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Background <p>High-throughput sequencing technologies generate massive amounts of FASTQ data comprising nucleotide sequences, quality scores, and read identifiers, necessitating efficient compression to alleviate storage and transmission burdens. Compared to general-purpose compressors, specialized FASTQ compressors achieve higher compression performance by exploiting the inherent redundancy in FASTQ files. However, existing FASTQ-specialized compressors often suffer from limited data applicability and tend to over-optimize either compression ratio or compression speed at the expense of the other.</p> Results <p>We present zDUR, a reference-free FASTQ compressor designed for efficient and scalable handling of next-generation sequencing data across diverse platforms and sequencing data types. Benchmarking against six reference-free compressors on 15 representative datasets spanning four sequencing data types demonstrates that zDUR achieves a favorable overall balance between compression ratio and speed, with broad applicability across data types. In particular, on single-cell RNA-seq and spatial transcriptomics datasets, zDUR achieves over a tenfold increase in runtime performance while maintaining higher compression ratios than SPRING, one of the state-of-the-art reference-free FASTQ compressors.</p> Conclusions <p>zDUR offers a scalable and efficient solution for reference-free FASTQ compression, balancing performance, speed, and usability across diverse datasets.</p>

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zDUR: reference-free FASTQ compressor with high compression ratio and speed

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月23日

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  • Artem Ershov,
  • Renpeng Ding,
  • Qian Fu,
  • Ivan Kozlov,
  • Ekaterina Fadeeva,
  • Evgeniy Mozheiko,
  • Ming Ni,
  • Yong Hou,
  • Yan Zhou

摘要

Background

High-throughput sequencing technologies generate massive amounts of FASTQ data comprising nucleotide sequences, quality scores, and read identifiers, necessitating efficient compression to alleviate storage and transmission burdens. Compared to general-purpose compressors, specialized FASTQ compressors achieve higher compression performance by exploiting the inherent redundancy in FASTQ files. However, existing FASTQ-specialized compressors often suffer from limited data applicability and tend to over-optimize either compression ratio or compression speed at the expense of the other.

Results

We present zDUR, a reference-free FASTQ compressor designed for efficient and scalable handling of next-generation sequencing data across diverse platforms and sequencing data types. Benchmarking against six reference-free compressors on 15 representative datasets spanning four sequencing data types demonstrates that zDUR achieves a favorable overall balance between compression ratio and speed, with broad applicability across data types. In particular, on single-cell RNA-seq and spatial transcriptomics datasets, zDUR achieves over a tenfold increase in runtime performance while maintaining higher compression ratios than SPRING, one of the state-of-the-art reference-free FASTQ compressors.

Conclusions

zDUR offers a scalable and efficient solution for reference-free FASTQ compression, balancing performance, speed, and usability across diverse datasets.