Accelerated nanopore basecalling with SLOW5 data format

Hiruna Samarakoon; James M Ferguson; Hasindu Gamaarachchi; Ira W Deveson

doi:10.1093/bioinformatics/btad352

Accelerated nanopore basecalling with SLOW5 data format

Bioinformatics. 2023 Jun 1;39(6):btad352. doi: 10.1093/bioinformatics/btad352.

Authors

Hiruna Samarakoon^{1

2

3}, James M Ferguson^{1

2}, Hasindu Gamaarachchi^{1

2

3}, Ira W Deveson^{1

2

4}

Affiliations

¹ Genomics Pillar, Garvan Institute of Medical Research, Sydney, NSW 2010, Australia.
² Centre for Population Genomics, Garvan Institute of Medical Research and Murdoch Children's Research Institute, Australia.
³ School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia.
⁴ Faculty of Medicine, University of New South Wales, Sydney, NSW 2052, Australia.

Abstract

Motivation: Nanopore sequencing is emerging as a key pillar in the genomic technology landscape but computational constraints limiting its scalability remain to be overcome. The translation of raw current signal data into DNA or RNA sequence reads, known as 'basecalling', is a major friction in any nanopore sequencing workflow. Here, we exploit the advantages of the recently developed signal data format 'SLOW5' to streamline and accelerate nanopore basecalling on high-performance computing (HPC) and cloud environments.

Results: SLOW5 permits highly efficient sequential data access, eliminating a potential analysis bottleneck. To take advantage of this, we introduce Buttery-eel, an open-source wrapper for Oxford Nanopore's Guppy basecaller that enables SLOW5 data access, resulting in performance improvements that are essential for scalable, affordable basecalling.

Availability and implementation: Buttery-eel is available at https://github.com/Psy-Fer/buttery-eel.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Genome
Genomics
High-Throughput Nucleotide Sequencing
Nanopores*
Sequence Analysis, DNA / methods
Software*