An Unsupervised, Model-Free, Machine-Learning Combiner for Peptide Identifications from Tandem Mass Spectra

Clinical Proteomics

Chau-Wen Tseng¹  Nathan Edwards²  Xue Wu¹ 

[1] Department of Computer Science, University of Maryland, College Park, USADepartment of Computer Science, University of Maryland, College Park, USADepartment of Computer Science, University of Maryland, College Park, USA;Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, USADepartment of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, USADepartment of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, USA

关键词: Bioinformatics;    Peptide identification;    Tandem mass spectra;    Machine-learning;   

DOI  :  10.1007/s12014-009-9024-5

来源: Humana Press Inc

【 摘 要 】

Abstract

As the speed of mass spectrometers, sophistication of sample fractionation, and complexity of experimental designs increase, the volume of tandem mass spectra requiring reliable automated analysis continues to grow. Software tools that quickly, effectively, and robustly determine the peptide associated with each spectrum with high confidence are sorely needed. Currently available tools that postprocess the output of sequence-database search engines use three techniques to distinguish the correct peptide identifications from the incorrect: statistical significance re-estimation, supervised machine learning scoring and prediction, and combining or merging of search engine results. We present a unifying framework that encompasses each of these techniques in a single model-free machine-learning framework that can be trained in an unsupervised manner. The predictor is trained on the fly for each new set of search results without user intervention, making it robust for different instruments, search engines, and search engine parameters. We demonstrate the performance of the technique using mixtures of known proteins and by using shuffled databases to estimate false discovery rates, from data acquired on three different instruments with two different ionization technologies. We show that this approach outperforms machine-learning techniques applied to a single search engine’s output, and demonstrate that combining search engine results provides additional benefit. We show that the performance of the commercial Mascot tool can be bested by the machine-learning combination of two open-source tools X!Tandem and OMSSA, but that the use of all three search engines boosts performance further still. The Peptide identification Arbiter by Machine Learning (PepArML) unsupervised, model-free, combining framework can be easily extended to support an arbitrary number of additional searches, search engines, or specialized peptide–spectrum match metrics for each spectrum data set. PepArML is open-source and is available from http://peparml.sourceforge.net.

【 授权许可】

Unknown