transPLANT Resources for Triticeae Genomic Data

Spannagl, Manuel; Alaux, Michaël; Lange, Matthias; Bolser, Dan; Bader, Kai Christian; Letellier, Thomas; Kimmel, Erik; Flores, Raphaël; Pommier, Cyril; Kerhornou, Arnaud; Walts, Brandon; Nußbaumer, Thomas; Grabmüller, Christoph; Chen, Jinbo; Colmsee, Christian; Beier, Sebastian; Mascher, Martin; Schmutzer, Thomas; Arend, Daniel; Thanki, Anil; Ramírez-González, Ricardo H.; Ayling, Martin; Ayling, Sarah; Cáccamo, Mario; Mayer, Klaus; Scholz, Uwe; Steinbach, Delphine; Quesneville, Hadi; Kersey, Paul J.

doi:10.3835/plantgenome2015.06.0038

Cited by 9 publications

(5 citation statements)

References 29 publications

(34 reference statements)

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“…For comparative analysis we established a stand‐alone BLAST web server similar to the IPK Barley Blast Server (Spannagl et al ., ) using ViroBLAST (Deng et al ., ). For direct application of analyses based on sequence homology it contains the WGS assembly and the rye gene models.…”

Section: Methodsmentioning

confidence: 99%

“…Comprehensive whole‐genome sequence information of the allogamous rye has been missing so far, whereas draft genome sequences of the related autogamous Triticeae species barley, bread wheat, Aegilops tauschii and T. urartu became available recently (Mayer et al ., , ; Jia et al ., ; Ling et al ., ). These genomic resources are indispensable tools for understanding the biology and evolution of major Triticeae species through comparative genomic approaches (Spannagl et al ., ) and for relating this knowledge to phenotypic traits (Esch et al ., ). As yet, rye is not well represented in public sequence databases, which prohibits large‐scale functional analyses in rye and genomics‐assisted genetic improvement of rye for sustainable crop production.…”

Section: Introductionmentioning

confidence: 99%

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Towards a whole‐genome sequence for rye (Secale cereale L.)

et al. 2017

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SUMMARYWe report on a whole-genome draft sequence of rye (Secale cereale L.). Rye is a diploid Triticeae species closely related to wheat and barley, and an important crop for food and feed in Central and Eastern Europe. Through whole-genome shotgun sequencing of the 7.9-Gbp genome of the winter rye inbred line Lo7 we obtained a de novo assembly represented by 1.29 million scaffolds covering a total length of 2.8 Gbp. Our reference sequence represents nearly the entire low-copy portion of the rye genome. This genome assembly was used to predict 27 784 rye gene models based on homology to sequenced grass genomes. Through resequencing of 10 rye inbred lines and one accession of the wild relative S. vavilovii, we discovered more than 90 million single nucleotide variants and short insertions/deletions in the rye genome. From these variants, we developed the high-density Rye600k genotyping array with 600 843 markers, which enabled anchoring the sequence contigs along a high-density genetic map and establishing a synteny-based virtual gene order. Genotyping data were used to characterize the diversity of rye breeding pools and genetic resources, and to obtain a genome-wide map of selection signals differentiating the divergent gene pools. This rye whole-genome sequence closes a gap in Triticeae genome research, and will be highly valuable for comparative genomics, functional studies and genome-based breeding in rye.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards a whole‐genome sequence for rye (Secale cereale L.)

et al. 2017

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“…It bridges genetic and genomic data, allowing researchers' access to both genetic information (e.g., genetic maps, quantitative trait loci, association genetics, markers, polymorphisms, germplasms, phenotypes and genotypes) and genomic data (e.g., genome sequences, physical maps, genome annotation and expression data). For genomic data and genome sequences in particular, transplant is an EU-funded project aiming at building hardware, software, and data infrastructure (Spannagl et al 2016).…”

Section: Improvement Of Databasesmentioning

confidence: 99%

Population Genomics Along With Quantitative Genetics Provides a More Efficient Valorization of Crop Plant Genetic Diversity in Breeding and Pre-breeding Programs

Civáň

Rincent

Danguy-Des-Deserts

et al. 2021

Population Genomics

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The breeding efforts of the twentieth century contributed to large increases in yield but selection may have increased vulnerability to environmental perturbations. In that context, there is a growing demand for methodology to re-introduce useful variation into cultivated germplasm. Such efforts can focus on the introduction of specific traits monitored through diagnostic molecular markers identified by QTL/association mapping or selection signature screening. A combined approach is to increase the global diversity of a crop without targeting any particular trait.A considerable portion of the genetic diversity is conserved in genebanks. However, benefits of genetic resources (GRs) in terms of favorable alleles have to be weighed against unfavorable traits being introduced along. In order to facilitate utilization of GR, core collections are being identified and progressively characterized at the phenotypic and genomic levels. High-throughput genotyping and sequencing technologies allow to build prediction models that can estimate the genetic value of an entire genotyped collection. In a pre-breeding program, predictions can accelerate recurrent selection using rapid cycles in greenhouses by skipping some phenotyping steps. In a breeding program, reduced phenotyping characterization allows to increase the number of tested parents and crosses (and global genetic variance) for a fixed budget. Finally, the whole cross design can be optimized using progeny variance predictions to maximize short-term genetic gain or long-term genetic gain by constraining a minimum level of diversity in the germplasm. There is also a potential to further increase the accuracy of genomic predictions by taking into account genotype by environment interactions, integrating additional layers of omics and environmental information.Here, we aim to review some relevant concepts in population genomics together with recent advances in quantitative genetics in order to discuss how the combination of both disciplines can facilitate the use of genetic diversity in plant (pre) breeding programs.

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“…A number of initiatives have begun to address the issue of data integration (Supplemental Table S1), including the Wheat Information System (Wheat IS) (http://wheatis. org/), the Arabidopsis Information Portal (ARAPORT) (https://www.araport.org/) (Krishnakumar et al, 2015), the Global Grape Information System (GrapeIS) (https://www6.inra.fr/iggp) (Adam-Blondon et al, 2016), and the transPLANT project (http://www.transplantdb.eu/) (Spannagl et al, 2016). However, these are seldom integrated with each other, although there is increasing effort to standardize and harmonize approaches within research communities such as Divseek (Meyer, 2015).…”

Section: Navigating Crop Genetic Diversit Ymentioning

confidence: 99%

“…The FAIR principles endorse sharing and data reuse with an emphasis on machine-readable data, and metadata readable by humans (Leonelli, Davey, Arnaud, Parry, & Bastow, 2017;Marsden & Shahtout, 2014;Rodríguez-Iglesias et al, 2016). The trans-PLANT project and an increasing number of other initiatives have adopted these principles by providing search services for a broad range of databases (Spannagl et al, 2016). Such genome-centric platforms are also now facilitating prediction of gene function for crops (Dong, Schlueter, & Brendel, 2004;Krishnakumar et al, 2015;Nussbaumer et al, 2013;Rhee et al, 2003).…”

Section: The "Babel Fish" Ideal: Data Standardization and Interoperabilit Ymentioning

confidence: 99%

Knowledge representation and data sharing to unlock crop variation for nutritional food security

et al. 2020

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Meeting the challenge of food and nutritional security requires ongoing innovation, particularly in managing dietary nutritional information for pre-breeding analysis, selection, and cultivation of specific food crops and cultivars. At present, the ability to compare the relative nutritional value of crops is limited, with data management systems for most crops often inconsistent and poorly integrated. Here, we review generic efforts to standardize the description and management of crop trait data and discuss several issues currently constraining their exchange and comparison, with a focus on knowledge representation related to dietary nutrition. These issues include lack of consistency within or between crop specific databases, as well as limited data standardization and interoperability. At present, the use of common descriptors or controlled vocabularies between crops is fragmentary, with only partial implementation or uptake of formal ontologies, particularly for dietary nutritional composition. Although development of the existing Crop Ontology (CO) system has improved data sharing and reuse, it represents only a limited set of trait classes and crops. We identify the need for more robust and generic ontologies, particularly those that may address crop contributions to human dietary nutrition. We propose development of a Crop Dietary Nutrition Ontology (CDNO) as a robust structured controlled vocabulary for dietary nutritional composition and function, and provide examples of specific use cases and different end users who would benefit from using CDNO terms in their database searches. This development is likely to transform the way in which crops may be compared in terms of optimal dietary nutritional values.

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transPLANT Resources for Triticeae Genomic Data

Cited by 9 publications

References 29 publications

Towards a whole‐genome sequence for rye (Secale cereale L.)

Towards a whole‐genome sequence for rye (Secale cereale L.)

Population Genomics Along With Quantitative Genetics Provides a More Efficient Valorization of Crop Plant Genetic Diversity in Breeding and Pre-breeding Programs

Knowledge representation and data sharing to unlock crop variation for nutritional food security

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