Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States.
Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States.
J Proteome Res. 2021 Apr 2;20(4):1966-1971. doi: 10.1021/acs.jproteome.0c01010. Epub 2021 Feb 17.
Proteomics studies rely on the accurate assignment of peptides to the acquired tandem mass spectra-a task where machine learning algorithms have proven invaluable. We describe mokapot, which provides a flexible semisupervised learning algorithm that allows for highly customized analyses. We demonstrate some of the unique features of mokapot by improving the detection of RNA-cross-linked peptides from an analysis of RNA-binding proteins and increasing the consistency of peptide detection in a single-cell proteomics study.
蛋白质组学研究依赖于将肽准确分配到获得的串联质谱——在这个任务中，机器学习算法已经被证明是非常有价值的。我们描述了 mokapot，它提供了一个灵活的半监督学习算法，允许进行高度定制的分析。我们通过改进 RNA 结合蛋白分析中 RNA 交联肽的检测，以及提高单细胞蛋白质组学研究中肽检测的一致性，展示了 mokapot 的一些独特功能。
