A Comprehensive Evaluation of Consensus Spectrum Generation Methods in Proteomics

Abstract

Spectrum clustering is a powerful strategy to minimize redundant mass spectra by grouping them based on similarity, with the aim of forming groups of mass spectra from the same repeatedly measured analytes. Each such group of near-identical spectra can be represented by its so-called consensus spectrum for downstream processing. Although several algorithms for spectrum clustering have been adequately benchmarked and tested, the influence of the consensus spectrum generation step is rarely evaluated. Here, we present an implementation and benchmark of common consensus spectrum algorithms, including spectrum averaging, spectrum binning, the most similar spectrum, and the best-identified spectrum. We have analyzed diverse public data sets using two different clustering algorithms (spectra-cluster and MaRaCluster) to evaluate how the consensus spectrum generation procedure influences downstream peptide identification. The BEST and BIN methods were found the most reliable methods for consensus spectrum generation, including for data sets with post-translational modifications (PTM) such as phosphorylation. All source code and data of the present study are freely available on GitHub at .

Discovery of N-Acyl Amino Acids and Novel Related N-, O-Acyl Lipids by Integrating Molecular Networking and an Extended In Silico Spectral Library

“…In general, highly similar spectra were compiled into a high-quality representative spectrum, which was considered a consensus spectrum. , One key aspect for creating a consensus spectrum is filtering the original spectra and clustering similar spectra. On examining the annotations derived from MN, the MS/MS spectra of NAAs (or STAs) were found to depend on the amino acid moiety, regardless of the fatty acyls (or bile acids) linked.…”

“…24 Generally, highly similar spectra are compiled into a high-quality representative spectrum, which is considered as a consensus spectrum. 25 Generating consensus spectra is useful for visualizing clustering results, e.g., each MN node represents a precursor ion and the associated consensus spectrum compiled from several highly similar spectra. 12 It also helps the data-driven construction of spectral libraries, e.g., sophisticated spectrum clustering algorithms developed for data processing of proteomics.…”

“…The consensus spectrum is initially proposed for peptide identification in proteomics . Generally, highly similar spectra are compiled into a high-quality representative spectrum, which is considered as a consensus spectrum . Generating consensus spectra is useful for visualizing clustering results, e.g., each MN node represents a precursor ion and the associated consensus spectrum compiled from several highly similar spectra .…”

HyperSpec: Ultrafast Mass Spectra Clustering in Hyperdimensional Space

“…For example, Wang et al reported using spectral clustering to reduce the runtime of subsequent peptide identification by over 50%. Third, the downstream analysis can achieve better results by operating on high-quality consensus spectra with a higher signal-to-noise ratio compared to the raw spectra …”

“…Third, the downstream analysis can achieve better results by operating on high-quality consensus spectra with a higher signal-to-noise ratio compared to the raw spectra. 10 Previous spectral clustering tools have focused on optimizing clustering quality and clustering speed. For example, MS-Cluster 5 and spectra-cluster 8 use an iterative greedy approach to efficiently merge similar spectra.…”