Rapid and Accurate Peptide Identification from Tandem Mass Spectra

Park, Christopher Y.; Klammer, Aaron A.; Käll, Lukas; MacCoss, Michael J.; Noble, William S.

doi:10.1021/pr800127y

Cited by 179 publications

(199 citation statements)

References 25 publications

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“…Standard Computation of XCorr-The form of XCorr preferred in current implementations (3,8,12) is the so-called "fast" XCorr (8). Fast XCorr entails four steps of preprocessing the observed spectrum ( Fig.…”

Section: Methodsmentioning

confidence: 99%

Computing Exact p-values for a Cross-correlation Shotgun Proteomics Score Function

Howbert

Noble

2014

Molecular & Cellular Proteomics

107

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The core of every protein mass spectrometry analysis pipeline is a function that assesses the quality of a match between an observed spectrum and a candidate peptide. We describe a procedure for computing exact p-values for the oldest and still widely used score function, SEQUEST XCorr. The procedure uses dynamic programming to enumerate efficiently the full distribution of scores for all possible peptides whose masses are close to that of the spectrum precursor mass. Ranking identified spectra by p-value rather than XCorr significantly reduces variance because of spectrum-specific effects on the score. In combination with the Percolator postprocessor, the XCorr p-value yields more spectrum and peptide identifications at a fixed false discovery rate than Mascot, X!Tandem, Comet, and MS-GF؉ across a variety of data sets. Molecular & Cellular

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Section: Methodsmentioning

confidence: 99%

Computing Exact p-values for a Cross-correlation Shotgun Proteomics Score Function

Howbert

Noble

2014

Molecular & Cellular Proteomics

107

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“…A decoy database comprises protein sequences that have been shuffled or reversed, generated from the given target database beforehand or on-the-fly [33][34][35]. HiTMS uses this target-decoy strategy, generating decoys by shuffling the candidate lipid A structure on-the-fly while analyzing each MS n spectrum.…”

Section: On-the-fly Decoy Generationmentioning

confidence: 99%

Automated Lipid A Structure Assignment from Hierarchical Tandem Mass Spectrometry Data

Ting

Shaffer

Jones

et al. 2011

J. Am. Soc. Mass Spectrom.

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Infusion-based electrospray ionization (ESI) coupled to multiple-stage tandem mass spectrometry (MS n ) is a standard methodology for investigating lipid A structural diversity (Shaffer et al. J. Am. Soc. Mass. Spectrom. 18(6), [1080][1081][1082][1083][1084][1085][1086][1087][1088][1089][1090][1091][1092] 2007). Annotation of these MS n spectra, however, has remained a manual, expert-driven process. In order to keep up with the data acquisition rates of modern instruments, we devised a computational method to annotate lipid A MS n spectra rapidly and automatically, which we refer to as hierarchical tandem mass spectrometry (HiTMS) algorithm. As a first-pass tool, HiTMS aids expert interpretation of lipid A MS n data by providing the analyst with a set of candidate structures that may then be confirmed or rejected. HiTMS deciphers the signature ions (e.g., A-, Y-, and Z-type ions) and neutral losses of MS n spectra using a species-specific library based on general prior structural knowledge of the given lipid A species under investigation. Candidates are selected by calculating the correlation between theoretical and acquired MS n spectra. At a false discovery rate of less than 0.01, HiTMS correctly assigned 85% of the structures in a library of 133 manually annotated Francisella tularensis subspecies novicida lipid A structures. Additionally, HiTMS correctly assigned 85% of the structures in a smaller library of lipid A species from Yersinia pestis demonstrating that it may be used across species.

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“…Database search methods usually assign peptide sequences by comparing MS/MS spectra to theoretical peptide spectra predicted from a protein database, as exemplified in SEQUEST (9), Mascot (10), OMSSA (11), X!Tandem (12), Spectrum Mill (13), ProteinProspector (14), MyriMatch (15), Crux (16), MS-GFDB (17), Andromeda (18), BaMS 2 (19), and…”

mentioning

confidence: 99%

JUMP: A Tag-based Database Search Tool for Peptide Identification with High Sensitivity and Accuracy

Wang

et al. 2014

Molecular & Cellular Proteomics

129

124

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Database search programs are essential tools for identifying peptides via mass spectrometry (MS) in shotgun proteomics. Simultaneously achieving high sensitivity and high specificity during a database search is crucial for improving proteome coverage. Here we present JUMP, a new hybrid database search program that generates amino acid tags and ranks peptide spectrum matches (PSMs) by an integrated score from the tags and pattern matching. In a typical run of liquid chromatography coupled with high-resolution tandem MS, more than 95% of MS/MS spectra can generate at least one tag, whereas the remaining spectra are usually too poor to derive genuine PSMs. To enhance search sensitivity, the JUMP program enables the use of tags as short as one amino acid. Using a target-decoy strategy, we compared JUMP with other programs (e.g. SEQUEST, Mascot, PEAKS DB, and InsPecT) in the analysis of multiple datasets and found that JUMP outperformed these preexisting programs. JUMP also permitted the analysis of multiple co-fragmented peptides from "mixture spectra" to further increase PSMs. In addition, JUMP-derived tags allowed partial de novo sequencing and facilitated the unambiguous assignment of modified residues. In summary, JUMP is an effective database search algorithm complementary to current search programs. Molecular & Cellular Proteomics 13: 10.1074/mcp.O114.039586, 3663-3673, 2014.Peptide identification by tandem mass spectra is a critical step in mass spectrometry (MS)-based 1 proteomics (1). Numerous computational algorithms and software tools have been developed for this purpose (2-6). These algorithms can be classified into three categories: (i) pattern-based database search, (ii) de novo sequencing, and (iii) hybrid search that combines database search and de novo sequencing. With the continuous development of high-performance liquid chromatography and high-resolution mass spectrometers, it is now possible to analyze almost all protein components in mammalian cells (7). In contrast to rapid data collection, it remains a challenge to extract accurate information from the raw data to identify peptides with low false positive rates (specificity) and minimal false negatives (sensitivity) (8).Database search methods usually assign peptide sequences by comparing MS/MS spectra to theoretical peptide spectra predicted from a protein database, as exemplified in SEQUEST (9), Mascot (10), OMSSA (11), X!Tandem (12), Spectrum Mill (13), ProteinProspector (14), MyriMatch (15), Crux (16), MS-GFDB (17), Andromeda (18), BaMS 2 (19), and Morpheus (20). Some other programs, such as SpectraST (21) and Pepitome (22), utilize a spectral library composed of experimentally identified and validated MS/MS spectra. These methods use a variety of scoring algorithms to rank potential peptide spectrum matches (PSMs) and select the top hit as a putative PSM. However, not all PSMs are correctly assigned. For example, false peptides may be assigned to MS/MS spectra with numerous noisy peaks and poor fragmentation patterns. If the samples conta...

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Rapid and Accurate Peptide Identification from Tandem Mass Spectra

Cited by 179 publications

References 25 publications

Computing Exact p-values for a Cross-correlation Shotgun Proteomics Score Function

Computing Exact p-values for a Cross-correlation Shotgun Proteomics Score Function

Automated Lipid A Structure Assignment from Hierarchical Tandem Mass Spectrometry Data

JUMP: A Tag-based Database Search Tool for Peptide Identification with High Sensitivity and Accuracy

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