mokapot: Fast and Flexible Semisupervised Learning for Peptide Detection.

mokapot: Fast and Flexible Semisupervised Learning for Peptide Detection.
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DOI:
10.1021/acs.jproteome.0c01010
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发表时间:
2021-04-02
影响因子:
4.4
通讯作者:
Noble WS
Noble WS
中科院分区:
生物学2区
文献类型:
--
作者:
Fondrie WE;Noble WS

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蛋白质组学研究依赖于肽的准确分配,以获得串联质谱-机器学习算法已被证明是无价的任务。我们描述mokapot,它提供了一个灵活的半监督学习算法,允许高度定制的分析。我们证明了一些独特的功能mokapot通过改善检测RNA交联肽从RNA结合蛋白的分析,并增加了肽检测的一致性,在单细胞蛋白质组学研究。
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.
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