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PEPerMINT:使用图形神经网络在基于质谱的蛋白质组学中进行丰度归算

Tobias Pietz¹, Sukrit Gupta^1,2, Christoph N Schlaffner^1,3

¹Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, 14482, Germany.

Bioinformatics (Oxford, England)

|September 4, 2024

概括

新型图形神经网络PEPerMINT有效地归因了质谱学中缺少的丰度数据. 这种方法通过解决缺失值和提供不确定性估计来改善定量蛋白质组学.

科学领域:

蛋白质组学是指蛋白质组学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

定量蛋白质组学依赖质谱法 (MS) 来估计蛋白质的丰度.
无标签,自下而上的MS协议将蛋白质消化为,以量化.
缺失的丰度值,通常超过50%,阻碍了精确的蛋白质丰度分析.

研究的目的:

开发一种可靠的方法,用于在定量蛋白质组学中赋予缺失的丰度值.
提高下游蛋白质组数据分析的准确性和可靠性.

主要方法:

建议PEPerMINT,一个在类水平上运行的图形神经网络模型.
包含与蛋白的关系和氨基酸序列信息.
与6个不同的数据集 (细胞系,组织,血) 的11种归算方法进行了基准测试.

主要成果:

PEPerMINT的表现始终优于11种常见的归算方法.
在不同的失踪级别和评估策略中保持了高预测性能.
在微分表达式预测中证明有效性.

结论:

PEPerMINT提供了一种优质的解决方案,用于归因缺失的丰度数据.
提供有价值的不确定性估计,允许用户根据可靠性量身定制归算.
提高了定量蛋白质组分析的准确性和可靠性.

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