Project detail

PGine: Py/Bioconda software package for calculation of polygenic risk score in plants

Duration: 1.3.2023 — 28.2.2024

Funding resources

Vysoké učení technické v Brně - Vnitřní projekty VUT

- whole funder (1. 1. 2023 - 31. 12. 2024)

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

Schwarzerová Jana, Ing. et Ing., MSc - principal person responsible
Hurta Martin, Ing. - fellow researcher
Provazník Valentýna, prof. Ing., Ph.D. - fellow researcher
Sekanina Lukáš, prof. Ing., Ph.D. - fellow researcher

Units

Faculty of Electrical Engineering and Communication
- responsible department (25.1.2023 - not assigned)
Department of Biomedical Engineering
- internal (1.1.2023 - 31.12.2023)
Department of Computer Systems
- internal (1.1.2023 - 31.12.2023)
Faculty of Information Technology
- internal (1.1.2023 - 31.12.2023)
Faculty of Electrical Engineering and Communication
- beneficiary (1.1.2023 - 31.12.2023)

Results

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.; 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

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

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

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

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

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