Pregled bibliografske jedinice broj: 948685
End-to-End Deep Learning Model for Base Calling of MinION Nanopore Reads
End-to-End Deep Learning Model for Base Calling of MinION Nanopore Reads, 2018., diplomski rad, Fakultet Elektrotehnike i Računarstva, Zagreb
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Naslov
End-to-End Deep Learning Model for Base Calling of MinION Nanopore Reads
Autori
Miculinić, Neven
Vrsta, podvrsta i kategorija rada
Ocjenski radovi, diplomski rad
Fakultet
Fakultet Elektrotehnike i Računarstva
Mjesto
Zagreb
Datum
05.07
Godina
2018
Stranica
47
Mentor
Šikić, Mile
Ključne riječi
base calling ; Oxford Nanopore Technologies ; MinION ; deep learning, seq2seq, convolutional neural network ; residual network ; CTC loss
(base calling ; Oxford Nanopore Technologies ; MinION ; deep learning ; seq2seq ; convolutional neural network ; residual network ; CTC loss)
Sažetak
The MinION device by Oxford Nanopore Technologies is the first portable DNA sequencing device. Main advantages include producing longer reads than competing technologies and real-time data analysis making it suitable for a wide array of possible applications. Although long reads of up to 882 000 bp can be achieved, this comes at a cost - an error rate of 10% or higher. The goal of this thesis is to explore novel basecaller training technique using multi- task training and autoencoder loss as a secondary task to improve performance of basecalling. The model has been trained on R9.4 E.Coli dataset and has been compared with contemporary solutions on Klebsiella pneumoniae dataset. The complete source code is available on https://github.com/nmiculinic/minion-basecaller/.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
