Weida Tong, Linda Amaral-Zettler, … Winston Hide
Unlocking the genetic diversity and population structure of the newly introduced two-row spring European HerItage Barley collecTion (ExHIBiT)
Villő Bernád, Nadia Al-Tamimi, Patrick Langan, Gary Gillespie, Timothy Dempsey, Joey Henchy, Mary Harty, Luke Ramsay, Kelly Houston, Malcolm Macaulay, … Sónia Negrão
Unlocking the genetic diversity and population structure of the newly introduced two-row spring European HerItage Barley collecTion (ExHIBiT)
Villő Bernád, Nadia Al-Tamimi, Patrick Langan, Gary Gillespie, Timothy Dempsey, Joey Henchy, Mary Harty, Luke Ramsay, Kelly Houston, Malcolm Macaulay, … Sónia Negrão
Admixture and reproductive skew shape the conservation value of ex situ populations of the Critically Endangered eastern black rhino
Franziska Elsner-Gearing, Petra Kretzschmar, Susanne Shultz, Mark Pilgrim, Deborah Ann Dawson, Gavin John Horsburgh, Jírí Hruby, Jane Hopper, Tony King, Catherine Walton
Admixture and reproductive skew shape the conservation value of ex situ populations of the Critically Endangered eastern black rhino
Franziska Elsner-Gearing, Petra Kretzschmar, Susanne Shultz, Mark Pilgrim, Deborah Ann Dawson, Gavin John Horsburgh, Jírí Hruby, Jane Hopper, Tony King, Catherine Walton
+0000-0003-0284-1885
+· 
+mrouard
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