Reference-free lossless compression of nanopore sequencing reads using an approximate assembly approach.
2023; 13 (1): 2082
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[Formula: see text] 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[Formula: see text] faster decompression with 20 threads). NanoSpring is available on GitHub at https://github.com/qm2/NanoSpring .
View details for DOI 10.1038/s41598-023-29267-8
View details for PubMedID 36747011
Learned Compression of High Dimensional Image Datasets
IEEE. 2022: 1747-1751
View details for DOI 10.1109/CVPRW56347.2022.00184
View details for Web of Science ID 000861612701079