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肽段和蛋白质鉴定的错误发现率计算

Calculation of False Discovery Rate for Peptide and Protein Identification.

作者信息

Prieto Gorka, Vázquez Jesús

机构信息

Department of Communications Engineering, Faculty of Engineering of Bilbao, University of the Basque Country (UPV/EHU), Bilbao, Spain.

Laboratory of Cardiovascular Proteomics, Centro Nacional de Investigaciones Cardiovasculares (CNIC) and CIBER de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain.

出版信息

Methods Mol Biol. 2020;2051:145-159. doi: 10.1007/978-1-4939-9744-2_6.

Abstract

Shotgun proteomics is the method of choice for large-scale protein identification. However, the use of a robust statistical workflow to validate such identification is mandatory to minimize false matches, ambiguities, and amplification of error rates from spectra to proteins. In this chapter we emphasize the key concepts to take into account when processing the output of a search engine to obtain reliable peptide or protein identifications. We assume that the reader is already familiar with tandem mass spectrometry so we can focus on the use of statistical confidence methods. After introducing the key concepts we present different software tools and how to use them with an example dataset.

摘要

鸟枪法蛋白质组学是大规模蛋白质鉴定的首选方法。然而,必须使用强大的统计工作流程来验证此类鉴定,以尽量减少错误匹配、模糊性以及从光谱到蛋白质的错误率放大。在本章中,我们强调在处理搜索引擎输出以获得可靠的肽或蛋白质鉴定时需要考虑的关键概念。我们假设读者已经熟悉串联质谱,因此我们可以专注于统计置信度方法的使用。在介绍关键概念之后,我们展示不同的软件工具以及如何使用示例数据集来使用它们。

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