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Nanopore sequencing is being rapidly adopted in genomics. We recently developed SLOW5, a new file format with advantages for storage and analysis of raw signal data from nanopore experiments. Here we introduce slow5tools , an intuitive toolkit for handling nanopore data in SLOW5 format. Slow5tools enables lossless data conversion and a range of tools for interacting with SLOW5 files. Slow5tools uses multi-threading, multi-processing, and other engineering strategies to achieve fast data conversion and manipulation, including live FAST5-to-SLOW5 conversion during sequencing. We provide examples and benchmarking experiments to illustrate slow5tools usage, and describe the engineering principles underpinning its performance.

Nanopore sequencing depends on the FAST5 file format, which does not allow efficient parallel analysis. Here we introduce SLOW5, an alternative format engineered for efficient parallelization and acceleration of nanopore data analysis. Using the example of DNA methylation profiling of a human genome, analysis runtime is reduced from more than two weeks to approximately 10.5 h on a typical high-performance computer. SLOW5 is approximately 25% smaller than FAST5 and delivers consistent improvements on different computer architectures. Nanopore sequencing data are rapidly analyzed with parallel data access.

Nanopore sequencing is an increasingly central tool for genomics. Despite rapid advances in the field, large data volumes and computational bottlenecks continue to pose major challenges. Here we introduce ex-zd, a new data compression strategy that helps address the large size of raw signal data generated during nanopore experiments. Ex-zd encompasses both a lossless compression method, which modestly outperforms all current methods for nanopore signal data compression, and a ‘lossy’ method, which can be used to achieve dramatic additional savings. The latter component works by reducing the number of bits used to encode signal data. We show that the three least significant bits in signal data generated on instruments from Oxford Nanopore Technologies (ONT) predominantly encode noise. Their removal reduces file sizes by half without impacting downstream analyses, including basecalling and detection of DNA methylation. Ex-zd compression saves hundreds of gigabytes on a single ONT sequencing experiment, thereby increasing the scalability, portability and accessibility of nanopore sequencing.

Nanopore sequencing is an emerging technology that is being rapidly adopted in research and clinical genomics. We recently developed SLOW5, a new file format for storage and analysis of raw data from nanopore sequencing experiments. SLOW5 is a community-centric, open source format that offers considerable performance benefits over the existing nanopore data format, known as FAST5. Here we introduce slow5tools, a simple, intuitive toolkit for handling nanopore raw signal data in SLOW5 format. Slow5tools enables lossless FAST5-to-SLOW5 and SLOW5-to-FAST5 data conversion, and a range of tools for structuring, indexing, viewing and querying SLOW5 files. Slow5tools uses multi-threading, multi-processing and other engineering strategies to achieve fast data conversion and manipulation, including live FAST5-to-SLOW5 conversion during sequencing. We outline a series of examples and benchmarking experiments to illustrate slow5tools usage, and describe the engineering principles underpinning its high performance. Slow5tools is an essential toolkit for handling nanopore signal data, which was developed to support adoption of SLOW5 by the nanopore community. Slow5tools is written in C/C++ with minimal dependencies and is freely available as an open-source program under an MIT licence: https://github.com/hasindu2008/slow5tools.

Nanopore sequencing is an emerging genomic technology with great potential. However, the storage and analysis of nanopore sequencing data have become major bottlenecks preventing more widespread adoption in research and clinical genomics. Here, we elucidate an inherent limitation in the file format used to store raw nanopore data – known as FAST5 – that prevents efficient analysis on high-performance computing (HPC) systems. To overcome this, we have developed SLOW5, an alternative file format that permits efficient parallelisation and, thereby, acceleration of nanopore data analysis. For example, we show that using SLOW5 format, instead of FAST5, reduces the time and cost of genome-wide DNA methylation profiling by an order of magnitude on common HPC systems, and delivers consistent improvements on a wide range of different architectures. With a simple, accessible file structure and a ~25% reduction in size compared to FAST5, SLOW5 format will deliver substantial benefits to all areas of the nanopore community.