Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Spectral dictionaries: Integrating de novo peptide sequencing with database search of tandem mass spectra.

ABSTRACT: Database search tools identify peptides by matching tandem mass spectra against a protein database. We study an alternative approach when all plausible de novo interpretations of a spectrum (spectral dictionary) are generated and then quickly matched against the database. We present a new MS-Dictionary algorithm for efficiently generating spectral dictionaries and demonstrate that MS-Dictionary can identify spectra that are missed in the database search. We argue that MS-Dictionary enables proteogenomics searches in six-frame translation of genomic sequences that may be prohibitively time-consuming for existing database search approaches. We show that such searches allow one to correct sequencing errors and find programmed frameshifts.

SUBMITTER: Kim S

PROVIDER: S-EPMC2621003 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Spectral dictionaries: Integrating de novo peptide sequencing with database search of tandem mass spectra.

Kim Sangtae S Gupta Nitin N Bandeira Nuno N Pevzner Pavel A PA

Molecular & cellular proteomics : MCP 20080814 1

Database search tools identify peptides by matching tandem mass spectra against a protein database. We study an alternative approach when all plausible de novo interpretations of a spectrum (spectral dictionary) are generated and then quickly matched against the database. We present a new MS-Dictionary algorithm for efficiently generating spectral dictionaries and demonstrate that MS-Dictionary can identify spectra that are missed in the database search. We argue that MS-Dictionary enables prote ...[more]

PMID: 18703573

Similar Datasets

Peptide de novo sequencing of mixture tandem mass spectra.

Project description:The impact of mixture spectra deconvolution on the performance of four popular de novo sequencing programs was tested using artificially constructed mixture spectra as well as experimental proteomics data. Mixture fragmentation spectra are recognized as a limitation in proteomics because they decrease the identification performance using database search engines. De novo sequencing approaches are expected to be even more sensitive to the reduction in mass spectrum quality resulting from peptide precursor co-isolation and thus prone to false identifications. The deconvolution approach matched complementary b-, y-ions to each precursor peptide mass, which allowed the creation of virtual spectra containing sequence specific fragment ions of each co-isolated peptide. Deconvolution processing resulted in equally efficient identification rates but increased the absolute number of correctly sequenced peptides. The improvement was in the range of 20-35% additional peptide identifications for a HeLa lysate sample. Some correct sequences were identified only using unprocessed spectra; however, the number of these was lower than those where improvement was obtained by mass spectral deconvolution. Tight candidate peptide score distribution and high sensitivity to small changes in the mass spectrum introduced by the employed deconvolution method could explain some of the missing peptide identifications.

| S-EPMC5297990 | biostudies-literature

Lessons in de novo peptide sequencing by tandem mass spectrometry.

Project description:Mass spectrometry has become the method of choice for the qualitative and quantitative characterization of protein mixtures isolated from all kinds of living organisms. The raw data in these studies are MS/MS spectra, usually of peptides produced by proteolytic digestion of a protein. These spectra are "translated" into peptide sequences, normally with the help of various search engines. Data acquisition and interpretation have both been automated, and most researchers look only at the summary of the identifications without ever viewing the underlying raw data used for assignments. Automated analysis of data is essential due to the volume produced. However, being familiar with the finer intricacies of peptide fragmentation processes, and experiencing the difficulties of manual data interpretation allow a researcher to be able to more critically evaluate key results, particularly because there are many known rules of peptide fragmentation that are not incorporated into search engine scoring. Since the most commonly used MS/MS activation method is collision-induced dissociation (CID), in this article we present a brief review of the history of peptide CID analysis. Next, we provide a detailed tutorial on how to determine peptide sequences from CID data. Although the focus of the tutorial is de novo sequencing, the lessons learned and resources supplied are useful for data interpretation in general.

| S-EPMC4367481 | biostudies-literature

A hierarchical MS2/MS3 database search algorithm for automated analysis of phosphopeptide tandem mass spectra.

Project description:A novel hierarchical MS(2)/MS(3) database search algorithm has been developed to analyze MS(2)/MS(3) phosphopeptides proteomic data. The algorithm is incorporated in an automated database search program, MassMatrix. The algorithm matches experimental MS(2) spectra against a supplied protein database to determine candidate peptide matches. It then matches the corresponding experimental MS(3) spectra against those candidate peptide matches. The MS(2) and MS(3) spectra are used in concert to arrive at peptide matches with overall higher confidence rather than combining MS(2) and MS(3) data searched separately. Receiver operating characteristic analysis showed that hierarchical MS(2)/MS(3) database searches with MassMatrix had better sensitivity and specificity than the two-stage MS(2)/MS(3) database searches obtained with MassMatrix, MASCOT, and X!Tandem. A greater number of true peptide matches at a given false rate were identified by use of this new algorithm for data collected on both LCQ and LTQ-FTICR mass spectrometers. The additional MS(3) spectral data also improved the overall reliability and the number of true positives (TPs) due to the fact that the TPs of the MS(2)/MS(3) search results had higher scores than those of the MS(2).

| S-EPMC2775432 | biostudies-literature

Deconvolution and database search of complex tandem mass spectra of intact proteins: a combinatorial approach.

Project description:Top-down proteomics studies intact proteins, enabling new opportunities for analyzing post-translational modifications. Because tandem mass spectra of intact proteins are very complex, spectral deconvolution (grouping peaks into isotopomer envelopes) is a key initial stage for their interpretation. In such spectra, isotopomer envelopes of different protein fragments span overlapping regions on the m/z axis and even share spectral peaks. This raises both pattern recognition and combinatorial challenges for spectral deconvolution. We present MS-Deconv, a combinatorial algorithm for spectral deconvolution. The algorithm first generates a large set of candidate isotopomer envelopes for a spectrum, then represents the spectrum as a graph, and finally selects its highest scoring subset of envelopes as a heaviest path in the graph. In contrast with other approaches, the algorithm scores sets of envelopes rather than individual envelopes. We demonstrate that MS-Deconv improves on Thrash and Xtract in the number of correctly recovered monoisotopic masses and speed. We applied MS-Deconv to a large set of top-down spectra from Yersinia rohdei (with a still unsequenced genome) and further matched them against the protein database of related and sequenced bacterium Yersinia enterocolitica. MS-Deconv is available at http://proteomics.ucsd.edu/Software.html.

| S-EPMC3101958 | biostudies-literature

Novel peptide identification from tandem mass spectra using ESTs and sequence database compression.

Project description:Peptide identification by tandem mass spectrometry is the dominant proteomics workflow for protein characterization in complex samples. Traditional search engines, which match peptide sequences with tandem mass spectra to identify the samples' proteins, use protein sequence databases to suggest peptide candidates for consideration. Although the acquisition of tandem mass spectra is not biased toward well-understood protein isoforms, this computational strategy is failing to identify peptides from alternative splicing and coding SNP protein isoforms despite the acquisition of good-quality tandem mass spectra. We propose, instead, that expressed sequence tags (ESTs) be searched. Ordinarily, such a strategy would be computationally infeasible due to the size of EST sequence databases; however, we show that a sophisticated sequence database compression strategy, applied to human ESTs, reduces the sequence database size approximately 35-fold. Once compressed, our EST sequence database is comparable in size to other commonly used protein sequence databases, making routine EST searching feasible. We demonstrate that our EST sequence database enables the discovery of novel peptides in a variety of public data sets.

| S-EPMC1865584 | biostudies-literature

GlycoDeNovo - an Efficient Algorithm for Accurate de novo Glycan Topology Reconstruction from Tandem Mass Spectra.

Project description:A major challenge in glycomics is the characterization of complex glycan structures that are essential for understanding their diverse roles in many biological processes. We present a novel efficient computational approach, named GlycoDeNovo, for accurate elucidation of the glycan topologies from their tandem mass spectra. Given a spectrum, GlycoDeNovo first builds an interpretation-graph specifying how to interpret each peak using preceding interpreted peaks. It then reconstructs the topologies of peaks that contribute to interpreting the precursor ion. We theoretically prove that GlycoDeNovo is highly efficient. A major innovative feature added to GlycoDeNovo is a data-driven IonClassifier which can be used to effectively rank candidate topologies. IonClassifier is automatically learned from experimental spectra of known glycans to distinguish B- and C-type ions from all other ion types. Our results showed that GlycoDeNovo is robust and accurate for topology reconstruction of glycans from their tandem mass spectra. Graphical Abstract ?.

| S-EPMC5647224 | biostudies-literature

DeNovoGUI: an open source graphical user interface for de novo sequencing of tandem mass spectra.

Project description:De novo sequencing is a popular technique in proteomics for identifying peptides from tandem mass spectra without having to rely on a protein sequence database. Despite the strong potential of de novo sequencing algorithms, their adoption threshold remains quite high. We here present a user-friendly and lightweight graphical user interface called DeNovoGUI for running parallelized versions of the freely available de novo sequencing software PepNovo+, greatly simplifying the use of de novo sequencing in proteomics. Our platform-independent software is freely available under the permissible Apache2 open source license. Source code, binaries, and additional documentation are available at http://denovogui.googlecode.com .

| S-EPMC3923451 | biostudies-literature

MSblender: A probabilistic approach for integrating peptide identifications from multiple database search engines.

Project description:Shotgun proteomics using mass spectrometry is a powerful method for protein identification but suffers limited sensitivity in complex samples. Integrating peptide identifications from multiple database search engines is a promising strategy to increase the number of peptide identifications and reduce the volume of unassigned tandem mass spectra. Existing methods pool statistical significance scores such as p-values or posterior probabilities of peptide-spectrum matches (PSMs) from multiple search engines after high scoring peptides have been assigned to spectra, but these methods lack reliable control of identification error rates as data are integrated from different search engines. We developed a statistically coherent method for integrative analysis, termed MSblender. MSblender converts raw search scores from search engines into a probability score for every possible PSM and properly accounts for the correlation between search scores. The method reliably estimates false discovery rates and identifies more PSMs than any single search engine at the same false discovery rate. Increased identifications increment spectral counts for most proteins and allow quantification of proteins that would not have been quantified by individual search engines. We also demonstrate that enhanced quantification contributes to improve sensitivity in differential expression analyses.

| S-EPMC3128686 | biostudies-literature

De novo correction of mass measurement error in low resolution tandem MS spectra for shotgun proteomics.

Project description:We report an algorithm designed for the calibration of low resolution peptide mass spectra. Our algorithm is implemented in a program called FineTune, which corrects systematic mass measurement error in 1 min, with no input required besides the mass spectra themselves. The mass measurement accuracy for a set of spectra collected on an LTQ-Velos improved 20-fold from -0.1776?±?0.0010?m/z to 0.0078?±?0.0006?m/z after calibration (avg?±?95 % confidence interval). The precision in mass measurement was improved due to the correction of non-linear variation in mass measurement accuracy across the m/z range.

| S-EPMC3515694 | biostudies-literature

Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics.

Project description:Here we present GlycanFinder, a database search and de novo sequencing tool for the analysis of intact glycopeptides from mass spectrometry data. GlycanFinder integrates peptide-based and glycan-based search strategies to address the challenge of complex fragmentation of glycopeptides. A deep learning model is designed to capture glycan tree structures and their fragment ions for de novo sequencing of glycans that do not exist in the database. We performed extensive analyses to validate the false discovery rates (FDRs) at both peptide and glycan levels and to evaluate GlycanFinder based on comprehensive benchmarks from previous community-based studies. Our results show that GlycanFinder achieved comparable performance to other leading glycoproteomics softwares in terms of both FDR control and the number of identifications. Moreover, GlycanFinder was also able to identify glycopeptides not found in existing databases. Finally, we conducted a mass spectrometry experiment for antibody N-linked glycosylation profiling that could distinguish isomeric peptides and glycans in four immunoglobulin G subclasses, which had been a challenging problem to previous studies.

| S-EPMC10329677 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data