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1. A unified approach to publish semantic annotations of agricultural documents as knowledge graphs

Author

Nadia Yacoubi Ayadi, Stephan Bernard, Robert Bossy, Marine Courtin, Bill Gates Happi Happi, Pierre Larmande, Franck Michel, Claire Nédellec, Catherine Roussey, and Catherine Faron

Subjects
Agriculture, Knowledge graphs, Semantic modelling, RDF transformation, Natural language processing, Annotations, Agriculture (General), S1-972, Agricultural industries, HD9000-9495
Abstract

The research results presented in this paper were obtained as part of the D2KAB project (Data to Knowledge in Agriculture and Biodiversity) which aims to develop semantic web-based tools to describe and make agronomical data actionable and accessible following the FAIR principles. We focus on constructing domain-specific Knowledge Graphs (KGs) from textual data sources, using Natural Language Processing (NLP) techniques to extract and structure relevant entities. Our approach is based on the formalization of a semantic data model using common linked open vocabularies such as the Web Annotation Ontology (OA) and the Provenance Ontology (PROV). The model was developed by formulating motivating scenarios and competency questions from domain experts. This model has been used to construct three different KGs from three distinct corpora: PubMed scientific publications on wheat and rice genetics and phenotyping, and French agricultural alert bulletins. The named entities to be recognized include genes, phenotypes, traits, genetic markers, taxa and phenological stages normalized using semantic resources such as the Wheat Trait and Phenotype Ontology (WTO), the French Crop Usage (FCU) thesaurus and the Plant Phenological Description Ontology (PPDO). Named entities were extracted using different NLP approaches and tools. The relevance of the semantic model was validated by implementing experts questions as SPARQL queries to be answered on the constructed RDF knowledge graphs. Our work demonstrates how domain-specific vocabularies and systematic querying of KGs can reveal hidden interactions and support agronomists in navigating vast amounts of data. The resources and transformation pipelines developed are publicly available in Git repositories.

Published
2024
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2. COMOKIT: A Modeling Kit to Understand, Analyze, and Compare the Impacts of Mitigation Policies Against the COVID-19 Epidemic at the Scale of a City

Author

Benoit Gaudou, Nghi Quang Huynh, Damien Philippon, Arthur Brugière, Kevin Chapuis, Patrick Taillandier, Pierre Larmande, and Alexis Drogoul

Subjects
COVID-19, agent-based modeling (ABM), epidemiological modeling, GAMA platform, computer simulation (CS), Public aspects of medicine, RA1-1270
Abstract

Since its emergence in China, the COVID-19 pandemic has spread rapidly around the world. Faced with this unknown disease, public health authorities were forced to experiment, in a short period of time, with various combinations of interventions at different scales. However, as the pandemic progresses, there is an urgent need for tools and methodologies to quickly analyze the effectiveness of responses against COVID-19 in different communities and contexts. In this perspective, computer modeling appears to be an invaluable lever as it allows for the in silico exploration of a range of intervention strategies prior to the potential field implementation phase. More specifically, we argue that, in order to take into account important dimensions of policy actions, such as the heterogeneity of the individual response or the spatial aspect of containment strategies, the branch of computer modeling known as agent-based modeling is of immense interest. We present in this paper an agent-based modeling framework called COVID-19 Modeling Kit (COMOKIT), designed to be generic, scalable and thus portable in a variety of social and geographical contexts. COMOKIT combines models of person-to-person and environmental transmission, a model of individual epidemiological status evolution, an agenda-based 1-h time step model of human mobility, and an intervention model. It is designed to be modular and flexible enough to allow modelers and users to represent different strategies and study their impacts in multiple social, epidemiological or economic scenarios. Several large-scale experiments are analyzed in this paper and allow us to show the potentialities of COMOKIT in terms of analysis and comparison of the impacts of public health policies in a realistic case study.

Published
2020
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3. Enabling a fast annotation process with the Table2Annotation tool

Author

Pierre Larmande and Kazim Muhammed Jibril

Subjects
bioinformatics, ontologies, semantic annotation, Genetics, QH426-470
Abstract

In semantic annotation, semantic concepts are linked to natural language. Semantic annotation helps in boosting the ability to search and access resources and can be used in information retrieval systems to augment the queries from the user. In the research described in this paper, we aimed to identify ontological concepts in scientific text contained in spreadsheets. We developed a tool that can handle various types of spreadsheets. Furthermore, we used the NCBO Annotator API provided by BioPortal to enhance the semantic annotation functionality to cover spreadsheet data. Table2Annotation has strengths in certain criteria such as speed, error handling, and complex concept matching.

Published
2020
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4. OryzaGP: rice gene and protein dataset for named-entity recognition

Author

Pierre Larmande, Huy Do, and Yue Wang

Subjects
named-entity recognition, natural language processing, Oryza sativa, plant molecular biology, rice, text mining, Genetics, QH426-470
Abstract

Text mining has become an important research method in biology, with its original purpose to extract biological entities, such as genes, proteins and phenotypic traits, to extend knowledge from scientific papers. However, few thorough studies on text mining and application development, for plant molecular biology data, have been performed, especially for rice, resulting in a lack of datasets available to solve named-entity recognition tasks for this species. Since there are rare benchmarks available for rice, we faced various difficulties in exploiting advanced machine learning methods for accurate analysis of the rice literature. To evaluate several approaches to automatically extract information from gene/protein entities, we built a new dataset for rice as a benchmark. This dataset is composed of a set of titles and abstracts, extracted from scientific papers focusing on the rice species, and is downloaded from PubMed. During the 5th Biomedical Linked Annotation Hackathon, a portion of the dataset was uploaded to PubAnnotation for sharing. Our ultimate goal is to offer a shared task of rice gene/protein name recognition through the BioNLP Open Shared Tasks framework using the dataset, to facilitate an open comparison and evaluation of different approaches to the task.

Published
2019
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5. Agronomic Linked Data (AgroLD): A knowledge-based system to enable integrative biology in agronomy.

Author

Aravind Venkatesan, Gildas Tagny Ngompe, Nordine El Hassouni, Imene Chentli, Valentin Guignon, Clement Jonquet, Manuel Ruiz, and Pierre Larmande

Subjects
Medicine, Science
Abstract

Recent advances in high-throughput technologies have resulted in a tremendous increase in the amount of omics data produced in plant science. This increase, in conjunction with the heterogeneity and variability of the data, presents a major challenge to adopt an integrative research approach. We are facing an urgent need to effectively integrate and assimilate complementary datasets to understand the biological system as a whole. The Semantic Web offers technologies for the integration of heterogeneous data and their transformation into explicit knowledge thanks to ontologies. We have developed the Agronomic Linked Data (AgroLD- www.agrold.org), a knowledge-based system relying on Semantic Web technologies and exploiting standard domain ontologies, to integrate data about plant species of high interest for the plant science community e.g., rice, wheat, arabidopsis. We present some integration results of the project, which initially focused on genomics, proteomics and phenomics. AgroLD is now an RDF (Resource Description Format) knowledge base of 100M triples created by annotating and integrating more than 50 datasets coming from 10 data sources-such as Gramene.org and TropGeneDB-with 10 ontologies-such as the Gene Ontology and Plant Trait Ontology. Our evaluation results show users appreciate the multiple query modes which support different use cases. AgroLD's objective is to offer a domain specific knowledge platform to solve complex biological and agronomical questions related to the implication of genes/proteins in, for instances, plant disease resistance or high yield traits. We expect the resolution of these questions to facilitate the formulation of new scientific hypotheses to be validated with a knowledge-oriented approach.

Published
2018
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6. Developing data interoperability using standards: A wheat community use case [version 2; referees: 2 approved]

Author

Esther Dzale Yeumo, Michael Alaux, Elizabeth Arnaud, Sophie Aubin, Ute Baumann, Patrice Buche, Laurel Cooper, Hanna Ćwiek-Kupczyńska, Robert P. Davey, Richard Allan Fulss, Clement Jonquet, Marie-Angélique Laporte, Pierre Larmande, Cyril Pommier, Vassilis Protonotarios, Carmen Reverte, Rosemary Shrestha, Imma Subirats, Aravind Venkatesan, Alex Whan, and Hadi Quesneville

Subjects
Agriculture & Biotechnology, Data Sharing, Medicine, Science
Abstract

In this article, we present a joint effort of the wheat research community, along with data and ontology experts, to develop wheat data interoperability guidelines. Interoperability is the ability of two or more systems and devices to cooperate and exchange data, and interpret that shared information. Interoperability is a growing concern to the wheat scientific community, and agriculture in general, as the need to interpret the deluge of data obtained through high-throughput technologies grows. Agreeing on common data formats, metadata, and vocabulary standards is an important step to obtain the required data interoperability level in order to add value by encouraging data sharing, and subsequently facilitate the extraction of new information from existing and new datasets. During a period of more than 18 months, the RDA Wheat Data Interoperability Working Group (WDI-WG) surveyed the wheat research community about the use of data standards, then discussed and selected a set of recommendations based on consensual criteria. The recommendations promote standards for data types identified by the wheat research community as the most important for the coming years: nucleotide sequence variants, genome annotations, phenotypes, germplasm data, gene expression experiments, and physical maps. For each of these data types, the guidelines recommend best practices in terms of use of data formats, metadata standards and ontologies. In addition to the best practices, the guidelines provide examples of tools and implementations that are likely to facilitate the adoption of the recommendations. To maximize the adoption of the recommendations, the WDI-WG used a community-driven approach that involved the wheat research community from the start, took into account their needs and practices, and provided them with a framework to keep the recommendations up to date. We also report this approach’s potential to be generalizable to other (agricultural) domains.

Published
2017
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14. BrAPI - an application programming interface for plant breeding applications.

Author

Peter Selby, Rafael Abbeloos, Jan Erik Backlund, Martin Basterrechea Salido, Guillaume Bauchet, Omar E. Benites-Alfaro, Clayton L. Birkett, Viana C. Calaminos, Pierre Carceller, Guillaume Cornut, Bruno Vasques Costa, Jeremy D. Edwards, Richard Finkers, Star Yanxin Gao, Mehmood Ghaffar, Philip Glaser, Valentin Guignon, Puthick Hok, Andrzej Kilian, Patrick König, Jack Elendil B. Lagare, Matthias Lange 0001, Marie-Angélique Laporte, Pierre Larmande, David S. LeBauer, David A Lyon, David F. Marshall, Dave Matthews, Iain Milne, Naymesh Mistry, Nicolas Morales, Lukas A. Mueller, Pascal Neveu, Evangelia Papoutsoglou, Brian Pearce, Ivan Perez-Masias, Cyril Pommier, Ricardo H. Ramirez-Gonzalez, Abhishek Rathore, Angel Manica Raquel, Sebastian Raubach, Trevor Rife, Kelly Robbins, Mathieu Rouard, Chaitanya Sarma, Uwe Scholz, Guilhem Sempéré, Paul D. Shaw, Reinhard Simon, Nahuel Soldevilla, Gordon Stephen, Qi Sun, Clarysabel Tovar, Grzegorz Uszynski, Maikel Verouden, and The BrAPI consortium

Published
2019
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26. Developing data interoperability using standards: A wheat community use case [version 1; referees: 1 approved]

Author

Esther Dzale Yeumo, Michael Alaux, Elizabeth Arnaud, Sophie Aubin, Ute Baumann, Patrice Buche, Laurel Cooper, Hanna Ćwiek-Kupczyńska, Robert P. Davey, Richard Allan Fulss, Clement Jonquet, Marie-Angélique Laporte, Pierre Larmande, Cyril Pommier, Vassilis Protonotarios, Carmen Reverte, Rosemary Shrestha, Imma Subirats, Aravind Venkatesan, Alex Whan, and Hadi Quesneville

Subjects
Opinion Article, Articles, Agriculture & Biotechnology, Data Sharing, wheat, data interoperability, metadata, ontology repository, bio-ontologies, standard vocabularies, data formats
Abstract

In this article, we present a joint effort of the wheat research community, along with data and ontology experts, to develop wheat data interoperability guidelines. Interoperability is the ability of two or more systems and devices to cooperate and exchange data, and interpret that shared information. Interoperability is a growing concern to the wheat scientific community, and agriculture in general, as the need to interpret the deluge of data obtained through high-throughput technologies grows. Agreeing on common data formats, metadata, and vocabulary standards is an important step to obtain the required data interoperability level in order to add value by encouraging data sharing, and subsequently facilitate the extraction of new information from existing and new datasets. During a period of more than 18 months, the RDA Wheat Data Interoperability Working Group (WDI-WG) surveyed the wheat research community about the use of data standards, then discussed and selected a set of recommendations based on consensual criteria. The recommendations promote standards for data types identified by the wheat research community as the most important for the coming years: nucleotide sequence variants, genome annotations, phenotypes, germplasm data, gene expression experiments, and physical maps. For each of these data types, the guidelines recommend best practices in terms of use of data formats, metadata standards and ontologies. In addition to the best practices, the guidelines provide examples of tools and implementations that are likely to facilitate the adoption of the recommendations. To maximize the adoption of the recommendations, the WDI-WG used a community-driven approach that involved the wheat research community from the start, took into account their needs and practices, and provided them with a framework to keep the recommendations up to date. We also report this approach’s potential to be generalizable to other (agricultural) domains.

Published
2017
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29. AgBioData consortium recommendations for sustainable genomics and genetics databases for agriculture.

Author

Lisa C. Harper, Jacqueline D. Campbell, Ethalinda K. S. Cannon, Sook Jung, Monica Poelchau, Ramona L. Walls, Carson M. Andorf, Elizabeth Arnaud, Tanya Z. Berardini, Clayton L. Birkett, Steven B. Cannon, James D. Carson, Bradford Condon, Laurel Cooper, Nathan A. Dunn, Christine G. Elsik, Andrew D. Farmer, Stephen P. Ficklin, David M. Grant, Emily S. Grau, Nic Herndon, Zhi-Liang Hu, Jodi L. Humann, Pankaj Jaiswal, Clément Jonquet, Marie-Angélique Laporte, Pierre Larmande, Gerard R. Lazo, Fiona McCarthy, Naama Menda, Christopher J. Mungall, Monica C. Munoz-Torres, Sushma Naithani, Rex T. Nelson, Daureen Nesdill, Carissa A. Park, James M. Reecy, Leonore Reiser, Lacey-Anne Sanderson, Taner Z. Sen, Margaret Staton, Sabarinath Subramaniam, Marcela Karey Tello-Ruiz, Victor Unda, Deepak R. Unni, Liya Wang, Doreen Ware, Jill L. Wegrzyn, Jason Williams 0003, Margaret Woodhouse, Jing Yu 0002, and Doreen Main

Published
2018
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31. Managing High-Density Genotyping Data with Gigwa

Author

Guilhem, Sempéré, Pierre, Larmande, and Mathieu, Rouard

Subjects
Genotype, Genotyping Techniques, High-Throughput Nucleotide Sequencing, Genomics, Polymorphism, Single Nucleotide, Software
Abstract

Next generation sequencing technologies enabled high-density genotyping for large numbers of samples. Nowadays SNP calling pipelines produce up to millions of such markers, but which need to be filtered in various ways according to the type of analyses. One of the main challenges still lies in the management of an increasing volume of genotyping files that are difficult to handle for many applications. Here, we provide a practical guide for efficiently managing large genomic variation data using Gigwa, a user-friendly, scalable and versatile application that may be deployed either remotely on web servers or on a local machine.

Published
2022

32. AgroLD: A Knowledge Graph Database for Plant Functional Genomics

Author

Pierre, Larmande, Gildas, Tagny Ngompe, Aravind, Venkatesan, and Manuel, Ruiz

Subjects
Proteomics, Databases, Factual, Genomics, Plants, Pattern Recognition, Automated
Abstract

Recent advances in high-throughput technologies have resulted in tremendous increase in the amount of data in the agronomic domain. There is an urgent need to effectively integrate complementary information to understand the biological system in its entirety. We have developed AgroLD, a knowledge graph that exploits the Semantic Web technology and some of the relevant standard domain ontologies, to integrate information on plant species and in this way facilitating the formulation of new scientific hypotheses. This chapter outlines some integration results of the project, which initially focused on genomics, proteomics and phenomics.

Published
2022

33. Managing High-Density Genotyping Data with Gigwa

Author

Guilhem Sempéré, Pierre Larmande, Mathieu Rouard, Interactions hôtes-vecteurs-parasites-environnement dans les maladies tropicales négligées dues aux trypanosomatides (UMR INTERTRYP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université de Bordeaux (UB), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), South Green Bioinformatics Platform [Montpellier], Diversité, adaptation, développement des plantes (UMR DIADE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM), Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT) [Rome] (Alliance), and Consultative Group on International Agricultural Research [CGIAR] (CGIAR)

Subjects
INDELs, VCF, NoSQL database, interoperability, web tool, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], SNP markers
Abstract

International audience; Next generation sequencing technologies enabled high-density genotyping for large numbers of samples. Nowadays SNP calling pipelines produce up to millions of such markers, but which need to be filtered in various ways according to the type of analyses. One of the main challenges still lies in the management of an increasing volume of genotyping files that are difficult to handle for many applications. Here, we provide a practical guide for efficiently managing large genomic variation data using Gigwa, a user-friendly, scalable and versatile application that may be deployed either remotely on web servers or on a local machine.

Published
2022

38. Enabling a fast annotation process with the Table2Annotation tool

Author

Kazim Muhammed Jibril, Pierre Larmande, Diversité, adaptation, développement des plantes (UMR DIADE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), South Green Bioinformatics Platform [Montpellier], Information, Communication and Technology Laboratory [Hanoi] (ICTLab - USTH ), and University of Science and Technology of Hanoi (USTH)

Subjects
0303 health sciences, Semantic annotation, Boosting (machine learning), Information retrieval, lcsh:QH426-470, Computer science, Health Informatics, bioinformatics, semantic annotation, 03 medical and health sciences, Annotation, lcsh:Genetics, 0302 clinical medicine, 030220 oncology & carcinogenesis, Application Note, Genetics, ontologies, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Ecology, Evolution, Behavior and Systematics, Natural language, 030304 developmental biology
Abstract

International audience; In semantic annotation, semantic concepts are linked to natural language. Semantic annotation helps in boosting the ability to search and access resources and can be used in information retrieval systems to augment the queries from the user. In the research described in this paper, we aimed to identify ontological concepts in scientific text contained in spreadsheets. We developed a tool that can handle various types of spreadsheets. Furthermore, we used the NCBO Annotator API provided by BioPortal to enhance the semantic annotation functionality to cover spreadsheet data. Table2Annotation has strengths in certain criteria such as speed, error handling, and complex concept matching.

Published
2020

39. Candidate gene prioritization using graph embedding

Author

Pierre Larmande, Quan Do, Hanoi University of Science and Technology (HUST), Diversité, adaptation, développement des plantes (UMR DIADE), Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])

Subjects
0106 biological sciences, Candidate gene, Computer science, Graph embedding, Bioinformatics, Convolutional Neural Network, 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Machine learning, computer.software_genre, 01 natural sciences, Convolutional neural network, Candidate genes, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, Knowledge graphs, 0202 electrical engineering, electronic engineering, information engineering, Rank (graph theory), Representation (mathematics), 030304 developmental biology, 0303 health sciences, business.industry, ComputingMethodologies_PATTERNRECOGNITION, Knowledge base, Embedding, Unsupervised learning, 020201 artificial intelligence & image processing, Artificial intelligence, Rice, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, computer, 010606 plant biology & botany
Abstract

Candidate genes prioritization allows to rank among a large number of genes, those that are strongly associated with a phenotype or a disease. Due to the important amount of data that needs to be integrate and analyse, gene-to-phenotype association is still a challenging task. In this paper, we evaluated a knowledge graph approach combined with embedding methods to overcome these challenges. We first introduced a dataset of rice genes created from several open-access databases. Then, we used the Translating Embedding model and Convolution Knowledge Base model, to vectorize gene information. Finally, we evaluated the results using link prediction performance and vectors representation using some unsupervised learning techniques.

Published
2020

40. Enabling Fast Annotation Process With Table2Annotation Tool

Author

Kazim Muhammed Jibril and Pierre Larmande

Subjects
Annotation, Information retrieval, Computer science, Ontology, Natural language
Abstract

Semantic annotation is the process in which semantic concepts are linked to natural language. It helps in boosting the search and access of resources and can be used in information retrieval systems to increase the queries from the user. In this paper, we are interested in identifying ontological concepts in scientific text contained in spreadsheet. We developed a tool which is able to handle various types of spreadsheet. Furthermore, we used the benefits of NCBO Annotator API provided by BioPortal to enhance the semantic annotation functionalities covering spreadsheet data. Table2Annotation developed strengths in certain criteria like speed, error handling and complex concept matching.AvailabilityGitHub : https://github.com/pierrelarmande/ontology-project

Published
2020

41. PyRice: a Python package for querying Oryza sativa databases

Author

Pierre Larmande, Quan Do, Ho Bich Hai, University of Science and Technology of Hanoi (USTH), Diversité, adaptation, développement des plantes (UMR DIADE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), and South Green Bioinformatics Platform [Montpellier]

Subjects
Statistics and Probability, Databases, Factual, Computer science, Information Storage and Retrieval, computer.software_genre, Biochemistry, 03 medical and health sciences, Documentation, Molecular Biology, Gene, 030304 developmental biology, computer.programming_language, 0303 health sciences, Oryza sativa, Database, business.industry, 030302 biochemistry & molecular biology, Oryza, Modular design, Python (programming language), Computer Science Applications, Computational Mathematics, Computational Theory and Mathematics, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, computer, Software
Abstract

SummaryCurrently, gene information available for Oryza sativa species is located in various online heterogeneous data sources. Moreover, methods of access are also diverse, mostly web-based and sometimes query APIs, which might not always be straightforward for domain experts. The challenge is to collect information quickly from these applications and combine it logically, to facilitate scientific research. We developed a Python package named PyRice, a unified programming API to access all supported databases at the same time with consistent output. PyRice design is modular and implements a smart query system which fits the computing resources to optimize the query speed. As a result, PyRice is easy to use and produces intuitive results.Availability and implementationhttps://github.com/SouthGreenPlatform/PyRiceDocumentationhttps://pyrice.readthedocs.ioContactpierre.larmande@ird.frLicence informationMITSupplementary informationSupplementary data are available online.

Published
2020

42. Corrigendum to: Rice Galaxy: an open resource for plant science

Author

Joshua Dizon, Nickolai Alexandrov, Tobias Kretzschmar, Pierre Larmande, Venice Margarette Juanillas, Gabriel Zhou, Ramil Mauleon, John Robert Mendoza, Nicolas Beaume, Michael J. Thomson, Kunalan Ratharanjan, Jon Peter Perdon, Alexis Dereeper, Manuel Ruiz, Lindsay R. Triplett, Gaëtan Droc, Jillian M. Lang, Jason H. Haga, Beth Plale, Jan E. Leach, and Locedie Mansueto

Subjects
Resource (biology), business.industry, Environmental resource management, Oryza, Health Informatics, Genomics, Galaxy, Computer Science Applications, Plant Breeding, Geography, Plant science, Seed Bank, Databases, Genetic, Corrigendum, business, Software
Abstract

Rice molecular genetics, breeding, genetic diversity, and allied research (such as rice-pathogen interaction) have adopted sequencing technologies and high-density genotyping platforms for genome variation analysis and gene discovery. Germplasm collections representing rice diversity, improved varieties, and elite breeding materials are accessible through rice gene banks for use in research and breeding, with many having genome sequences and high-density genotype data available. Combining phenotypic and genotypic information on these accessions enables genome-wide association analysis, which is driving quantitative trait loci discovery and molecular marker development. Comparative sequence analyses across quantitative trait loci regions facilitate the discovery of novel alleles. Analyses involving DNA sequences and large genotyping matrices for thousands of samples, however, pose a challenge to non-computer savvy rice researchers.The Rice Galaxy resource has shared datasets that include high-density genotypes from the 3,000 Rice Genomes project and sequences with corresponding annotations from 9 published rice genomes. The Rice Galaxy web server and deployment installer includes tools for designing single-nucleotide polymorphism assays, analyzing genome-wide association studies, population diversity, rice-bacterial pathogen diagnostics, and a suite of published genomic prediction methods. A prototype Rice Galaxy compliant to Open Access, Open Data, and Findable, Accessible, Interoperable, and Reproducible principles is also presented.Rice Galaxy is a freely available resource that empowers the plant research community to perform state-of-the-art analyses and utilize publicly available big datasets for both fundamental and applied science.

Published
2019

43. AgroPortal: A vocabulary and ontology repository for agronomy

Author

Vincent Emonet, Pierre Larmande, Sophie Aubin, Esther Dzale Yeumo, Clement Jonquet, Valeria Pesce, Marie-Angélique Laporte, John Graybeal, Elizabeth Arnaud, Anne Toulet, Mark A. Musen, Système Multi-agent, Interaction, Langage, Evolution (SMILE), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Stanford Center for BioMedical Informatics Research (BMIR), Stanford University, Bioversity International [Montpellier], Bioversity International [Rome], Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), DIST Délégation Information Scientifique et Technique (DV-IST), Institut National de la Recherche Agronomique (INRA), Food and Agriculture Organization of the United Nations [Rome, Italie] (FAO), Diversité, adaptation, développement des plantes (UMR DIADE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), French National Research Agency [ANR-12-JS02-01001], NUMEV Labex [ANR-10-LABX-20], Computational Biology Institute of Montpellier [ANR-11-BINF-0002], University of Montpellier, CNRS, ANR-12-JS02-0010,SIFR,Indexation sémantique de ressources biomédicales francophones(2012), ANR-11-BINF-0002,IBC,Institut de biologie Computationnelle(2011), ANR-10-LABX-0020,NUMEV,Digital and Hardware Solutions and Modeling for the Environement and Life Sciences(2010), European Project: 701771,H2020,H2020-MSCA-IF-2015,SIFRm(2016), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Stanford University [Stanford], Bioversity International, Consultative Group on International Agricultural Research [CGIAR], Food and Agricultural Organization of the United Nations (FAO), United Nations Organization, ANR-11-BINF-0002,IBC,Institut de Biologie Computationnelle de Montpellier(2011), and ANR-10-LABX-0020/10-LABX-0020,NUMEV,Digital and Hardware Solutions and Modeling for the Environement and Life Sciences(2010)

Subjects
0301 basic medicine, Computer science, 02 engineering and technology, Horticulture, Knowledge organization system, Ontology (information science), Domain (software engineering), Ontology repository, Open Biomedical Ontologies, 03 medical and health sciences, A50 - Recherche agronomique, Controlled vocabulary, Ontologies, 0202 electrical engineering, electronic engineering, information engineering, Semantic Web, ontologie, vocabulaire, Metadata, Semantic annotation, U10 - Informatique, mathématiques et statistiques, [INFO.INFO-WB]Computer Science [cs]/Web, Forestry, Biodiversity, computer.file_format, Recommendation, Knowledge organization systems or artifacts, Agronomy, Computer Science Applications, 030104 developmental biology, C30 - Documentation et information, Mapping, Food, Simple Knowledge Organization System, 020201 artificial intelligence & image processing, Controlled vocabularies, Banque de données, Plant sciences, Agronomy and Crop Science, computer, Modèle mathématique
Abstract

International audience; Many vocabularies and ontologies are produced to represent and annotate agronomic data. However, those ontologies are spread out, in different formats, of different size, with different structures and from overlapping domains. Therefore, there is need for a common platform to receive and host them, align them, and enabling their use in agro-informatics applications. By reusing the National Center for Biomedical Ontologies (NCBO) BioPortal technology, we have designed AgroPortal, an ontology repository for the agronomy domain. The AgroPortal project re-uses the biomedical domain's semantic tools and insights to serve agronomy, but also food, plant, and biodiversity sciences. We offer a portal that features ontology hosting, search, versioning, visualization, comment, and recommendation; enables semantic annotation; stores and exploits ontology alignments; and enables interoperation with the semantic web. The AgroPortal specifically satisfies requirements of the agronomy community in terms of ontology formats (e.g., SKOS vocabularies and trait dictionaries) and supported features (offering detailed metadata and advanced annotation capabilities). In this paper, we present our platform's content and features, including the additions to the original technology, as well as preliminary outputs of five driving agronomic use cases that participated in the design and orientation of the project to anchor it in the community. By building on the experience and existing technology acquired from the biomedical domain, we can present in AgroPortal a robust and feature-rich repository of great value for the agronomic domain.

Published
2018

44. Scientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities

Author

Olivier Collin, Sarah Cohen-Boulakia, Konrad Hinsen, Alban Gaignard, Khalid Belhajjame, Pierre Larmande, Fabien Mareuil, Christine Froidevaux, Frédéric Lemoine, Christophe Blanchet, Jérôme Chopard, Christophe Pradal, Hervé Ménager, Yvan Le Bras, Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision (LAMSADE), Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Plateforme bioinformatique GenOuest [Rennes], Université de Rennes (UR)-Plateforme Génomique Santé Biogenouest®-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Mathématiques, Informatique et STatistique pour l'Environnement et l'Agronomie (MISTEA), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Centre hospitalier universitaire de Nantes (CHU Nantes), Synchrotron SOLEIL (SSOLEIL), Centre National de la Recherche Scientifique (CNRS), Centre de biophysique moléculaire (CBM), Université d'Orléans (UO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Diversité, adaptation, développement des plantes (UMR DIADE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Centre d'Informatique pour la Biologie, Institut Pasteur [Paris] (IP), Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut Français de Bioinformatique - UMS CNRS 3601 (IFB-CORE), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), The authors acknowledge the support of GDR CNRS MaDICS, pro-gramme CPER Region Bretagne 'CeSGO', and programme Region Paysde la Loire 'Connect Talent' (SyMeTRIC). We acknowledge funding by thecall Infrastructures in Biology and Health' in the framework of the FrenchInvestments for the Future' (ANR-11-INBS-0012 and ANR-11-INBS-0013).This work was conducted in part at the IBC (Institute of Computational Bi-ology) in Montpellier, France., ANR-11-INBS-0013,IFB (ex Renabi-IFB),Institut français de bioinformatique(2011), ANR-11-INBS-0011,NeurATRIS,Infrastructure de Recherche Translationnelle pour les Biothérapies en Neurosciences(2011), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Plateforme Génomique Santé Biogenouest®-Inria Rennes – Bretagne Atlantique, Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA), Université d'Orléans (UO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris], Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Université Paris Dauphine-PSL-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Inria Sophia Antipolis - Méditerranée (CRISAM), Université d'Orléans (UO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA), ANR-11-INBS-0013/11-INBS-0013,ReNaBi-IFB,Institut français de bioinformatique(2011), ANR-11-INBS-0011/11-INBS-0011,NeurATRIS,Infrastructure de Recherche Translationnelle pour les Biothérapies en Neurosciences(2011), Cohen-Boulakia, Sarah, Infrastructures - Institut français de bioinformatique - - IFB (ex Renabi-IFB)2011 - ANR-11-INBS-0013 - INBS - VALID, and Infrastructures - Infrastructure de Recherche Translationnelle pour les Biothérapies en Neurosciences - - NeurATRIS2011 - ANR-11-INBS-0011 - INBS - VALID

Subjects
0301 basic medicine, Scientific Workflows, Computer Networks and Communications, Computer science, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], [INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS], Context (language use), 02 engineering and technology, Domain (software engineering), 03 medical and health sciences, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], Packaging environments, Use case, Set (psychology), environments, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], Reproducibility, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Data science, 030104 developmental biology, Workflow, C30 - Documentation et information, Packaging, 13. Climate action, Hardware and Architecture, Scientific workflows, Provenance, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 020201 artificial intelligence & image processing, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], U30 - Méthodes de recherche, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Software
Abstract

International audience; With the development of new experimental technologies, biologists are faced with an avalanche of data to be computationally analyzed for scientific advancements and discoveries to emerge. Faced with the complexity of analysis pipelines, the large number of computational tools, and the enormous amount of data to manage, there is compelling evidence that many if not most scientific discoveries will not stand the test of time: increasing the reproducibility of computed results is of paramount importance. The objective we set out in this paper is to place scientific workflows in the context of reproducibility. To do so, we define several kinds of repro-ducibility that can be reached when scientific workflows are used to perform experiments. We characterize and define the criteria that need to be catered for by reproducibility-friendly scientific workflow systems, and use such criteria to place several representative and widely used workflow systems and companion tools within such a framework. We also discuss the remaining challenges posed by reproducible scientific workflows in the life sciences. Our study was guided by three use cases from the life science domain involving in silico experiments.

Published
2017

45. OryzaGP: rice gene and protein dataset for named-entity recognition

Author

Huy Do, Yue Wang, Pierre Larmande, Diversité, adaptation, développement des plantes (UMR DIADE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), South Green Bioinformatics Platform [Montpellier], University of Science and Technology of Hanoi (USTH), and Database Center for Life Science [Chiba] (DBCLS)

Subjects
lcsh:QH426-470, Computer science, Health Informatics, Oryza sativa, text mining, computer.software_genre, Task (project management), Set (abstract data type), plant molecular biology, 03 medical and health sciences, Upload, Annotation, 0302 clinical medicine, Text mining, Named-entity recognition, Application Note, Genetics, natural language processing, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, 0303 health sciences, plant m lecular biology, Information retrieval, business.industry, named-entity recognition, rice, Biomedical text mining, lcsh:Genetics, ComputingMethodologies_PATTERNRECOGNITION, 030220 oncology & carcinogenesis, Benchmark (computing), [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, computer
Abstract

International audience; Text mining has become an important research method in biology, with its original purpose to extract biological entities, such as genes, proteins and phenotypic traits, to extend knowledge from scientific papers. However, few thorough studies on text mining and application development, for plant molecular biology data, have been performed, especially for rice, resulting in a lack of datasets available to solve named-entity recognition tasks for this species. Since there are rare benchmarks available for rice, we faced various difficulties in exploiting advanced machine learning methods for accurate analysis of the rice literature. To evaluate several approaches to automatically extract information from gene/protein entities, we built a new dataset for rice as a benchmark. This dataset is composed of a set of titles and abstracts, extracted from scientific papers focusing on the rice species, and is downloaded from PubMed. During the 5th Biomedical Linked Annotation Hackathon, a portion of the dataset was uploaded to PubAnnotation for sharing. Our ultimate goal is to offer a shared task of rice gene/protein name recognition through the BioNLP Open Shared Tasks framework using the dataset, to facilitate an open comparison and evaluation of different approaches to the task.

Published
2019

46. Rice Galaxy: An open resource for plant science

Author

Gabriel Zhou, Pierre Larmande, Jan E. Leach, Ramil Mauleon, Tobias Kretzschmar, Venice Margarette Juanillas, Nicolas Beaume, Gaëtan Droc, Kunalan Ratharanjan, John Robert Mendoza, Joshua Dizon, Michael J. Thomson, Beth Plale, Locedie Mansueto, Manuel Ruiz, Jillian M. Lang, Jason H. Haga, Nickolai Alexandrov, Jon Peter Perdon, Alexis Dereeper, and Lindsay R. Triplett

Subjects
0106 biological sciences, Germplasm, Application des ordinateurs, 01 natural sciences, Genome, F30 - Génétique et amélioration des plantes, chemistry.chemical_compound, Molecular marker, Technical Note, riz, 2. Zero hunger, 0303 health sciences, biology, U10 - Informatique, mathématiques et statistiques, high-density genotypes, food and beverages, single-nucleotide polymorphism, Computer Science Applications, C30 - Documentation et information, Diffusion de l'information, Banque de données, Génotype, workflow, Health Informatics, Computational biology, Quantitative trait locus, Oryza, DNA sequencing, 03 medical and health sciences, Genotyping, reproducibility, Genetic association, 030304 developmental biology, Genetic diversity, Génome, rice, 15. Life on land, biology.organism_classification, chemistry, breeding, genome-wide association studies, genomes, Galaxy project, 010606 plant biology & botany
Abstract

BackgroundRice molecular genetics, breeding, genetic diversity, and allied research (such as rice-pathogen interaction) have adopted sequencing technologies and high density genotyping platforms for genome variation analysis and gene discovery. Germplasm collections representing rice diversity, improved varieties and elite breeding materials are accessible through rice gene banks for use in research and breeding, with many having genome sequences and high density genotype data available. Combining phenotypic and genotypic information on these accessions enables genome-wide association analysis, which is driving quantitative trait loci (QTL) discovery and molecular marker development. Comparative sequence analyses across QTL regions facilitate the discovery of novel alleles. Analyses involving DNA sequences and large genotyping matrices for thousands of samples, however, pose a challenge to non-computer savvy rice researchers.FindingsWe adopted the Galaxy framework to build the federated Rice Galaxy resource, with shared datasets, tools, and analysis workflows relevant to rice research. The shared datasets include high density genotypes from the 3,000 Rice Genomes project and sequences with corresponding annotations from nine published rice genomes. Rice Galaxy includes tools for designing single nucleotide polymorphism (SNP) assays, analyzing genome-wide association studies, population diversity, rice-bacterial pathogen diagnostics, and a suite of published genomic prediction methods. A prototype Rice Galaxy compliant to Open Access, Open Data, and Findable, Accessible, Interoperable, and Reproducible principles is also presented.ConclusionsRice Galaxy is a freely available resource that empowers the plant research community to perform state-of-the-art analyses and utilize publicly available big datasets for both fundamental and applied science.

Published
2019

47. Benchmarking database systems for Genomic Selection implementation

Author

Valentin Guignon, Victor Jun M. Ulat, Kelly R. Robbins, Jon Renner, Elizabeth Jones, Dave Matthews, Guilhem Sempere, Raza Syed, Adrien Pétel, Yaw Nti-Addae, and Pierre Larmande

Subjects
Genotype, Genotyping Techniques, Computer science, Information Storage and Retrieval, 02 engineering and technology, Review, Hierarchical Data Format, computer.software_genre, Logiciel, Turnaround time, General Biochemistry, Genetics and Molecular Biology, F30 - Génétique et amélioration des plantes, Set (abstract data type), 03 medical and health sciences, Relational database management system, 020204 information systems, Databases, Genetic, 0202 electrical engineering, electronic engineering, information engineering, Information system, Allele, Genotyping, Selection (genetic algorithm), 030304 developmental biology, amélioration génétique, 0303 health sciences, Database, U10 - Informatique, mathématiques et statistiques, computer.file_format, Benchmarking, Genomics, L10 - Génétique et amélioration des animaux, DNA extraction, C30 - Documentation et information, General Agricultural and Biological Sciences, Banque de données, computer, Genomic selection, Software, Génotype, Information Systems
Abstract

MotivationWith high-throughput genotyping systems now available, it has become feasible to fully integration genotyping information into breeding programs [22]. To make use of this information effectively requires DNA extraction facilities and marker production facilities that can efficiently deploy the desired set of markers across samples with a rapid turnaround time that allows for selection before crosses needed to be made. In reality, breeders often have a short window of time to make decisions by the time they are able collect all their phenotyping data and receive corresponding genotyping data. This presents a challenge to organize information and utilize them in downstream analyses to support decisions made by breeders. In order to implement genomic selection routinely as part of breeding programs one would need an efficient genotype data storage system. We selected and benchmarked six popular open-source data storage systems, including relational database management and columnar storage systems.ResultsWe found that data extract times are greatly influenced by the orientation in which genotype data is stored in a system. HDF5 consistently performed best, in part because it can more efficiently work with both orientations of the allele matrix.Availabilityhttp://gobiinx1.bti.cornell.edu:6083/projects/GBM/repos/benchmarking/browseContactyn259@cornell.edu

Published
2019

49. BrAPI-an application programming interface for plant breeding applications

Author

Guilhem Sempere, Patrick König, Bruno Costa, Jan Erik Backlund, Jack Elendil B. Lagare, Viana C. Calaminos, Jeremy D. Edwards, Gordon Stephen, Omar Eduardo Benites-Alfaro, Pierre Carceller, Martin Basterrechea Salido, David LeBauer, Maikel P. H. Verouden, Guillaume Cornut, Nicolas Morales, Philip Glaser, David Marshall, Evangelia Papoutsoglou, Cyril Pommier, Guillaume Bauchet, Valentin Guignon, Kelly R. Robbins, Iain Milne, David A. Lyon, Pascal Neveu, Brian Pearce, Chaitanya Sarma, Qi Sun, Nahuel Soldevilla, Sebastian Raubach, Peter Selby, Clarysabel Tovar, Grzegorz Uszynski, Rafael Abbeloos, Reinhard Simon, Paul Shaw, Marie-Angélique Laporte, Mathieu Rouard, Star Yanxin Gao, Naymesh Mistry, Puthick Hok, Uwe Scholz, Trevor W. Rife, Ricardo H. Ramirez-Gonzalez, Dave Matthews, Mehmood Ghaffar, Pierre Larmande, Matthias Lange, Andrzej Kilian, Clayton Birkett, Ivan Perez-Masias, Angel Manica Raquel, Abhishek Rathore, Lukas A. Mueller, Richard Finkers, Wren, Jonathan, Flanders Institute for Biotechnology, Leibniz Institute of Plant Genetics and Crop Plant Research [Gatersleben] (IPK-Gatersleben), Boyce Thompson Institute [Ithaca], International Potato Center, USDA-ARS : Agricultural Research Service, International Rice Research Institute [Philippines] (IRRI), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Unité de Recherche Génomique Info (URGI), Institut National de la Recherche Agronomique (INRA), Instituto de Biologia Experimental e Tecnológica (IBET), Wageningen University and Research Centre (WUR), Cornell University [New York], CGIAR, Diversity Arrays Technology Pty Ltd (DArT P/L), Institut de Recherche pour le Développement (IRD), University of Arizona, The James Hutton Institute, LeafNode Technology, Partenaires INRAE, Mathématiques, Informatique et STatistique pour l'Environnement et l'Agronomie (MISTEA), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), John Innes Centre [Norwich], Biotechnology and Biological Sciences Research Council (BBSRC), International Crops Research Institute for the Semi-Arid Tropics [Inde] (ICRISAT), Kansas State University, Leafnode Technolog, Biometris, Gates Foundation, Bioversity International, Federal Ministry of Education & Research (BMBF) : FKZ 031A536A, 031B0190A, Boyce Thompson Institute for Plant Research CGIAR Research Program on Roots, Tubers and Bananas, Cornell University Excellence in Breeding Platform, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and John Innes Centre

Subjects
0106 biological sciences, Germplasm, programme informatique, Computer science, [SDV]Life Sciences [q-bio], Data management, computer.software_genre, 01 natural sciences, Biochemistry, F30 - Génétique et amélioration des plantes, User-Computer Interface, Software, Laboratorium voor Plantenveredeling, [MATH]Mathematics [math], 0303 health sciences, Application programming interface, U10 - Informatique, mathématiques et statistiques, Genomics, Original Papers, Computer Science Applications, Computational Mathematics, Computational Theory and Mathematics, Web service, sélection végétale, Statistics and Probability, Technology and Engineering, Databases and Ontologies, génomique, 03 medical and health sciences, Bioinformatique, application informatique, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Life Science, [INFO]Computer Science [cs], Plant breeding, Genotyping, Molecular Biology, 030304 developmental biology, business.industry, Biology and Life Sciences, Amélioration des plantes, Data science, Plant Breeding, business, computer, 010606 plant biology & botany
Abstract

Motivation Modern genomic breeding methods rely heavily on very large amounts of phenotyping and genotyping data, presenting new challenges in effective data management and integration. Recently, the size and complexity of datasets have increased significantly, with the result that data are often stored on multiple systems. As analyses of interest increasingly require aggregation of datasets from diverse sources, data exchange between disparate systems becomes a challenge. Results To facilitate interoperability among breeding applications, we present the public plant Breeding Application Programming Interface (BrAPI). BrAPI is a standardized web service API specification. The development of BrAPI is a collaborative, community-based initiative involving a growing global community of over a hundred participants representing several dozen institutions and companies. Development of such a standard is recognized as critical to a number of important large breeding system initiatives as a foundational technology. The focus of the first version of the API is on providing services for connecting systems and retrieving basic breeding data including germplasm, study, observation, and marker data. A number of BrAPI-enabled applications, termed BrAPPs, have been written, that take advantage of the emerging support of BrAPI by many databases. Availability and implementation More information on BrAPI, including links to the specification, test suites, BrAPPs, and sample implementations is available at https://brapi.org/. The BrAPI specification and the developer tools are provided as free and open source.

Published
2018

50. Evaluating Named-Entity Recognition approaches in plant molecular biology

Author

Pierre Larmande, Khoat Than, and Huy Do

Subjects
Conditional random field, business.industry, computer.software_genre, Molecular biology, Long short term memory, ComputingMethodologies_PATTERNRECOGNITION, Text mining, Named-entity recognition, Face (geometry), Benchmark (computing), State (computer science), business, CRFS, computer
Abstract

Text mining research is becoming an important topic in biology with the aim to extract biological entities from scientific papers in order to extend the biological knowledge. However, few thorough studies on text mining and applications are developed for plant molecular biology data, especially rice, thus resulting a lack of datasets available to train models able to detect entities such as genes, proteins and phenotypic traits. Since there is rare benchmarks for rice, we have to face various difficulties in exploiting advanced machine learning methods for accurate analysis of rice bibliography. In this article, we developed a new training datasets (Oryzabase) as the benchmark. Then, we evaluated the performance of several current approaches to find a methodology with the best results and assigned it as the state of the art method for our own technique in the future. We applied Name Entities Recognition (NER) tagger, which is built from a Long Short Term Memory (LSTM) model, and combined with Conditional Random Fields (CRFs) to extract information of rice genes and proteins. We analyzed the performance of LSTM-CRF when applying to the Oryzabase dataset and improved the results up to 86% in F1. We found that on average, the result from LSTM-CRF is more exploitable with the new benchmark.

Published
2018

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