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快速多个序列对齐通过多臂强盗

Kayvon Mazooji¹, Ilan Shomorony¹

¹Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States.

Bioinformatics (Oxford, England)

|June 28, 2024

概括

这项研究引入了使用自适应得分估计的多个序列对齐的更快方法,减少了计算时间而不牺牲准确性. 这种方法加速了UPP等生物信息学工具中的序列到模型分配.

科学领域:

计算生物学计算生物学
生物信息学是一种生物信息学.
机器学习机器学习

背景情况:

多个序列对齐对于理解蛋白质进化和功能至关重要.
准确对准大型生物数据集是计算密集的.
像UPP这样的现有方法依赖于计算上昂贵的隐藏马尔科夫模型 (HMM) 评分.

研究的目的:

为了在多个序列对齐中加速序列到HMM的分配步骤.
为了保持对齐准确度,同时显著减少计算时间.
开发一种适应性方法,以有效估计得分.

主要方法:

用高效估计的替代分数取代精确的HMM概率得分.
使用多臂强盗算法进行自适应性得分估计.
将该方法应用于大型数据集,重点关注长序列.

主要成果:

与已建立的UPP软件相比,实现了类似的对齐精度.
显示了计算时间的显著减少.
对于包含长序列的数据集来说,加快速度尤其显著.

结论:

适应性得分估计为多个序列对齐提供了一个计算效率高的替代方案.
拟议的方法提高了生物信息学工具用于大规模序列分析的可扩展性.
这种方法可以广泛应用于加速其他基于HMM的计算生物学任务.

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