Comparison of Mascot and X!Tandem Performance for Low and High Accuracy Mass Spectrometry and the Development of an Adjusted Mascot Threshold

Brosch, Markus; Swamy, Sajani; Hubbard, Tim; Choudhary, Jyoti S.

doi:10.1074/mcp.m700293-mcp200

Cited by 61 publications

(64 citation statements)

References 28 publications

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“…Regarding MSMS of naturally occurring peptides, precursor mass acquisition with conventional mass spectrometers (mass accuracy, ϳ50 ppm) and subsequent filtering in the setting of "no enzyme" very often lead to ambiguous peptide identification (22). To cope with this issue, many peptidomics studies have used in-house databases containing a limited number of entries to identify peptides that would otherwise remain elusive (11,23).…”

Section: Table IImentioning

confidence: 99%

Snapshot Peptidomics of the Regulated Secretory Pathway

Sasaki

Satomi

Takao

et al. 2009

Molecular & Cellular Proteomics

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Neurons and endocrine cells have the regulated secretory pathway (RSP) in which precursor proteins undergo proteolytic processing by prohormone convertase (PC) 1/3 or 2 to generate bioactive peptides. Although motifs for PCmediated processing have been described ((R/K)X n (R/K) where n ‫؍‬ 0, 2, 4, or 6), actual processing sites cannot be predicted from amino acid sequences alone. We hypothesized that discovery of bioactive peptides would be facilitated by experimentally identifying signal peptide cleavage sites and processing sites. However, in vivo and in vitro peptide degradation, which is widely recognized in peptidomics, often hampers processing site determination. To obtain sequence information about peptides generated in the RSP on a large scale, we applied a brief exocytotic stimulus (2 min) to cultured endocrine cells and analyzed peptides released into supernatant using LC-MSMS. Of note, 387 of the 400 identified peptides arose from 19 precursor proteins known to be processed in the RSP, including nine peptide hormone and neuropeptide precursors, seven granin-like proteins, and three processing enzymes (PC1/3, PC2, and peptidyl-glycine ␣-amidating monooxygenase). In total, 373 peptides were informative enough to predict processing sites in that they have signal sequence cleavage sites, PC consensus sites, or monobasic cleavage sites. Several monobasic cleavage sites identified here were previously proved to be generated by PCs. Thus, our approach helps to predict processing sites of RSP precursor proteins and will expedite the identification of unknown bioactive peptides hidden in precursor sequences.

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Section: Table IImentioning

confidence: 99%

Snapshot Peptidomics of the Regulated Secretory Pathway

Sasaki

Satomi

Takao

et al. 2009

Molecular & Cellular Proteomics

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“…We have previously evaluated (Brosch et al 2008) the standard database search engine Mascot (Perkins et al 1999) and extended it with an improved semi-supervised machine learning algorithm, Percolator (Käll et al 2007), to develop Mascot Percolator, which provides highly accurate significance measures and results in much improved sensitivity (Brosch et al 2009). Moreover, Percolator provides two significance measures, the q-value (Storey and Tibshirani 2003;Käll et al 2008a;2008b) and the posterior error probability (PEP) (Käll et al 2008b;2008c).…”

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confidence: 99%

Shotgun proteomics aids discovery of novel protein-coding genes, alternative splicing, and “resurrected” pseudogenes in the mouse genome

Brosch¹,

Saunders²,

Frankish³

et al. 2011

Genome Res.

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118

125

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Recent advances in proteomic mass spectrometry (MS) offer the chance to marry high-throughput peptide sequencing to transcript models, allowing the validation, refinement, and identification of new protein-coding loci. We present a novel pipeline that integrates highly sensitive and statistically robust peptide spectrum matching with genome-wide protein-coding predictions to perform large-scale gene validation and discovery in the mouse genome for the first time. In searching an excess of 10 million spectra, we have been able to validate 32%, 17%, and 7% of all protein-coding genes, exons, and splice boundaries, respectively. Moreover, we present strong evidence for the identification of multiple alternatively spliced translations from 53 genes and have uncovered 10 entirely novel protein-coding genes, which are not covered in any mouse annotation data sources. One such novel protein-coding gene is a fusion protein that spans the Ins2 and Igf2 loci to produce a transcript encoding the insulin II and the insulin-like growth factor 2–derived peptides. We also report nine processed pseudogenes that have unique peptide hits, demonstrating, for the first time, that they are not just transcribed but are translated and are therefore resurrected into new coding loci. This work not only highlights an important utility for MS data in genome annotation but also provides unique insights into the gene structure and propagation in the mouse genome. All these data have been subsequently used to improve the publicly available mouse annotation available in both the Vega and Ensembl genome browsers (http://vega.sanger.ac.uk).

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“…Hass et al noted that high mass accuracy search benefits identification for low quality spectra, especially for phosphorylated peptides where fragment ions are low in intensity compared with phosphoric acid and water neutral loss peaks (35). Brosch et al evaluated Mascot and X!Tandem and show that Mascot is more sensitive to changes in the peptide mass tolerance parameter compared with X!Tandem and show benefit to applying accurate mass as a postsearch filter to a relaxed tolerance search (36). Hsieh et al also demonstrate that post search accurate mass filtering yields a greater number of identifications at a given false discovery rate using SEQUEST (37).…”

Section: Figmentioning

confidence: 99%

“…Subsequent years saw the publication of both commercial and open-source tools that implemented this approach. For a detailed review of specific search tools and a collection of search engine comparisons, please see (7)(8)(9)(10)(11)(12)(13)(14). The common elements of these tools and practical considerations guiding their use are the specific focus of this review.…”

mentioning

confidence: 99%

A Face in the Crowd: Recognizing Peptides Through Database Search

Eng

Searle²,

Clauser

et al. 2011

Molecular & Cellular Proteomics

170

172

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Peptide identification via tandem mass spectrometry sequence database searching is a key method in the array of tools available to the proteomics researcher. The ability to rapidly and sensitively acquire tandem mass spectrometry data and perform peptide and protein identifications has become a commonly used proteomics analysis technique because of advances in both instrumentation and software. Although many different tandem mass spectrometry database search tools are currently available from both academic and commercial sources, these algorithms share similar core elements while maintaining distinctive features. This review revisits the mechanism of sequence database searching and discusses how various parameter settings impact the underlying search. Molecular & Cellular Proteomics 10: 10.1074/ mcp.R111.009522, 1-9, 2011.Innovations in tandem mass spectrometry (MS/MS) 1 have enabled the rapid growth of proteomics for chemists and biologists alike. In addition to the evolution of the instrument hardware to acquire spectra more rapidly and sensitively, improvements to data analysis facilitates the process of identifying and quantifying peptides and proteins for a wide user base. Researchers new to the field can benefit from a review of the fundamentals of tandem mass spectrometry sequence database searching, and even experienced proteomics researchers may profit from considerations of practical strategies to maximize information extraction from data.In a typical shotgun proteomics experiment, the sample is first denatured to enable proteolysis. An enzyme such as trypsin is applied to cleave proteins to smaller peptide components. This peptide mixture is usually then separated on a liquid chromatography (LC) column; more complex samples may necessitate prior fractionation by strong cation exchange or isoelectric focusing. Peptides are subjected to ionizing voltage as they are electrosprayed from the column, and these ions are introduced into the near-vacuum environment of the mass spectrometer.In a tandem mass spectrometry experiment, the instrument will first acquire a survey or precursor scan, also referred to as an MS scan, which measures all intact peptide ions eluting into the mass spectrometer at that given time. One or more peptide ions are selected, sequentially isolated, fragmented, and the resulting fragment ions are measured to produce an MS/MS spectrum. This process is repeated to automatically acquire MS/MS spectra on as many different peptide ions as possible throughout the LC gradient. See Fig. 1 for a schematic showing the relationship between precursor ions in the MS scans and the resulting MS/MS scans. While the MS/MS spectrum contains the peptide fragmentation pattern, the experimental peptide mass and charge state are obtained from the precursor ion measure in the MS spectrum.To clarify terminology, the intact peptide ions in the MS scans are termed precursor ions. Fragment ions are also called product ions and MS/MS spectra are referred to as product ion spectra. MS and MS/MS spectra can also ...

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Comparison of Mascot and X!Tandem Performance for Low and High Accuracy Mass Spectrometry and the Development of an Adjusted Mascot Threshold

Cited by 61 publications

References 28 publications

Snapshot Peptidomics of the Regulated Secretory Pathway

Snapshot Peptidomics of the Regulated Secretory Pathway

Shotgun proteomics aids discovery of novel protein-coding genes, alternative splicing, and “resurrected” pseudogenes in the mouse genome

A Face in the Crowd: Recognizing Peptides Through Database Search

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