Chapter title |
Analyzing Tandem Mass Spectra Using the DRIP Toolkit: Training, Searching, and Post-Processing
|
---|---|
Chapter number | 12 |
Book title |
Data Mining for Systems Biology
|
Published in |
Methods in molecular biology, January 2018
|
DOI | 10.1007/978-1-4939-8561-6_12 |
Pubmed ID | |
Book ISBNs |
978-1-4939-8560-9, 978-1-4939-8561-6
|
Authors |
John T. Halloran, Halloran, John T. |
Abstract |
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. |
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