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Génétique Quantitative et Évolution - Le Moulon

Yannick DE OLIVEIRA

Yannick DE OLIVEIRA

Engineer, INRAE

Programming & framework, DBMS & NoSQL Technologies

yannick.de-oliveira@inrae.fr

+33 (0)1 69 33 23 76

orcid.org/0000-0002-6902-0005

Publications

  • Génétique Quantitative et Évolution - Le Moulon
  • Université Paris-Saclay, INRAE, CNRS, AgroParisTech
  • Ferme du Moulon
  • F-91190 Gif-sur-Yvette

Education and positions

  • BioInformatics engineer (2009-…), Java/Python-Django SHiNeMaS and BioMercator projects, INRA
  • Head of development (2007-2009), Java-J2EE/Python-Django development and customer support of a LIMS solution, Sibio
  • BioInformatics engineer (2004-2007), Java/Perl developer on FLAGdb++ and UTILLd, INRA
  • Master degree in BioInformatics (2004) « Etude de Génomes Outils Informatiques et Statistiques », Rouen.

Current projects

I'm at head of two development projects in the team:

  • SHiNeMaS (Seeds History and network Management System), a database dedicated to seed lots history, phenotyping data and field practices,
  • BioMercato, a complete framework to integrate QTL, meta-QTL, genome annotation and genome-wide association studies.

I'm also involved in other development projects:

  • Thaliadb, A database dedicated to association genetic in plants,
  • Bakery, an ANR project focused on diversity and interactions in a low-input « Wheat/Human/Sourdough » agro-food ecosystem in which I develop a new database.

Skills

xx yy
Programming & framework Python – Django, Java – Spring & Hibernate, Perl – BioPerl - Javascript, Jquery, HTML
DBMS & NoSQL Technologies PostgreSQL, Oracle, Mongodb

Publications

De Oliveira Y, Burlot L, Dawson JC, Goldringer I, Madi D, Rivière P, Steinbach D, van Frank G, Thomas M. (2020) SHiNeMaS: a web tool dedicated to seed lots history, phenotyping and cultural practices. Plant Methods, 1 (16) 98
Lopez Arias DC, Chastellier A, Thouroude T, Bradeen J, Van Eck L, De Oliveira Y, Paillard S, Foucher F, Hibrand-Saint Oyant L, Soufflet-Freslon V. (2020) Characterization of black spot resistance in diploid roses with QTL detection, meta-analysis and candidate-gene identification. Theor Appl Genet,