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Adaptive Sequencing with Base calling

ABSTRACT: Demonstrating the use of Adaptive Nanopore Sequencing (Read Until) for target enrichment, mixed samples and more.

PROVIDER: PRJEB36644 | ENA |

REPOSITORIES: ENA

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Dataset's files

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			Action	DRS
	ERR3950923_1.fastq.gz	Fastqsanger.gz
	ERR3950924_1.fastq.gz	Fastqsanger.gz
	ERR3950925_1.fastq.gz	Fastqsanger.gz
	ERR3950926_1.fastq.gz	Fastqsanger.gz
	ERR3956276_1.fastq.gz	Fastqsanger.gz

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Project description: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. 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. https://github.com/fmfi-compbio/coral-basecaller. Supplementary data are available at Bioinformatics online.

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