Youlian Pan

Youlian Pan

First Name: 

Youlian

Last Name: 

Pan

Email: 

youlian.pan@nrc-cnrc.gc.ca

Address: 

Information and Communications Technologies Emerging Technologies National Research Council Canada 1200 Montreal Rd. Ottawa, Ontario, Canada K1A 0R6

Remote: 

Saskatoon

Abstract Title: 

PASKAL: Plant Abiotic Stress Knowledge Applications and Libraries

Abstract Authors: 

Alain B. Tchagang1, Rene Richard2, Alan Barton1, Daiqing Huang3, Sieu Phan1, Fazel Famili1, Jitao Zou3, Adrian J. Cutler3, and Youlian Pan1, 1Information and Communications Technology, National Research Council of Canada, 1200 Montréal Road, Ottawa, Ontario K1A 0R6 Canada, 2Information and Communications Technology, National Research Council of Canada, 46 Dineen Drive Fredericton, New Brunswick E3B 9W4 Canada, 3Aquatic and Crop Resource Development, National Research Council of Canada, 110 Gymnasium Place, Saskatoon Saskatchewan, S7N 0W9 Canada

Abstract Text: 

Abiotic stresses (drought, cold, heat, etc.) are known to impact yield of major crops (wheat, rice, barley, sorghum, corn, etc.). In order to improve the productivity of crops under extreme environmental conditions, Researchers and breeders are looking into engineer new generation stress tolerant cultivars. Substantial amount of data have been generated and available in literature. Computational tools associated with high-throughput technologies offer new venues into mining and integration these data. Here we propose and develop an integrative and interactive knowledge base: the Plant Abiotic Stress Knowledge Applications and Libraries (PASKAL). PASKAL hosts knowledge on major crop species that integrates with a set of automated web data mining tools and mathematical models to enable researchers and agriculture-industries in selection of traits with stress tolerant genetic markers for breeding next generation of resilient crop cultivars. PASKAL is made of four modules: the plant knowledge library, the tool, the predictor, and the requests/results modules. The library module is a collection of curated abiotic and biotic stress knowledge and the interactions between them. The knowledge is a collection from published data in literature or computed and experimentally/statistically validated from our research. The tool module is a collection of mathematical methods and models (biomarkers discovery, gene networks analysis, genotype phenotype associations analysis, pathways analysis, etc.) developed in house and that can be used online or offline for abiotic knowledge discovery. The predictor module gives the likelihood of genes, proteins, metabolites, etc., to play a role in abiotic and biotic stress processes, and how they can be tuned to improve crop productivity under extreme environmental conditions. The requests/results module allows the user to interact, explore, and communicate with PASKAL. PASKAL is currently at the development stage and accessible to collaborators at: https://132.246.39.186/paskal/index.php. Interested in collaboration with us, please contact the first or the last author.