Nanopore Base Calling on the Edge

November 09, 2020 Β· Entered Twilight Β· πŸ› Bioinform.

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Repo contents: .gitignore, README.md, backend.py, basecall.py, networks, requirements.txt

Authors Peter PereΕ‘Γ­ni, VladimΓ­r BoΕΎa, Broňa BrejovΓ‘, TomΓ‘Ε‘ VinaΕ™ arXiv ID 2011.04312 Category cs.LG: Machine Learning Cross-listed q-bio.GN Citations 49 Venue Bioinform. Repository https://github.com/fmfi-compbio/coral-basecaller ⭐ 12 Last Checked 1 month ago
Abstract
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
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