S-leaping:一种高效的下方采样方法,用于大规模的高通量测序数据
1Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
Bioinformatics (Oxford, England)
|June 24, 2023
概括
缩小样本的大型omics数据集在计算上具有挑战性. 我们开发了s-leaping,一种高效的方法,以及fadso,一种用于FASTQ文件的工具,可以提高速度并减少对omics数据分析的内存使用.
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:
- 测序覆盖面对OMIC研究设计至关重要.
- 低采样用于估计具有成本效益的测序覆盖范围.
- 大数据集使传统的下方采样计算密集.
研究的目的:
- 为了开发一个高效和准确的下方采样方法,大omics数据.
- 创建一个用户友好的工具,用于将该方法应用于FASTQ文件.
主要方法:
- 开发了一种近似的下方采样算法,称为s-leaping.
- 创建了一个名为fadso的基于C的工具,用于处理FASTQ数据.
- 对比了s-leaping和fadso与现有的下方采样方法.
主要成果:
- s-leaping 显示的性能高达比现有准确度可比的方法快39%.
- 在大型数据集上,fadso显示速度增加了12%,内存使用量降低了21%.
- fadso在并行计算设置中实现了高达40%的更高吞吐量.
结论:
- s-leaping提供了一种高效且准确的解决方案,用于减少大omics数据集的样本.
- fadso为FASTQ文件下采样提供了一种实用且高吞吐量的工具.
- 开发的方法有助于成本效益的设计和分析omics研究.
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