ABSTRACT
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 the first deep learning model - Chiron - that can directly translate the raw signal to DNA sequence, without the error-prone segmentation step. We show that our model provides state-of-the-art basecalling accuracy when trained with only a small set of 4000 reads. Chiron achieves basecalling speeds of over 2000 bases per second using desktop computer graphics processing units, making it competitive with other deep-learning-based basecalling algorithms.
