Dynamic Pooling Improves Nanopore Base Calling Accuracy.

In nanopore sequencing, electrical signal is measured as DNA molecules pass through the sequencing pores. Translating these signals into DNA bases (base calling) is a highly non-trivial task, and its quality has a large impact on the sequencing accuracy. The most successful nanopore base callers to date use convolutional neural networks (CNN) to accomplish the task. Convolutional layers in CNNs are typically composed of filters with constant window size, performing best in analysis of signals with uniform speed. However, the speed of nanopore sequencing varies greatly both within reads and between sequencing runs. Here, we present dynamic pooling, a novel neural network component, which addresses this problem by adaptively adjusting the pooling ratio. To demonstrate the usefulness of dynamic pooling, we developed two base callers: Heron and Osprey. Heron improves the accuracy beyond the experimental high-accuracy base caller Bonito developed by Oxford Nanopore. Osprey is a fast base caller that can compete in accuracy with Guppy high-accuracy mode, but does not require GPU acceleration and achieves a near real-time speed on common desktop CPUs. Availability: https://github.com/fmfi-compbio/osprey, https://github.com/fmfi-compbio/heron.

Nanopore base calling on the edge.

MOTIVATION: MinION is a portable nanopore sequencing device that can be easily operated in the field with features including monitoring of run progress and selective sequencing. To fully exploit these features, real-time base calling is required. Up to date, this has only been achieved at the cost of high computing requirements that pose limitations in terms of hardware availability in common laptops and energy consumption. RESULTS: 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 10 W of power. AVAILABILITY AND IMPLEMENTATION: https://github.com/fmfi-compbio/coral-basecaller. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DeepNano-blitz: a fast base caller for MinION nanopore sequencers.

MOTIVATION: Oxford Nanopore MinION is a portable DNA sequencer that is marketed as a device that can be deployed anywhere. Current base callers, however, require a powerful GPU to analyze data produced by MinION in real time, which hampers field applications. RESULTS: We have developed a fast base caller DeepNano-blitz that can analyze stream from up to two MinION runs in real time using a common laptop CPU (i7-7700HQ), with no GPU requirements. The base caller settings allow trading accuracy for speed and the results can be used for real time run monitoring (i.e. sample composition, barcode balance, species identification, etc.) or prefiltering of results for more detailed analysis (i.e. filtering out human DNA from human-pathogen runs). AVAILABILITY AND IMPLEMENTATION: DeepNano-blitz has been developed and tested on Linux and Intel processors and is available under MIT license at https://github.com/fmfi-compbio/deepnano-blitz. CONTACT: vladimir.boza@fmph.uniba.sk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.