在生物时间序列中识别局部关联:算法,统计意义和应用
Dongmei Ai1, Lulu Chen1, Jiemin Xie2
1School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China.
Briefings in bioinformatics
|November 6, 2023
概括
局部相似性分析通过检查时间序列数据中的时空相关性来揭示生物系统动态. 这些算法适用于多omics测序,对于理解基因共同表达和微生物相互作用至关重要.
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
- 系统生物学 系统生物学
背景情况:
- 生物系统通过局部关联表现出复杂的动态,例如时间依赖的基因共同表达和季节性微生物相互作用.
- 分析生物时间序列数据是揭示这些复杂的动态和固有的系统相互作用的关键.
研究的目的:
- 为生物时间序列数据提供局部相似性分析算法的全面概述.
- 讨论这些算法的历史发展,最近的进展,统计属性和应用.
- 突出这些方法的适应性和加速性,用于下一代测序和多omics数据集.
主要方法:
- 对时间序列对齐的局部相似性分析算法和统计方法的审查.
- 检查算法的历史演变和最近的改进.
- 在生物数据分析中评估统计属性和现实应用.
主要成果:
- 当地相似性分析算法已经从微阵列数据显著演变为先进的多omics下一代测序数据集.
- 这些方法有效地识别了时空相关性,揭示了生物系统的动态.
- 审查提供了关于这些分析工具的性能和实用性的综合资源.
结论:
- 局部相似性分析是使用时间序列数据剖析复杂生物系统的强大方法.
- 这些算法的调整大大提高了它们在现代生物研究中的影响,特别是在多omics研究中.
- 免费使用的基准数据和脚本有助于可复制性和该领域的进一步研究.
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