RT Journal Article
SR Electronic
T1 Chiron: Translating nanopore raw signal directly into nucleotide sequence using deep learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 179531
DO 10.1101/179531
A1 Haotian Teng
A1 Minh Duc Cao
A1 Michael B. Hall
A1 Tania Duarte
A1 Sheng Wang
A1 Lachlan J.M. Coin
YR 2018
UL http://biorxiv.org/content/early/2018/02/26/179531.abstract
AB 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.