A ranking-based scoring function for peptide-spectrum matches.
A ranking-based scoring function for peptide-spectrum matches.
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DOI：
10.1021/pr800678b
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发表时间：
2009-05
影响因子：
4.4
通讯作者：
Frank AM
中科院分区：
文献类型：
--
作者：
Frank AM
目前产生的大量串联质谱（MS/MS）蛋白质组学数据的分析依赖于从质谱中识别肽的自动化算法。这些算法的一个重要组成部分是用于评估肽谱匹配（psm）质量的评分函数。在本文中，我们提出了一种新的psm评分方法。我们认为，由于这个问题的核心是一个排序任务（特别是在从头排序的情况下），因此可以使用机器学习排序算法有效地解决它。我们开发了一种新的基于区别性提升的评分方法。我们的评分模型利用大量不同的特征函数来衡量psm的不同质量。我们的方法提高了我们的从头排序算法的性能，超越了目前最先进的技术，也大大提高了数据库搜索程序的性能。此外，通过提高标签过滤的效率和提高PSM评分的灵敏度，我们使大规模MS/MS分析变得可行，例如人类基因组六帧翻译的蛋白质基因组搜索（与InsPecT数据库搜索工具相比，我们将运行时间减少了15倍，鉴定肽的数量增加了60%）。我们的评分功能被纳入PepNovo+，可下载或在线运行：http://bix.ucsd.edu。
The analysis of the large volume of tandem mass spectrometry (MS/MS) proteomics data that is generated these days relies on automated algorithms that identify peptides from their mass spectra. An essential component of these algorithms is the scoring function used to evaluate the quality of peptide-spectrum matches (PSMs). In this paper, we present new approach to scoring of PSMs. We argue that since this problem is at its core a ranking task (especially in the case of de novo sequencing), it can be solved effectively using machine learning ranking algorithms. We developed a new discriminative boosting-based approach to scoring. Our scoring models draw upon a large set of diverse feature functions that measure different qualities of PSMs. Our method improves the performance of our de novo sequencing algorithm beyond the current state-of-the-art, and also greatly enhances the performance of database search programs. Furthermore, by increasing the efficiency of tag filtration and improving the sensitivity of PSM scoring, we make it practical to perform large-scale MS/MS analysis, such as proteogenomic search of a six-frame translation of the human genome (in which we achieve a reduction of the running time by a factor of 15 and a 60% increase in the number of identified peptides, compared to the InsPecT database search tool). Our scoring function is incorporated into PepNovo+ which is available for download or can be run online at: http://bix.ucsd.edu.
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影响因子：
4.4
作者：
Colinge, J;Cusin, I;Bougueleret, L
通讯作者：
Bougueleret, L
影响因子：
5.8
作者：
Craig, R;Beavis, RC
通讯作者：
Beavis, RC
影响因子：
12.3
作者：
Fermin, Damian;Allen, Baxter B.;Blackwell, Thomas W.;Menon, Rajasree;Adamski, Marcin;Xu, Yin;Ulintz, Peter;Omenn, Gilbert S.;States, David J.
通讯作者：
States, David J.
DOI：
10.1002/bms.1200190607
发表时间：
1990-06-01
期刊：
BIOMEDICAL AND ENVIRONMENTAL MASS SPECTROMETRY
影响因子：
--
作者：
BARTELS, C
通讯作者：
BARTELS, C
影响因子：
3.4
作者：
Colinge, J;Masselot, A;Bougueleret, L
通讯作者：
Bougueleret, L