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Project detail
Duration: 01.03.2023 — 28.02.2024
Funding resources
Brno University of Technology - Vnitřní projekty VUT
- whole funder (2023-01-01 - 2024-12-31)
On the project
The most important scientific challenges connect to climate change. Prediction algorithms take an important role, including improving plant breeding according to new environmental conditions. Therefore, this project focuses on calculating human disease probability based on genotype information — polygenic risk score. This project's main goal is to adapt these algorithms to plant data in functional software. The software can be used to bring new insights into plant breeding and thus uncover possible solutions to the climate crisis.
Mark
FEKT/FIT-J-23-8274
Default language
Czech
People responsible
Hurta Martin, Ing. - fellow researcherProvazník Valentine, prof. Ing., Ph.D. - fellow researcherSekanina Lukáš, prof. Ing., Ph.D. - fellow researcherSchwarzerová Jana, Ing. et Ing., MSc - principal person responsible
Units
Faculty of Electrical Engineering and Communication- beneficiary (2023-01-01 - 2023-12-31)Department of Biomedical Engineering- internal (2023-01-01 - 2023-12-31)Department of Computer Systems- internal (2023-01-01 - 2023-12-31)Faculty of Information Technology- internal (2023-01-01 - 2023-12-31)
Results
SCHWARZEROVÁ, J.; BARTOŇ, V.; WALTHER, D.; WECKWERTH, W. Comprehensive analysis of putrescine metabolism in A. thaliana using GWAS, genetic risk score, metabolic modelling and data mining. In Proceedings II of the 29th Conference STUDENT EEICT 2023 Selected Papers. 1. Brno: Brno University of Technology, Faculty of Elektronic Engineering and Communication, 2023. p. 151-155. ISBN: 978-80-214-6154-3.Detail
SCHWARZEROVÁ, J.; HURTA, M.; WECKWERTH, W.; WALTHER, D. Decoding the Hidden Secrets of SNP Data: Revealing Ancestral Origins, Genomic Predictions, and Polygenic Risk Score. Germany: 2023.Detail
HURTA, M.; SCHWARZEROVÁ, J.; PROVAZNÍK, V.; WECKWERTH, W.; WALTHER, D.; SEKANINA, L. Utilizing Cartesian Genetic Programming for Efficient Polygenic Risk Score Calculation in Plants. Program and Abstract Book: Swedish Bioinformatics Workshop 2023. Stockholm: 2023. p. 49-49.Detail
JANIGOVÁ, P.; WECKWERTH, W.; SCHWARZEROVÁ, J. Py/ExplorReg: Exploration of Transcriptome for Potential Regulon Structure Detection. Proceedings I of the 30th Conference STUDENT EEICT 2024. 1. 2024. p. 28-31. ISBN: 978-80-214-6231-1.Detail
SCHWARZEROVÁ, J.; HURTA, M.; BARTOŇ, V.; LEXA, M.; WALTHER, D.; PROVAZNÍK, V.; WECKWERTH, W. A perspective on genetic and polygenic risk scores-advances and limitations and overview of associated tools. Briefings in Bioinformatics, 2024, vol. 25, no. 3, p. 1-11. ISSN: 1477-4054.Detail
SCHWARZEROVÁ, J.; JANIGOVÁ, P.; DVOŘÁČKOVÁ, M.; WECKWERTH, W. Optimizing of pre-processing analysis for Illumina RNA-Seq data in Arabidopsis thaliana. In Proceedings II of the 30st Conference STUDENT EEICT 2024: Selected papers. Proceedings II of the Conference STUDENT EEICT. 1. 2024. p. 142-146. ISBN: 978-80-214-6230-4. ISSN: 2788-1334.Detail
HURTA, M.; SCHWARZEROVÁ, J.; NAGELE, T.; WECKWERTH, W.; PROVAZNÍK, V.; SEKANINA, L. Utilizing Genetic Programming to Enhance Polygenic Risk Score Calculation. In 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2023). Istanbul: Institute of Electrical and Electronics Engineers, 2023. p. 3782-3787. ISBN: 979-8-3503-3748-8.Detail