Pregled bibliografske jedinice broj: 915066
MinCall | MinION end2end convolutional deep learning basecaller
MinCall | MinION end2end convolutional deep learning basecaller // 2nd International workshop on deep learning for precision medicine, ECML-PKDD 2017
Skopje, Sjeverna Makedonija, 2017. str. 1-8 (predavanje, međunarodna recenzija, ostalo, znanstveni)
CROSBI ID: 915066 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
MinCall | MinION end2end convolutional deep learning basecaller
Autori
Miculinić, Neven ; Ratković, Marko ; Šikić, Mile
Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, ostalo, znanstveni
Izvornik
2nd International workshop on deep learning for precision medicine, ECML-PKDD 2017
/ - , 2017, 1-8
Skup
2nd International workshop on deep learning for precision medicine, ECML-PKDD 2017
Mjesto i datum
Skopje, Sjeverna Makedonija, 18.09.2017. - 22.09.2017
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Basecaller, MinION, R9, CNN, CTC, Next generation sequencing
Sažetak
The Oxford Nanopore Technologies's MinION is the first portable DNA sequencing device. It is capable of producing long reads, over 100 kBp were reported. However, it has significantly higher error rate than other methods. In this study, we present MinCall, an end2end basecaller model for the MinION. The model is based on deep learning and uses convolutional neural networks (CNN) in its implementation. For extra performance, it uses cutting edge deep learning techniques and architectures, batch normalization and Connectionist Temporal Classification (CTC) loss. The best performing deep learning model achieves 91.4% median match rate on E. Coli dataset using R9 pore chemistry and 1D reads.
Izvorni jezik
Engleski
Znanstvena područja
Biologija, Računarstvo
