2-AIN-506 a 2-AIN-252: Seminár z bioinformatiky (2) a (4)
Leto 2018

Haotian Teng, Michael B. Hall, Tania Duarte, Minh Duc Cao, Lachlan J.M. Coin. Chiron: Translating nanopore raw signal directly into nucleotide sequence using deep learning. Technical Report bioRxiv:179531, 2017.

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Download from publisher: https://doi.org/10.1101/179531

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Sequencing by translocating DNA fragments through an array of nanopores is a 
rapidly maturing technology which offers faster and cheaper sequencing than 
other approaches. However, accurately deciphering the DNA sequence from the 
noisy and complex electrical signal is challenging. Here, we report Chiron, 
the first deep learning model to achieve end-to-end basecalling: directly 
translating the raw signal to DNA sequence without the error-prone 
segmentation step. Trained with only a small set of 4000 reads, we show that 
our model provides state-of-the-art basecalling accuracy even on previously 
unseen species. Chiron achieves basecalling speeds of over 2000 bases per 
second using desktop computer graphics processing units.