Automatic Quality Assessment of Peptide Tandem Mass Spectra

Bern, Marshall; Goldberg, David; McDonald, W. Hayes; Yates, John R.

doi:10.1093/bioinformatics/bth947

Cited by 186 publications

(198 citation statements)

References 18 publications

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“…Tandem mass spectra were extracted into ms1 and ms2 files (14) from raw files using RawExtract 1.9.9. Poor quality spectra were removed from the dataset using an automated spectral quality assessment algorithm (17). MS/MS spectra remaining after filtering were searched with the ProLuCID algorithm against the EBI-IPI_Mouse_01_01_04-19-2009 concatenated to a decoy data base in which the sequence for each entry in the original data base was reversed (18).…”

Section: Fractionation Of Brain Tissue and Affinity Purifications-mentioning

confidence: 99%

Proteomic Analysis of Wild-type and Mutant Huntingtin-associated Proteins in Mouse Brains Identifies Unique Interactions and Involvement in Protein Synthesis

Culver

Savas

Park

et al. 2012

Journal of Biological Chemistry

126

112

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Background: Differences in protein interactions between wild-type and mutant huntingtin are relevant to the disease. Results: Mutant huntingtin interacts with unique proteins and alters the subcellular context of some interactions shared with wild type. Conclusion: Mutant Huntington disease protein has loss-of-function and gain-of-function attributes. Significance: Results implicate understudied proteins and cellular/molecular processes that may contribute to the onset of the Huntington disease pathology.

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Section: Fractionation Of Brain Tissue and Affinity Purifications-mentioning

confidence: 99%

Proteomic Analysis of Wild-type and Mutant Huntingtin-associated Proteins in Mouse Brains Identifies Unique Interactions and Involvement in Protein Synthesis

Culver

Savas

Park

et al. 2012

Journal of Biological Chemistry

126

112

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“…Methods have been devised to sort the good from the bad spectra 34,37,38 . Recently, Bern et al presented two different algorithms for assessing spectral quality prior to a database search: a binary classifier, which predicts whether or not the search engine will be able to make an identification, and a statistical regression, which predicts a more universal quality metric, independent of the database search program 39 . A quadratic discriminant analysis, a classical machine learning algorithm, was trained on a data set of manually validated good and bad spectra.…”

Section: Reviewmentioning

confidence: 99%

“…To limit the number of spectra and ensure an enriched set of unique spectra, poor-quality spectra could be removed and duplicates identified and eliminated 39,60,61 . Spectra should be searched with at least two algorithms to take advantage of the different selectivities of algorithms (for example, SEQUEST and Mascot).…”

Section: Box 3 Strategy For Large Scale Data Analysismentioning

confidence: 99%

Large-scale database searching using tandem mass spectra: Looking up the answer in the back of the book

2004

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Database searching is an essential element of large-scale proteomics. Because these methods are widely used, it is important to understand the rationale of the algorithms. Most algorithms are based on concepts first developed in SEQUEST and PeptideSearch. Four basic approaches are used to determine a match between a spectrum and sequence: descriptive, interpretative, stochastic and probability-based matching. We review the basic concepts used by most search algorithms, the computational modeling of peptide identification and current challenges and limitations of this approach for protein identification.An unintended consequence of whole-genome sequencing has been the birth of large-scale proteomics. What drives proteomics is the ability to use mass spectrometry data of peptides as an 'address' or 'zip code' to locate proteins in sequence databases. Two mass spectrometry methods are used to identify proteins by database search methods. The first method uses a molecular weight fingerprint measured from a protein digested with a site-specific protease [1][2][3][4][5] . A second method uses tandem mass spectra derived from individual peptides of a digested protein 6,7 (Fig. 1). Because each tandem mass spectrum represents an independent and verifiable piece of data, this approach to database searching has the ability to identify proteins in mixtures, enabling a rapid and comprehensive approach for the analysis of protein complexes and other complicated mixtures of proteins 6,[8][9][10][11][12] . New biology has been discovered based on fast and accurate protein identification [13][14][15][16][17][18] . As tandem mass spectral protein identification has proliferated, it has become increasingly important to understand the rationale of individual database search algorithms, their relative strengths and weaknesses, and the mathematics used to match sequence to spectrum.In this review we discuss the prevailing fragmentation models, spectral preprocessing, methods to match tandem mass spectra to sequences and several approaches to matching tandem mass spectra of peptides whose exact sequences may not be present in the database. Space limitations restrict a detailed description of all algorithms in this rapidly expanding field. Also, some algorithms are proprietary, and thus, details on how they work are unknown. This review should supplement and update earlier reviews on database search algorithms [19][20][21][22][23][24] . Peptide fragmentation and data preprocessingIn tandem mass spectrometry (MS/MS), gas phase peptide ions undergo collision-induced dissociation (CID) with molecules of an inert gas such as helium or argon 25 . Other methods of dissociation have been developed, such as electron capture dissociation (ECD), surface induced dissociation (SID) and electron transfer dissociation (ETD), but gas-phase CID is the most widely used in commercial tandem mass spectrometers. The dissociation pathways are strongly dependent on the collision energy, but the vast majority of instruments use low-energy CID (<100 eV) 26 ....

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“…Large MS/MS datasets contain many low-quality spectra that cannot result in reliable peptide identifications [28,29]. Typically, when a whole MS/MS dataset is searched, only a small fraction of the spectra (less than 20%) get identified.…”

Section: Filtering Ms/ms Datasetsmentioning

confidence: 99%

Clustering Millions of Tandem Mass Spectra

et al. 2007

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SummaryTandem mass spectrometry (MS/MS) experiments often generate redundant datasets containing multiple spectra of the same peptides. Clustering of MS/MS spectra takes advantage of this redundancy by identifying multiple spectra of the same peptide and replacing them with a single representative spectrum. Analyzing only representative spectra results in significant speed-up of MS/ MS database searches. We present an efficient clustering approach for analyzing large MS/MS datasets (over ten million spectra) with a capability to reduce the number of spectra submitted to further analysis by an order of magnitude. The MS/MS database search of clustered spectra results in fewer spurious hits to the database and increases number of peptide identifications as compared to regular non-clustered searches. Our open source software MS-Clustering is available for download at

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Automatic Quality Assessment of Peptide Tandem Mass Spectra

Cited by 186 publications

References 18 publications

Proteomic Analysis of Wild-type and Mutant Huntingtin-associated Proteins in Mouse Brains Identifies Unique Interactions and Involvement in Protein Synthesis

Proteomic Analysis of Wild-type and Mutant Huntingtin-associated Proteins in Mouse Brains Identifies Unique Interactions and Involvement in Protein Synthesis

Large-scale database searching using tandem mass spectra: Looking up the answer in the back of the book

Clustering Millions of Tandem Mass Spectra

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