Quantitative Biology

, Volume 6, Issue 4, pp 359–368 | Cite as

WaveNano: a signal-level nanopore base-caller via simultaneous prediction of nucleotide labels and move labels through bi-directional WaveNets

  • Sheng Wang
  • Zhen Li
  • Yizhou Yu
  • Xin Gao
Methodology Article



The Oxford MinION nanopore sequencer is the recently appealing third-generation genome sequencing device that is portable and no larger than a cellphone. Despite the benefits of MinION to sequence ultra-long reads in real-time, the high error rate of the existing base-calling methods, especially indels (insertions and deletions), prevents its use in a variety of applications.


In this paper, we show that such indel errors are largely due to the segmentation process on the input electrical current signal from MinION. All existing methods conduct segmentation and nucleotide label prediction in a sequential manner, in which the errors accumulated in the first step will irreversibly influence the final base-calling. We further show that the indel issue can be significantly reduced via accurate labeling of nucleotide and move labels directly from the raw signal, which can then be efficiently learned by a bi-directionalWaveNet model simultaneously through feature sharing. Our bi-directional WaveNet model with residual blocks and skip connections is able to capture the extremely long dependency in the raw signal. Taking the predicted move as the segmentation guidance, we employ the Viterbi decoding to obtain the final base-calling results from the smoothed nucleotide probability matrix.


Our proposed base-caller, WaveNano, achieves good performance on real MinION sequencing data from Lambda phage.


The signal-level nanopore base-callerWaveNano can obtain higher base-calling accuracy, and generate fewer insertions/deletions in the base-called sequences.


nanopore sequencing bi-directional WaveNets base-calling third generation sequencing deep learning 



We thank Minh Duc Cao and Lachlan J. M. Coin for providing the nanopore sequencing data for the Lambda phage sample. We thank Haotian Teng for providing helpful discussions. This work was supported by the Kind Abdullah Unviersity of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Awards Nos. FCC/1/1976-04, URF/1/2601-01, URF/1/3007-01, URF/1/3412-01 and URF/1/3450-01.


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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computational Bioscience Research Center (CBRC)King Abdullah University of Science and Technology (KAUST)ThuwalKingdom of Saudi Arabia
  2. 2.Department of Computer ScienceUniversity of Hong KongHong Kong SARChina
  3. 3.School of Science and Engineering, Shenzhen Research Institute of Big DataThe Chinese University of Hong KongShenzhenChina

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