对序列数据库搜索工具进行大规模评估,用于基于同质性的蛋白质功能预测
Chengxin Zhang1,2, P Lydia Freddolino1,2
1Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
bioRxiv : the preprint server for biology
|November 28, 2023
概括
优化序列搜索参数对于使用同质转移准确预测蛋白质功能至关重要. 默认情况下,BLASTp和MMseqs2的性能最好,但DIAMOND可以与适当的设置相匹配.
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:
- 基于同质性的功能转移是预测蛋白质功能的关键方法,通常使用基因本体学 (GO) 术语.
- 选择最佳的序列搜索工具和参数对于准确的功能预测至关重要,但尚未得到充分探索.
研究的目的:
- 评估不同序列搜索工具及其参数设置对蛋白质功能预测的影响.
- 确定基于同质性的功能转移最有效的工具和配置.
主要方法:
- 在大型基准数据集上对流行的序列搜索工具 (BLASTp,MMseqs2,DIAMOND) 的比较分析.
- 对每个工具的各种搜索参数设置进行系统评估.
主要成果:
- BLASTp和MMseqs2在GO期预测的默认参数下表现优于其他工具,包括DIAMOND.
- 优化的参数设置使得 DIAMOND 能够实现与 BLASTp 和 MMseqs2.2 相当的性能.
结论:
- 在基于同质性的函数转移中,序列搜索工具的性能高度依赖于参数配置.
- 仔细选择和调整搜索参数对于最大限度地提高蛋白质功能预测的准确性至关重要.
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