Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning
Haotian Teng, Duc Cao Minh, Michael B Hall, Tania Duarte, Sheng Wang, Lachlan JM Coin
GIGASCIENCE | OXFORD UNIV PRESS | Published : 2018
Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology that 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 and directly translate the raw signal to DNA sequence without the error-prone segmentation step. Trained with only a small set of 4,000 reads, we show that our model provides state-of-the-art basecalling accuracy, even on previously unseen species. Chiron achieves basecalling speeds of more than 2,000 bases per second using deskto..View full abstract
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Awarded by NHMRC career development fellowship
Awarded by ARC
LC is supported by an NHMRC career development fellowship (GNT1130084). The research is supported by an ARC research grant (DP170102626). MH is supported by a Westpac Future Leaders Scholarship (2016) awarded by the Westpac Bicentennial Foundation.