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MinCall | MinION end2end convolutional deep learning basecaller (CROSBI ID 656824)

Prilog sa skupa u zborniku | sažetak izlaganja sa skupa | međunarodna recenzija

Miculinić, Neven ; Ratković, Marko ; Šikić, Mile MinCall | MinION end2end convolutional deep learning basecaller // 2nd International workshop on deep learning for precision medicine, ECML-PKDD 2017. 2017. str. 1-8

Podaci o odgovornosti

Miculinić, Neven ; Ratković, Marko ; Šikić, Mile

engleski

MinCall | MinION end2end convolutional deep learning basecaller

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.

Basecaller, MinION, R9, CNN, CTC, Next generation sequencing

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Podaci o prilogu

1-8.

2017.

objavljeno

Podaci o matičnoj publikaciji

2nd International workshop on deep learning for precision medicine, ECML-PKDD 2017

Podaci o skupu

2nd International workshop on deep learning for precision medicine, ECML-PKDD 2017

predavanje

18.09.2017-22.09.2017

Skopje, Sjeverna Makedonija

Povezanost rada

Biologija, Računarstvo