Detail projektu

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

Období řešení: 01.03.2023 — 28.02.2024

Zdroje financování

Vysoké učení technické v Brně - Vnitřní projekty VUT

- plně financující (2023-01-01 - 2024-12-31)

O projektu

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.

Označení

FEKT/FIT-J-23-8274

Originální jazyk

čeština

Řešitelé

Schwarzerová Jana, Ing. et Ing., MSc - hlavní řešitel
Hurta Martin, Ing. - spoluřešitel
Provazník Valentine, prof. Ing., Ph.D. - spoluřešitel
Sekanina Lukáš, prof. Ing., Ph.D. - spoluřešitel

Útvary

Fakulta elektrotechniky a komunikačních technologií
- příjemce (01.01.2023 - 31.12.2023)
Fakulta informačních technologií
- interní (01.01.2023 - 31.12.2023)
Ústav biomedicínského inženýrství
- interní (01.01.2023 - 31.12.2023)
Ústav počítačových systémů
- interní (01.01.2023 - 31.12.2023)

Výsledky

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

Odpovědnost: Schwarzerová Jana, Ing. et Ing., MSc