e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.

This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.

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Analysis of the Pythium ultimum transcriptome using Sanger and Pyrosequencing approaches


Background: Pythium species are an agriculturally important genus of plant pathogens, yet are not understood well at the molecular, genetic, or genomic level. They are closely related to other oomycete plant pathogens such as Phytophthora species and are ubiquitous in their geographic distribution and host rage. To gain a better understanding of its gene complement, we generated Expressed Sequence Tags (ESTs) from the transcriptome of Pythium ultimum DAOM BR144 (= ATCC 200006 = CBS 805.95) using two high throughput sequencing methods, Sanger-based chain termination sequencing and pyrosequencing-based sequencing-by-synthesis. Results: A single half-plate pyrosequencing (454 FLX) run on adapter-ligated cDNA from a normalized cDNA population generated 90,664 reads with an average read length of 190 nucleotides following cleaning and removal of sequences shorter than 100 base pairs. After clustering and assembly, a total of 35,507 unique sequences were generated. In parallel, 9,578 reads were generated from a library constructed from the same normalized cDNA population using dideoxy chain termination Sanger sequencing, which upon clustering and assembly generated 4,689 unique sequences. A hybrid assembly of both Sanger- and pyrosequencing-derived ESTs resulted in 34,495 unique sequences with 1,110 sequences (3.2%) that were solely derived from Sanger sequencing alone. A high degree of similarity was seen between P. ultimum sequences and other sequenced plant pathogenic oomycetes with 91% of the hybrid assembly derived sequences > 500 bp having similarity to sequences from plant pathogenic Phytophthora species. An analysis of Gene Ontology assignments revealed a similar representation of molecular function ontologies in the hybrid assembly in comparison to the predicted proteomes of three Phytophthora species, suggesting a broad representation of the P. ultimum transcriptome was present in the normalized cDNA population. P. ultimum sequences with similarity to oomycete RXLR and Crinkler effectors, Kazal-like and cystatin-like protease inhibitors, and elicitins were identified. Sequences with similarity to thiamine biosynthesis enzymes that are lacking in the genome sequences of three Phytophthora species and one downy mildew were identified and could serve as useful phylogenetic markers. Furthermore, we identified 179 candidate simple sequence repeats that can be used for genotyping strains of P. ultimum. Conclusion: Through these two technologies, we were able to generate a robust set (similar to 10 Mb) of transcribed sequences for P. ultimum. We were able to identify known sequences present in oomycetes as well as identify novel sequences. An ample number of candidate polymorphic markers were identified in the dataset providing resources for phylogenetic and diagnostic marker development for this species. On a technical level, in spite of the depth possible with 454 FLX platform, the Sanger and pyro-based sequencing methodologies were complementary as each method generated sequences unique to each platform.

  • US
  • GB
  • CA
  • Michigan_State_Univ (US)
  • JCVI_J_Craig_Venter_Inst (US)
  • Colorado_State_Univ (US)
  • AAFC_Agr_&_Agri_Food_Canada (CA)
  • TSL_The_Sainsbury_Lab (UK)
Data keywords
  • ontology
Agriculture keywords
    Data topic
    • big data
    • semantics
    Document type

    Inappropriate format for Document type, expected simple value but got array, please use list format

    Institutions 10 co-publis
    • Michigan_State_Univ (US)
    • AAFC_Agr_&_Agri_Food_Canada (CA)
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    e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
    Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.