在光测序中通过预期最大化推断蛋白质丰富度推断.
Javier Kipen1, Matthew Beauregard Smith2, Thomas Blom2
1KTH Royal Institute of Technology, Department of Intelligent Systems, Division of Information Science and Engineering, Malvinas väg 10, SE-100 44 Stockholm, Sweden.
bioRxiv : the preprint server for biology
|August 12, 2025
概括
我们开发了一个新的计算框架,用于分析测序数据,以量化蛋白质丰度. 这种方法显著提高了准确性,特别是在较低的错误率下,推进了单分子蛋白质组学.
科学领域:
- 蛋白质组学是指蛋白质组学.
- 计算生物学 计算生物学
- 生物技术是生物技术.
背景情况:
- 测序产生了大量的单数据,但缺乏用于定量蛋白质丰度估计的可靠方法.
- 现有的分类工具提供了有价值的投入,但需要整合到蛋白质水平量化策略中.
研究的目的:
- 从测序数据引入一个概率框架来准确估计蛋白质丰度.
- 开发一种可扩展的计算方法,将类分类和蛋白质量定量联系起来.
主要方法:
- 实施了一个基于预期最大化 (EM) 的概率框架来估计相对蛋白质丰度.
- 将现有类别工具的结果集成到EM算法中.
- 使用模拟的五种蛋白质混合物和大规模的人类蛋白质组模拟来评估性能.
主要成果:
- 基于EM的框架显著降低了相对丰度的平均绝对误差,而不是统一的丰度猜测.
- 该方法证明了可扩展性,在标准和高性能计算系统上高效地处理数百万个读数.
- 精度改进在较低的测序错误率下更为明显,这表明未来进步的潜力.
结论:
- 基于EM的推断提供了一个可扩展的,基于模型的解决方案,用于使用光测序数据的定量蛋白质组学.
- 该框架增强了蛋白质丰度估计,并可以作为其他推理方法的改进步骤.
- 测序技术的改进与这种计算方法相结合,有望实现更精确的高通量单分子蛋白质组学.
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