Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computational Bioscience Research Center (CBRC)
MetadataShow full item record
AbstractSequencing 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 desktop computer graphics processing units.
CitationTeng H, Cao MD, Hall MB, Duarte T, Wang S, et al. (2018) Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning. GigaScience 7. Available: http://dx.doi.org/10.1093/gigascience/giy037.
SponsorsWe thank Jianhua Guo for contributing the DNA for the E. coli sample. We thank Arnold Bainomugisa for extracting DNA for the M. tuberculosis sample. We thank Sheng Wang and Han Qiao for the helpful discussion. We thank Jain et al.  for the open Human nanopore dataset.
PublisherOxford University Press (OUP)
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