增强基因组突变数据存储优化,基于稀疏性不对称性的压缩
Youde Ding1,2, Yuan Liao1, Ji He2
1The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People's Hospital, Qingyuan, China.
Frontiers in genetics
|June 16, 2023
概括
一个新的算法,CA_SAGM,有效地压缩稀疏的基因组突变数据. 虽然COO提供了更快的压缩,但CA_SAGM在更快的解压方面表现出色,平衡了基因组数据挑战的两个方面.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 数据压缩数据压缩
背景情况:
- 高通量测序产生了大量的基因组数据,这给存储和传输带来了挑战.
- 高效的压缩算法对于管理大规模的基因组数据集至关重要.
研究的目的:
- 为稀疏的不对称基因组突变数据提出和评估一种新的压缩算法 (CA_SAGM).
- 为了比较CA_SAGM的性能与现有的方法,如坐标格式 (COO) 和压缩散列列 (CSC).
主要方法:
- 开发了CA_SAGM,使用第一行排序和逆Cuthill-McKee重新排序用于稀疏的基因组突变数据.
- 将压缩数据压缩成压缩细数行 (CSR) 格式.
- 从TCGA评估了九个单核酸变异 (SNV) 和六个副本数变异 (CNV) 数据集.
主要成果:
- COO表现出最好的压缩速度和比率,而CSC表现最差.
- CA_SAGM提供了最快的解压速度和最短的时间,优于COO和CSC.
- 增加数据稀疏性对所有方法的压缩/解压缩时间和速率产生了负面影响.
结论:
- CA_SAGM是一个高效的算法,平衡了稀疏基因组突变数据的压缩和解压缩性能.
- 算法的选择取决于压缩或解压缩速度是否优先考虑.
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