Vladimír Boža, Broňa Brejová, Tomáš Vinař. DeepNano: Deep Recurrent Neural Networks for Base Calling in MinION Nanopore Reads. Technical Report arXiv:1603.09195, arXiv preprints, 2016.
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Download from publisher: http://arxiv.org/abs/1603.09195
Related web page: http://compbio.fmph.uniba.sk/deepnano/
Bibliography entry: BibTeX
Abstract:
Motivation: The MinION device by Oxford Nanopore is the first portable sequencing device. MinION is able to produce very long reads (reads over 100~kBp were reported), however it suffers from high sequencing error rate. In this paper, we show that the error rate can be reduced by improving the base calling process. Results: We present the first open-source DNA base caller for the MinION sequencing platform by Oxford Nanopore. By employing carefully crafted recurrent neural networks, our tool improves the base calling accuracy compared to the default base caller supplied by the manufacturer. This advance may further enhance applicability of MinION for genome sequencing and various clinical applications. Availability: DeepNano can be downloaded at http://compbio.fmph.uniba.sk/deepnano/
Last modified: 31.03.2016
Contact: Tomáš Vinař, KAI FMFI UK, Mlynská Dolina, 84248 Bratislava, Slovakia
Broňa Brejová, KI FMFI UK, Mlynská Dolina, 84248 Bratislava, Slovakia
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