Spectral archives: extending spectral libraries to analyze both identified and unidentified spectra

Abstract

MS/MS experiments generate multiple, nearly identical spectra of the same peptide in various laboratories, but proteomics researchers typically do not leverage the unidentified spectra produced in other labs to decode spectra generated in their own labs. We propose a spectral archives approach that clusters MS/MS datasets, representing similar spectra by a single consensus spectrum. Spectral archives extend spectral libraries by analyzing both identified and unidentified spectra in the same way and maintaining information about spectra of peptides shared across species and conditions. Thus archives offer both traditional library spectrum similarity-based search capabilities along with novel ways to analyze the data. By developing a clustering tool, MS-Cluster, we generated a spectral archive from ~1.18 billion spectra that greatly exceeds the size of existing spectral repositories. We advocate that publicly available data should be organized into spectral archives, rather than be analyzed as disparate datasets, as is mostly the case today.

Lifestyle chemistries from phones for individual profiling

“…Molecular networking is a MS visualization approach that matches MS/MS similarities and also uses the relationships between MS/MS spectra to dereplicate MS/MS signatures by matching them to reference MS/ MS spectra of known chemicals (36)(37)(38). Briefly, molecular networking first uses MScluster algorithm (39) to merge all identical spectra, followed by spectral alignment to compare pairs of related MS/MS spectra via cosine similarity scoring to generate familial groupings, where 1 indicates a perfect match. We recently developed the infrastructure to perform these tasks via a web interface at gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp (40).…”

Proteogenomics: Integrating Next-Generation Sequencing and Mass Spectrometry to Characterize Human Proteomic Variation

“…The reference isoform may be many orders of magnitude higher in abundance than the alternative isoform and the high sequence overlap of the two isoforms makes unambiguous identification of the alternative isoform difficult. Some solutions to this problem include the use of multiple enzymatic digestions to produce a complementary set of peptides, as was recently demonstrated for HeLa cells (95); to rely on massive spectral clustering databases, which could group as-of-yet unidentified peptides from unusual samples (96); or to employ targeted proteomics methods such as selected reaction monitoring (SRM) to detect possible peptide variants at higher sensitivity.…”

Beauty is only skin deep

“…Molecular networking allows for tractable analysis of a large number of MS/MS spectra obtained in various experiments and highlights molecular families of chemically similar molecules (14)(15)(16)(17). Briefly, to reduce the redundancy due to many spectra potentially generated for identical molecules, MSCluster, originally designed for proteomics experiments, was adapted to merge identical and nearly identical MS/MS spectra (18), and the resulting consensus spectra were further matched between each other by using a spectral alignment algorithm that calculates a cosine score to detect pairs of spectra with highly correlated fragmentation patterns (15,16,19). Cosine similarity scores range from 0 to 1, where 1 indicates perfectly matched MS/MS spectra.…”