Supporting data for "Chiron: Translating nanopore raw signal directly into nucleotide sequence using deep learning"

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, making it competitive with other deep-learning basecalling algorithms.

Keywords:

• ont nanopore sequencing
• deep learning
• artifcial neural network
• comparative performance

Read the peer-reviewed publication(s):

• Teng, H., Cao, M. D., Hall, M. B., Duarte, T., Wang, S., & Coin, L. J. M. (2018). Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning. GigaScience, 7(5). https://doi.org/10.1093/gigascience/giy037 (PubMed:29648610)
• Teng, H., Cao, M. D., Hall, M. B., Duarte, T., Wang, S., & Coin, L. J. M. (2019). Correction to: Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning. GigaScience, 8(5). https://doi.org/10.1093/gigascience/giz049 (PubMed:31077312)

Additional information:

https://github.com/haotianteng/chiron

https://pypi.python.org/pypi/chiron

https://github.com/nanopore-wgs-consortium/NA12878

Accessions (data included in GigaDB):

BioProject: PRJNA386696
SRA: SRP136964