Abstract
Background
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.
Methods
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.
Results
Our proposed base-caller, WaveNano, achieves good performance on real MinION sequencing data from Lambda phage.
Conclusions
The signal-level nanopore base-callerWaveNano can obtain higher base-calling accuracy, and generate fewer insertions/deletions in the base-called sequences.
Article PDF
Similar content being viewed by others
References
Cao, M. D., Nguyen, S. H., Ganesamoorthy, D., Elliott, A. G., Cooper, M. A. and Coin, L. J. (2017) Scaffolding and completing genome assemblies in real-time with nanopore sequencing. Nat. Commun., 8, 14515
Loman, N. J., Quick, J. and Simpson, J. T. (2015) A complete bacterial genome assembled de novo using only nanopore sequencing data. Nat. Methods, 12, 733–735
Li, Y., Han, R., Bi, C., Li, M., Wang, S. and Gao, X. (2018) DeepSimulator: a deep simulator for nanopore sequencing. Bioinformatics, 34, 2899–2908
Jain, M., Fiddes, I. T., Miga, K. H., Olsen, H. E., Paten, B. and Akeson, M. (2015) Improved data analysis for the MinION nanopore sequencer. Nat. Methods, 12, 351–356
Lu, H., Giordano, F. and Ning, Z. (2016) Oxford Nanopore MinION sequencing and genome assembly. Genom. Proteom. Bioinf., 14, 265–279
Quick, J., Loman, N. J., Duraffour, S., Simpson, J. T., Severi, E., Cowley, L., Bore, J. A., Koundouno, R., Dudas, G., Mikhail, A., et al. (2016) Real-time, portable genome sequencing for Ebola surveillance. Nature, 530, 228–232
Castro-Wallace, S. L., Chiu, C. Y., John, K. K., Stahl, S. E., Rubins, K. H., McIntyre, A. B. R., Dworkin, J. P., Lupisella, M. L., Smith, D. J., Botkin, D. J., et al. (2017) Nanopore DNA sequencing and genome assembly on the International Space Station. Sci. Rep., 7, 18022
Loose, M., Malla, S. and Stout, M. (2016) Real-time selective sequencing using nanopore technology. Nat. Methods, 13, 751–754
Jain, M., Olsen, H. E., Paten, B. and Akeson, M. (2016) The Oxford Nanopore MinION: delivery of nanopore sequencing to the genomics community. Genome Biol., 17, 239
Goodwin, S., Gurtowski, J., Ethe-Sayers, S., Deshpande, P., Schatz, M. C. and McCombie, W. R. (2015) Oxford Nanopore sequencing, hybrid error correction, and de novo assembly of a eukaryotic genome. Genome Res., 25, 1750–1756
Sovic, I., Šikic, M., Wilm, A., Fenlon, S. N., Chen, S. and Nagarajan, N. (2016) Fast and sensitive mapping of error-prone nanopore sequencing reads with GraphMap. Nat Commun., 7, 11307
Szalay, T. and Golovchenko, J. A. (2015) De novo sequencing and variant calling with nanopores using PoreSeq. Nat. Biotechnol., 33, 1087–1091
David, M., Dursi, L. J., Yao, D., Boutros, P. C. and Simpson, J. T. (2017) Nanocall: an open source basecaller for Oxford Nanopore sequencing data. Bioinformatics, 33, 49–55
Boža, V., Brejová, B. and Vinar, T. (2017) DeepNano: deep recurrent neural networks for base calling in MinION nanopore reads. PLoS One, 12, e0178751
Van Den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., and Kavukcuoglu K. (2016) Wavenet: A generative model for raw audio. ArXiv, 1609.03499
Hochreiter, S. and Schmidhuber, J. (1997) Long short-term memory. Neural Comput., 9, 1735–1780
Chung, J., Gulcehre, C., Cho, K. H. and Bengio, Y. (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. ArXiv, 1412.3555
LeCun, Y., Bengio, Y. and Hinton, G. (2015) Deep learning. Nature, 521, 436–444
He, K., Zhang, X., Ren, S., and Sun, J. (2016) Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas
Hirschberg, J. and Manning, C. D. (2015) Advances in natural language processing. Science, 349, 261–266
Wang, S., Sun, S., Li, Z., Zhang, R. and Xu, J. (2017) Accurate de novo prediction of protein contact map by ultra-deep learning model. PLoS Comput. Biol., 13, e1005324
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. and Lipman, D. J. (1990) Basic local alignment search tool. J. Mol. Biol., 215, 403–410
Pearson, W. R. and Miller, W. (1992) Dynamic programming algorithms for biological sequence comparison. In Methods in Enzymology. pp. 575–601, Elsevier
Wang, S., Ma, J. and Xu, J. (2016) AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields. Bioinformatics, 32, i672–i679
McIntyre, A. B., Rizzardi, L., Yu, A. M., Alexander, N., Rosen, G. L., Botkin, D. J., Stahl, S. E., John, K. K., Castro-Wallace, S. L., McGrath, K., et al. (2016) Nanopore sequencing in microgravity. npj Microgravity, 2, 16035
Teng, H., Cao, M. D., Hall, M. B., Duarte, T., Wang, S. and Coin, L. J. M. (2018) Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning. Gigascience, 7, giy037
Han, R., Li, Y., Wang, S. and Gao, X. (2017) An accurate and rapid continuous wavelet dynamic time warping algorithm for unbalanced global mapping in nanopore sequencing. bioRxiv, 238857
van den Oord, A., Kalchbrenner, N., Vinyals, O., Espeholt, L., Graves, A., and Kavukcuoglu, K. (2016) Conditional image generation with pixelcnn decoders. In Advances in Neural Information Processing Systems
Wang S., Sun S., and Xu J. (2016) AUC-maximized deep convolutional neural fields for protein sequence labeling. In Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2016. Lecture Notes in Computer Science, Frasconi P., Landwehr N., Manco G., Vreeken J. (eds) vol 9852. Springer, Cham
Calders T., and Jaroszewicz S. (2007) Efficient AUC optimization for classification. In Knowledge Discovery in Databases: PKDD 2007. Lecture Notes in Computer Science, Kok J. N., Koronacki J., Lopez de Mantaras R., Matwin S., Mladenic D., Skowron A. (eds), vol 4702. Springer, Berlin, Heidelberg
Acknowledgements
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.
Author information
Authors and Affiliations
Corresponding authors
Additional information
Author summary: Oxford nanopore sequencing is a rapidly developed sequencing technology in recent years. Despite the benefits of this technique 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 many applications. Here we show that such indel errors are largely due to the segmentation process on the input electrical current signals, and propose a new deep learning model bi-directional WaveNet to perform the base-calling directly on the signal level. The experimental result suggests that our method achieves good performance on real nanopore sequencing data from Lambda phage.
Rights and permissions
About this article
Cite this article
Wang, S., Li, Z., Yu, Y. et al. WaveNano: a signal-level nanopore base-caller via simultaneous prediction of nucleotide labels and move labels through bi-directional WaveNets. Quant Biol 6, 359–368 (2018). https://doi.org/10.1007/s40484-018-0155-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40484-018-0155-4