MS2Grouper: Group assessment and synthetic replacement of duplicate proteomic tandem mass spectra

Abstract

Shotgun proteomics experiments require the collection of thousands of tandem mass spectra; these sets of data will continue to grow as new instruments become available that can scan at even higher rates. Such data contain substantial amounts of redundancy with spectra from a particular peptide being acquired many times during a single LC-MS/MS experiment. In this article, we present MS2Grouper, an algorithm that detects spectral duplication, assesses groups of related spectra, and replaces these groups with synthetic representative spectra. Errors in detecting spectral similarity are corrected using a paraclique criterion-spectra are only assessed as groups if they are part of a clique of at least three completely interrelated spectra or are subsequently added to such cliques by being similar to all but one of the clique members. A greedy algorithm constructs a representative spectrum for each group by iteratively removing the tallest peaks from the spectral collection and matching to peaks in the other spectra. This strategy is shown to be effective in reducing spectral counts by up to 20% in LC-MS/MS datasets from protein standard mixtures and proteomes, reducing database search times without a concomitant reduction in identified peptides. (J Am Soc Mass Spectrom 2005, 16, 1250 -1261

Lessons Learned from Exploring the Backtracking Paradigm on the GPU

“…Maximal clique enumeration (MCE) is ubiquitous in real world problems. Examples of the uses of MCE include identification of common secondary structure elements of proteins [7], detection of protein-protein interaction complexes [19], clustering of similar mass spectrometry spectra [17], and detection of social heirarchy from email communications [15]. Thus, efficient MCE algorithms are of high value.…”

Determination of peptide and protein ion charge states by fourier transformation of isotope-resolved mass spectra

“…Afterwards, the standard mixture was digested with 200 ng of trypsin for 1 h at 37°C in 100 l of 80% acetonitrile/20% 50 mM Tris HCl/10 mM CaCl 2 (pH 7.6). For training the decision tree (vide infra), the trypsin digest of an "extended" PSM was used [17].…”

“…Ultra Zoom Scans and identifications from an "extended" PSM containing 20 proteins [17] were used to generate a decision tree for assigning precursor charge states (see Figure 2b). This process, conducted in the R statistical environment (www.r-project.org), determined the thresholds that would most reduce the Gini impurity [22] of mixed spectra with known charge states.…”

Exploring the potential of public proteomics data

“…When assessing the similarity of spectra, spectral clustering can be performed 53, 54, 55, 56. While transitive identifications and consensus or representative spectra have been reported in all of these studies, the concept was further developed in the creation of spectral archives 57.…”