PQSDC:通过序列分区和运行长度预测映射来获得质量评分数据的平行无损压缩机
Hui Sun1, Yingfeng Zheng1, Haonan Xie2
1Nankai-Baidu Joint Laboratory, Parallel and Distributed Software Technology Laboratory, TMCC, SysNet, DISSec, GTIISC, College of Computer Science, Nankai University, Tianjin 300350, China.
Bioinformatics (Oxford, England)
|May 17, 2024
概括
一个新的平行无损算法,PQSDC,有效地压缩来自测序技术的质量得分数据 (QSD). 它显著减少了时间和内存成本,同时改善了短和长读数的压缩比.
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
- 数据压缩数据压缩
背景情况:
- 质量评分数据 (QSD) 构成了短和长读数序列的压缩FastQ文件的很大一部分 (70%).
- QSD的高效压缩对于大规模的基因组学数据共享和长期存档至关重要,平衡压缩比,时间和内存.
- 现有的压缩方法可能无法最佳地解决QSD的特定特征.
研究的目的:
- 引入一种新的平行无损压缩算法,PQSDC,专门为质量得分数据 (QSD) 设计.
- 为了实现 QSD 压缩的压缩比,时间效率和内存消耗之间的平衡.
- 为了利用多核CPU集群进行高度并发的压缩和解压缩过程.
主要方法:
- 开发PQSDC,一个平行无损QSD专用压缩算法.
- 实施并行序列分区模型以减少峰值内存和时间成本.
- 整合一个平行四级运行长度预测映射模型,以提高压缩比.
- 设计用于使用多核CPU集群的高并发性.
主要成果:
- 在27个现实数据集上,PQSDC表现出了与最先进的方法相比的显著改进.
- 对于短读,PQSDC实现了高达7.06%的平均压缩比,以及相当大的时间/内存节省 (分别高达79.96%和68.34%).
- 对于长时间阅读,PQSDC在加权平均压缩比上显示了高达13.42%的改善,显著的时间/内存节省 (分别高达72.53%和19.44%).
- 在压缩强度方面,PQSDC排名第二,这表明它能够抵御QSD分布的变化.
结论:
- PQSDC为并行QSD压缩提供了一个有前途的解决方案,有效平衡存储成本,时间消耗和内存使用.
- 算法的并行设计和专用模型有助于其在压缩QSD方面的卓越性能.
- PQSDC可供下载,以促进其在基因组学数据管理和档案工作流程中的采用.
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