Reference-free lossless compression of nanopore sequencing reads using an approximate assembly approach

Abstract

The amount of data produced by genome sequencing experiments has been growing rapidly over the past several years, making compression important for efficient storage, transfer and analysis of the data. In recent years, nanopore sequencing technologies have seen increasing adoption since they are portable, real-time and provide long reads. However, there has been limited progress on compression of nanopore sequencing reads obtained in FASTQ files since most existing tools are either general-purpose or specialized for short read data. We present NanoSpring, a reference-free compressor for nanopore sequencing reads, relying on an approximate assembly approach. We evaluate NanoSpring on a variety of datasets including bacterial, metagenomic, plant, animal, and human whole genome data. For recently basecalled high quality nanopore datasets, NanoSpring, which focuses only on the base sequences in the FASTQ file, uses just 0.35–0.65 bits per base which is 3–6$$\times$$ × lower than general purpose compressors like gzip. NanoSpring is competitive in compression ratio and compression resource usage with the state-of-the-art tool CoLoRd while being significantly faster at decompression when using multiple threads (> 4$$\times$$ × faster decompression with 20 threads). NanoSpring is available on GitHub at https://github.com/qm2/NanoSpring.

Portable nanopore-sequencing technology: Trends in development and applications

“…This has prompted researchers to study long-read long compression techniques in greater depth. To reduce the size of raw FASTQ raw sequencing data, several compression techniques have been proposed, including Picopore, ENANO, RENANO, NanoSpring, FastqCLS, and CoLoRd ( Gigante, 2017 ; Dufort y Álvarez et al, 2020 , 2021 ; Meng et al, 2021 ; Kokot et al, 2022 ; Lee and Song, 2022 ). As an example, Picopore ( Gigante, 2017 ) provides a software suite that contains three compression methods: raw, lossless and deep lossless compression.…”

“…In comparison to ENANO, RENANO ( Dufort y Álvarez et al, 2021 ) achieves significantly improved compression of read sequences but is limited to aligned data with a usable reference. In contrast, NanoSpring ( Meng et al, 2021 ) is a reference-free tool that relies on approximate assembly methods in order to achieve compression gains, but it requires more time and memory to accomplish compression gains. The FastqCLS ( Lee and Song, 2022 ) compression algorithm uses read reordering to compress long reads of long sequencing data without sacrificing information and performs well in terms of compression ratio.…”