使用Libsais直接构建稀疏后数组
Simon Van de Vyver1, Tibo Vande Moortele1, Peter Dawyndt1
1Department of Mathematics, Computer Science and Statistics, Faculty of Sciences, Ghent University, Ghent, 9000, Belgium.
BMC bioinformatics
|October 17, 2025
概括
我们开发了一种通过编码文本直接构建稀疏后数组 (SSA) 的新方法,将内存和构建时间减少了50-75%. 这种方法绕过了完整的后数组构建,以实现高效的生物信息学数据搜索.
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
- 数据结构 数据结构
背景情况:
- 模式匹配对于基因组学,转录组学和蛋白质组学至关重要.
- 后数组是大型数据集的关键索引结构.
- 稀疏后数组 (SSA) 减少了内存,但通常需要先构建完整的后数组,从而导致高开销.
研究的目的:
- 介绍一种用于直接稀疏后数组构建的新方法.
- 为了减少SSAs的内存使用和构建时间.
- 在大型生物数据集中提高模式匹配的效率.
主要方法:
- 开发了一种文本编码方法,以分组字符并减少输入文本的长度.
- 扩展了Libsais库,从编码文本直接构建SSA.
- 实施了针对较高稀疏度因子的分样采集策略.
主要成果:
- 在核酸和氨基酸数据集上,SSAs (k=3,4) 的内存使用和构建时间减少了50-75%.
- 根据字母大小,对8个稀疏度因子表现出类似的性能增长,取决于字母大小.
- 这种方法对于生物信息学中常见的小字母来说特别有效.
结论:
- 介绍了一个有效的方法来构建稀疏后数组.
- 通过Libsais进行文本转换和直接SSA构建显著提高了执行时间和内存使用量.
- 该方法将输入文本和SSA长度减少k的因素.
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