ArticleScientific reports2023
Reference-free lossless compression of nanopore sequencing reads using an approximate assembly approach.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 14 citations in OpenAlex.
- ModSeqR: an R package for efficient analysis of modified nucleotide data.BMC genomics · 2026Article
- OReO: optimizing read order for practical compression.Bioinformatics advances · 2025Article
- PMFFRC: a large-scale genomic short reads compression optimizer via memory modeling and redundant clustering.BMC bioinformatics · 2023Article
- Portable nanopore-sequencing technology: Trends in development and applications.Frontiers in microbiology · 2023Review
- Nanopore quality score resolution can be reduced with little effect on downstream analysis.Bioinformatics advances · 2022Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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[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 .
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.