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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >PRIMA: PEPTIDE ROBUST IDENTIFICATION FROM MS/MS SPECTRA
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PRIMA: PEPTIDE ROBUST IDENTIFICATION FROM MS/MS SPECTRA

机译：原始资料：从MS / MS SPECTRA中鉴定肽的稳健性

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摘要

In proteomics, tandem mass spectrometry is the key technology for peptide sequencing. However, partially due to the deficiency of peptide identification software, a large portion of the tandem mass spectra are discarded in almost all proteomics centers because they are not interpretable. The problem is more acute with the lower quality data from low end but more popular devices such as the ion trap instruments. In order to deal with the noisy and low quality data, this paper develops a systematic machine learning approach to construct a robust linear scoring function, whose coefficients are determined by a linear programming. A prototype, PRIMA, was implemented. When tested with large benchmarks of varying qualities, PRIMA consistently has higher accuracy than commonly used software MASCOT, SEQUEST and X! Tandem.
机译：在蛋白质组学中，串联质谱是肽测序的关键技术。但是，部分由于肽段识别软件的缺乏，几乎所有蛋白质组学中心都放弃了大部分串联质谱，因为它们无法解释。问题来自低端的质量较低的数据，而离子阱仪器等更流行的设备则更为严重。为了处理嘈杂和低质量的数据，本文开发了一种系统的机器学习方法来构造鲁棒的线性评分函数，其系数由线性编程确定。实现了PRIMA原型。当使用各种质量的大型基准进行测试时，PRIMA始终具有比常用软件MASCOT，SEQUEST和X高的准确性！一前一后

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