A Spectral Clustering Approach to MS/MS Identification of Post-Translational Modifications

Falkner, Jayson A.; Falkner, Jarret W.; Yocum, Anastasia K.; Andrews, Philip C.

doi:10.1021/pr800226w

Cited by 74 publications

(73 citation statements)

References 26 publications

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“…We also, for the first time, proposed the FDR estimation of propagating annotations through a spectral network. Although some approaches have proposed the identification of modified peptides by propagating annotations (39–41), none has fully addressed FDR estimation. Our approach ensures a conservative estimate of the FDR and is increasingly rigorous for higher-modified peptides.…”

Section: Discussionmentioning

confidence: 99%

Multi-species Identification of Polymorphic Peptide Variants via Propagation in Spectral Networks

Payne

Bandeira

2016

Molecular & Cellular Proteomics

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Peptide and protein identification remains challenging in organisms with poorly annotated or rapidly evolving genomes, as are commonly encountered in environmental or biofuels research. Such limitations render tandem mass spectrometry (MS/MS) database search algorithms ineffective as they lack corresponding sequences required for peptide-spectrum matching. We address this challenge with the spectral networks approach to (1) match spectra of orthologous peptides across multiple related species and then (2) propagate peptide annotations from identified to unidentified spectra. We here present algorithms to assess the statistical significance of spectral alignments (Align-GF), reduce the impurity in spectral networks, and accurately estimate the error rate in propagated identifications. Analyzing three related Cyanothece species, a model organism for biohydrogen production, spectral networks identified peptides from highly divergent sequences from networks with dozens of variant peptides, including thousands of peptides in species lacking a sequenced genome. Our analysis further detected the presence of many novel putative peptides even in genomically characterized species, thus suggesting the possibility of gaps in our understanding of their proteomic and genomic expression. A web-based pipeline for spectral networks analysis is available at http://proteomics.ucsd.edu/software.

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Section: Discussionmentioning

confidence: 99%

Multi-species Identification of Polymorphic Peptide Variants via Propagation in Spectral Networks

Payne

Bandeira

2016

Molecular & Cellular Proteomics

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“…These spectral pairs are then constructed into networks of related spectra, from which more informative virtual spectra can be generated to facilitate de novo peptide sequencing, and also unanticipated modifications can be identified by propagation of database search results. Similarly, the Bonanza algorithm of Falkner et al (44) uses an improved version of the common spectral-dot-product similarity with shifted mass matching window to cluster related spectral pairs. Ahrne et al (45) proposed to construct an online library of spectra confidently identified by sequence database search and subsequently search the remaining unidentified spectra against the spectral library with a very large precursor mass tolerance.…”

Section: Liquid Chromatography Coupled With Tandem Mass Spectrometrymentioning

confidence: 99%

DeltAMT: A Statistical Algorithm for Fast Detection of Protein Modifications From LC-MS/MS Data

Xiu

Jia

et al. 2011

Molecular & Cellular Proteomics

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Identification of proteins and their modifications via liquid chromatography-tandem mass spectrometry is an important task for the field of proteomics. However, because of the complexity of tandem mass spectra, the majority of the spectra cannot be identified. The presence of unanticipated protein modifications is among the major reasons for the low spectral identification rate. The conventional database search approach to protein identification has inherent difficulties in comprehensive detection of protein modifications. In recent years, increasing efforts have been devoted to developing unrestrictive approaches to modification identification, but they often suffer from their lack of speed. This paper presents a statistical algorithm named DeltAMT (Delta Accurate Mass and Time) for fast detection of abundant protein modifications from tandem mass spectra with high-accuracy precursor masses. The algorithm is based on the fact that the modified and unmodified versions of a peptide are usually present simultaneously in a sample and their spectra are correlated with each other in precursor masses and retention times. By representing each pair of spectra as a delta mass and time vector, bivariate Gaussian mixture models are used to detect modification-related spectral pairs. Unlike previous approaches to unrestrictive modification identification that mainly rely upon the fragment information and the mass dimension in liquid chromatography-tandem mass spectrometry, the proposed algorithm makes the most of precursor information. Thus, it is highly efficient while being accurate and sensitive. On two published data sets, the algorithm effectively detected various modifications and other interesting events, yielding deep insights into the data. Based on these discoveries, the spectral identification rates were significantly increased and many modified peptides were identified. Molecular & Cellular Proteomics 10: 10.1074/ mcp.M110.000455, 1-15, 2011.Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) 1 is currently the predominant technology used to identify proteins and their modifications (1-3). The most successful approach for interpreting tandem mass spectra involves searching the database of known protein sequences (4 -9). Other approaches include the databaseindependent de novo peptide sequencing (10 -13) and the hybrid sequence tag-based approach (14 -16). However, in a typical shotgun proteomics experiment, only ϳ10 -30% of the tandem mass spectra can be successfully identified, and the remaining majority is discarded (17). Many factors contribute to the complexity of protein digests and the low identification rate of tandem mass spectra (18). Understanding the origin and identity of the unidentified spectra is of great importance in expanding our knowledge about biological systems and sample processing protocols. Nesvizhskii et al. (19) have shown that by properly mining the unidentified spectra, insights can be gained that are of interest to biologists, such as identification of post-transl...

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“…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.…”

Section: Introductionmentioning

confidence: 99%

Exploring the potential of public proteomics data

et al. 2015

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In a global effort for scientific transparency, it has become feasible and good practice to share experimental data supporting novel findings. Consequently, the amount of publicly available MS‐based proteomics data has grown substantially in recent years. With some notable exceptions, this extensive material has however largely been left untouched. The time has now come for the proteomics community to utilize this potential gold mine for new discoveries, and uncover its untapped potential. In this review, we provide a brief history of the sharing of proteomics data, showing ways in which publicly available proteomics data are already being (re‐)used, and outline potential future opportunities based on four different usage types: use, reuse, reprocess, and repurpose. We thus aim to assist the proteomics community in stepping up to the challenge, and to make the most of the rapidly increasing amount of public proteomics data.

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A Spectral Clustering Approach to MS/MS Identification of Post-Translational Modifications

Cited by 74 publications

References 26 publications

Multi-species Identification of Polymorphic Peptide Variants via Propagation in Spectral Networks

Multi-species Identification of Polymorphic Peptide Variants via Propagation in Spectral Networks

DeltAMT: A Statistical Algorithm for Fast Detection of Protein Modifications From LC-MS/MS Data

Exploring the potential of public proteomics data

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