Detail projektu

Real-time recognition of infection threats during nanopore sequencing

Období řešení: 01.02.2021 — 30.04.2022

Zdroje financování

Evropská unie - Interní grantová soutěž

- plně financující (2021-02-01 - 2022-04-30)

O projektu

The project focused on the analysis of core genome variability in a closely related microbial population using nanopore sequencing technology and convolutional neural network. The uniqueness of the proposal is based on real-time identification, processing and analysis of genetic variability directly from raw sequencing signals without the need of lossy decoding. Thanks to that the sequencing can be stopped as sufficient data are gathered, and the crucial epidemiologic information is available immediately.

Popis anglicky
The aim of the project is to create a competition for student research grants and its pilot verification. The creation of a new competition will contribute to the development of cross-sectional skills of doctoral students, and thus acquire competencies for work in science and research in the future and increase their success in submitting scientific projects to national and international competitions.

Označení

FEKT-K-21-6912

Originální jazyk

čeština

Řešitelé

Jakubíčková Markéta, Ing., Ph.D. - hlavní řešitel
Bartoň Vojtěch, Ing. - spoluřešitel

Útvary

Fakulta elektrotechniky a komunikačních technologií
- příjemce (01.02.2021 - 30.04.2022)
Ústav biomedicínského inženýrství
- spolupříjemce (01.02.2021 - 30.04.2022)

Výsledky

BARTOŇ, V.; NYKRÝNOVÁ, M.; ŠKUTKOVÁ, H. MANASIG: Python Package to MAnipulateNAnopore SIGnals from sequencing files. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2021. p. 1941-1947. ISBN: 978-1-6654-0126-5.
Detail

NOVÁK, V. Proceedings I of the 28th Conference STUDENT EEICT 2022 General papers. Proceedings I of the 28th Conference STUDENT EEICT 2022 General papers. 1. Brno: Brno University of Technology, Faculty of Electrical Engineering and Communication, 2022. ISBN: 978-80-214-6029-4.
Detail

NYKRÝNOVÁ, M.; JAKUBÍČEK, R.; BARTOŇ, V.; BEZDÍČEK, M.; LENGEROVÁ, M.; ŠKUTKOVÁ, H. Using deep learning for gene detection and classification in raw nanopore signals. Frontiers in Microbiology, 2022, vol. 13, no. 1, p. 1-11. ISSN: 1664-302X.
Detail

NYKRÝNOVÁ, M.; BARTOŇ, V.; VÍTEK, M.; BEZDÍČEK, M.; LENGEROVÁ, M.; ŠKUTKOVÁ, H. Raw nanopore squiggle alignment for bacterial typing distinction enhancement. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2021. p. 1969-1974. ISBN: 978-1-6654-0126-5.
Detail

Odpovědnost: Jakubíčková Markéta, Ing., Ph.D.