Analyzing Tandem Mass Spectra Using the DRIP Toolkit: Training, Searching, and Post-Processing

• Published in: Methods in molecular biology (Clifton, N.J.) Vol. 1807; p. 163
• Main Author: Halloran, John T
• Format: Journal Article
• Language: English
• Published: United States 2018
• Subjects: Tandem mass spectrometry; Graphical models; Shotgun proteomics; DBNs; DRIP; Dynamic Bayesian networks
• ISSN: 1940-6029
• DOI: 10.1007/978-1-4939-8561-6_12

Tandem mass spectrometry (MS/MS) is a high-throughput technology used to identify the proteins present in a complex, biological sample. Critical to MS/MS is the ability to accurately identify the peptide responsible for producing each observed spectrum. Recently, a dynamic Bayesian network (DBN) approach was shown to achieve state-of-the-art accuracy for this peptide identification problem. Modeling the stochastic process by which a peptide produces an MS/MS spectrum, this DBN for Rapid Identification of Peptides (DRIP) uses probabilistic inference to efficiently determine the most probable alignment between a peptide and an observed spectrum. DRIP's dynamic alignment strategy improves upon standard "static" alignment strategies, which rely on fixed quantization of the temporal axis of MS/MS data, in several significant ways. In particular, DRIP allows learning non-linear shifts of the temporal axis and, owing to the generative nature of the model, accurate feature extraction for substantially improved discriminative analysis (i.e., Percolator post-processing), all of which are supported in the DRIP Toolkit (DTK). Herein we describe how DTK may be used to significantly improve MS/MS identification accuracy, as well as DTK's interactive features for fine-grained analysis, including on the fly inference and plotting attributes.