Nanopore Base Calling on the Edge
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Abstract (arXiv)
We developed a new base caller DeepNano-coral for nanopore sequencing, which is optimized to run on the Coral Edge Tensor Processing Unit, a small USB-attached hardware accelerator. To achieve this goal, we have designed new versions of two key components used in convolutional neural networks for speech recognition and base calling. In our components, we propose a new way of factorization of a full convolution into smaller operations, which decreases memory access operations, memory access being a bottleneck on this device. DeepNano-coral achieves real-time base calling during sequencing with the accuracy slightly better than the fast mode of the Guppy base caller and is extremely energy efficient, using only 10W of power. Availability: https://github.com/fmfi-compbio/coral-basecaller
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Nov 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 1; 2025: 0; 2026: 1.
5 papers.
- 2024Two Sparse Matrices are Better than One: Sparsifying Neural Networks with Double Sparse Factorizationcited by 6
- 2022TargetCall: Eliminating the Wasted Computation in Basecalling via Pre-Basecalling Filteringcited by 2
- 2021Dynamic Pooling Improves Nanopore Base Calling Accuracycited by 0
- 2022DNA data storage, sequencing data-carrying DNAcited by 0
- 2026CRANE: Correcting Errors in Raw Nanopore Signals Using Hidden Markov Modelscited by 0
- 2026CRANE: Correcting Errors in Raw Nanopore Signals Using Hidden Markov Models20 Mar 2026
- 2024Two Sparse Matrices are Better than One: Sparsifying Neural Networks with Double Sparse Factorization27 Sep 2024
- 2022TargetCall: Eliminating the Wasted Computation in Basecalling via Pre-Basecalling Filtering9 Dec 2022
- 2022DNA data storage, sequencing data-carrying DNA11 May 2022
- 2021Dynamic Pooling Improves Nanopore Base Calling Accuracy16 May 2021