使用GeneSqueeze对FASTQ/A文件进行无损和无引用的压缩
Foad Nazari1, Sneh Patel1, Melissa LaRocca1
1Rajant Health Incorporated, 200 Chesterfield Parkway, Malvern, PA, 19355PA, USA.
Scientific reports
|January 2, 2025
概括
GeneSqueeze提供了一种新的无损压缩方法来测序数据,大大减少了对omics分析的存储需求. 这种无参考算法实现了高压缩比,没有数据丢失,有助于生物医学研究.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 高吞吐量测序产生了大量的数据文件,要求对OMIC分析提供高效的存储解决方案.
- 纵向研究加剧了存储挑战,阻碍了生物医学研究中测序技术的全部潜力.
- 当前的压缩方法可能无法充分满足测序数据的特定需求,从而影响存储和传输成本.
研究的目的:
- 介绍GeneSqueeze,一个新的无损,无引用的压缩算法用于FASTQ/A文件.
- 与gzip和SPRING等现有方法相比,评估GeneSqueeze的压缩性能和数据完整性.
- 为了解决对OMIC分析中高效存储解决方案的关键需求.
主要方法:
- 开发了GeneSqueeze,这是一个基于Python的,用于FASTQ/A文件的无引用压缩算法.
- 实现了一个基于文件分布的自动调整压缩协议.
- 将GeneSqueeze与gzip和SPRING进行比较,使用压缩比和数据丢失等指标.
主要成果:
- 基因挤压实现了高达gzip的三倍的压缩比率,其比率与SPRING相美.
- 基因挤压和gzip都确保了所有FASTQ文件组件的100%无损压缩.
- SPRING的模式显示非ACGTN核酸和元数据的数据丢失,与GeneSqueeze和gzip.zip不同.
结论:
- GeneSqueeze是一种竞争性,专门的核酸序列文件压缩方法.
- 该算法显著降低了大型omics数据集的存储和传输成本.
- 基因挤压保护数据完整性,使其对生物医学研究和个性化患者护理具有价值.
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