Graphical Models for Peptide Identification of Tandem Mass Spectra

Abstract

Graphical models (GMs) provide a flexible framework for modeling phenomena. In the past few decades, GMs have become indispensable tools for machine learning and computational biology. They afford a wide range of modeling granularity, from restricting only exact events of interest to occur to allowing all possible events in a phenomenon’s event space. For all such modeling considerations, GMs afford efficient algorithms to perform inference over the probabilistic quantities of interest. In this thesis, we show how GMs may be leveraged to improve both identification accuracy and search runtime of tandem mass (MS/MS) spectra. For the majority of existing MS/MS scoring algorithms, we give equivalent GMs and show how search time may be algorithmically improved. We present GMs for posterior based (sum-product) and max-product inference which offer state-of-the-art performance and, most importantly, are amenable to efficient parameter estimation. Furthermore, we show how a GM which generatively models the stochastic process by which peptides produce MS/MS spectra may be utilized to calculate features for improved classification between correctly and incorrectly identified spectra, leading to significantly improved identification accuracy.