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master's thesis
Metoda za pretvaranje signala dobivenog sekvenciranjem nanoporama u niz nukleotida
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:642231
Pavlić, Stanislav
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Pavlić, S. (2021). Metoda za pretvaranje signala dobivenog sekvenciranjem nanoporama u niz nukleotida (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:642231

Pavlić, Stanislav. "Metoda za pretvaranje signala dobivenog sekvenciranjem nanoporama u niz nukleotida." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:642231

Pavlić, Stanislav. "Metoda za pretvaranje signala dobivenog sekvenciranjem nanoporama u niz nukleotida." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:642231

Pavlić, S. (2021). 'Metoda za pretvaranje signala dobivenog sekvenciranjem nanoporama u niz nukleotida', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 23 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:642231

Pavlić S. Metoda za pretvaranje signala dobivenog sekvenciranjem nanoporama u niz nukleotida [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 November 23] Available at: https://urn.nsk.hr/urn:nbn:hr:168:642231

S. Pavlić, "Metoda za pretvaranje signala dobivenog sekvenciranjem nanoporama u niz nukleotida", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:642231

Metadata
TitleMetoda za pretvaranje signala dobivenog sekvenciranjem nanoporama u niz nukleotida
Title (english)DNA Nanopore Sequencing Basecaller
AuthorStanislav Pavlić
MentorMile Šikić (mentor)
Committee memberMile Šikić (predsjednik povjerenstva)
Committee memberMarko Subašić (član povjerenstva)
Committee memberKrešimir Križanović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-07-01, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Sekvenciranje nanoporama je jedna od vodećih tehnologija sekvenciranja danas. Prolaskom DNA kroz poru mijenja se ionska struja koja teče porom. Uslijed veličine pore, obilno se u pori nalazi 5 nukleotida (5-torka) koji utječu na mjereni signal. Svaka od 1024 moguće 5-torke uzrokuje različit signal i ta informacija se koristi za određivanje baza (pretvaranje sirovog signala u slijed nukleotida). Oblik signala je pravokutan jer se 5-torke mijenjaju po jedan nukleotid, ali je prisutan šum. Cilj rada je razvoj nove metode za pretvaranje signala dobivenog sekvenciranjem nanoporama korištenjem suvremenih arhitektura dubokog učenja, pritom imajući na umu mogućnost korištenja samo-nadziranog učenja. Arhitektura se temelji na transformerima. Za evaluaciju su korišteni javno dostupni skupovi podataka. Rješenje je implementirano u programskom jeziku Python uz korištenje PyTorch biblioteke. Izvorni kod je dostupan na Github repozitoriju na github.com/StanislavPavlic/attentioncall.
Abstract (english)
Nanopore sequencing is one of the state-of-the-art sequencing technologies. It passes a DNA sample through a pore which changes the ionic current in the pore. Due to the size of the pore, there are usually five nucleotides (5-mer) present in the pore influencing the measured signal. Each of the 1024 possible 5-mers produces a different signal, and this information is used for basecalling (converting the raw signal to a sequence of nucleotides). The signal is approximately rectangular because the 5-mer changes one nucleotide at a time, but there is a lot of noise present. The goal of this thesis was to develop a DNA nanopore sequencing basecaller using modern deep learning architectures with self-supervised learning in mind. The architecture is mainly based on transformers. The basecaller was evaluated on publicly available datasets. The solution called AttentionCall was implemented in Python and the PyTorch library. The source code is available on GitHub at github.com/StanislavPavlic/attentioncall.
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:642231
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-01-13 15:50:00