Stabilisation of eudesmane cation by tryptophan 334 during aristolochene synthase catalysisElectronic Supplementary Information (ESI) available: GC profiles of co-injections of germacrene A from ASW334F and ASW334V with authentic germacrene A; mass spectra of germacrene A produced by ASW334V and of an authentic sample; mass spectra of valencene produced by ASW334F and of an authentic sample. See http://www.rsc.org/suppdata/cc/b3/b306867f/

2003 ◽  
pp. 2162 ◽  
Author(s):  
Athina Deligeorgopoulou ◽  
Susan E. Taylor ◽  
Silvia Forcat ◽  
Rudolf K. Allemann
Keyword(s):  
Mass Spectra ◽  
Authentic Sample ◽  
Supplementary Information ◽  
Sample Mass ◽  
Aristolochene Synthase

CluMSID: an R package for similarity-based clustering of tandem mass spectra to aid feature annotation in metabolomics

2019 ◽  
Vol 35 (17) ◽  
pp. 3196-3198 ◽  
Author(s):  
Tobias Depke ◽  
Raimo Franke ◽  
Mark Brönstrup
Keyword(s):  
Mass Spectra ◽  
Neutral Loss ◽  
Metabolite Identification ◽  
R Package ◽  
Supplementary Information ◽  
Tandem Mass ◽  
Compound Identification ◽  
Feature Identification ◽  
Tandem Mass Spectra ◽  
Interactive Visualizations

Abstract Summary Compound identification is one of the most eminent challenges in the untargeted analysis of complex mixtures of small molecules by mass spectrometry. Similarity of tandem mass spectra can provide valuable information on putative structural similarities between known and unknown analytes and hence aids feature identification in the bioanalytical sciences. We have developed CluMSID (Clustering of MS2 spectra for metabolite identification), an R package that enables researchers to make use of tandem mass spectra and neutral loss pattern similarities as a part of their metabolite annotation workflow. CluMSID offers functions for all analysis steps from import of raw data to data mining by unsupervised multivariate methods along with respective (interactive) visualizations. A detailed tutorial with example data is provided as supplementary information. Availability and implementation CluMSID is available as R package from https://github.com/tdepke/CluMSID/and from https://bioconductor.org/packages/CluMSID/. Supplementary information Supplementary data are available at Bioinformatics online.

Top-Down Garbage Collector: a tool for selecting high-quality top-down proteomics mass spectra

2019 ◽  
Vol 35 (18) ◽  
pp. 3489-3490 ◽  
Author(s):  
Diogo B Lima ◽  
André R F Silva ◽  
Mathieu Dupré ◽  
Marlon D M Santos ◽  
Milan A Clasen ◽  
...  
Keyword(s):  
Quality Control ◽  
Mass Spectra ◽  
Rate Increase ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Top Down ◽  
High Quality ◽  
Garbage Collector ◽  
E Coli ◽  
Spectral Libraries

Abstract Motivation We present the first tool for unbiased quality control of top-down proteomics datasets. Our tool can select high-quality top-down proteomics spectra, serve as a gateway for building top-down spectral libraries and, ultimately, improve identification rates. Results We demonstrate that a twofold rate increase for two E. coli top-down proteomics datasets may be achievable. Availability and implementation http://patternlabforproteomics.org/tdgc, freely available for academic use. Supplementary information Supplementary data are available at Bioinformatics online.

The software for interactive evaluation of mass spectra stability and reproducibility

2020 ◽  
Author(s):  
E S Zhvansky ◽  
A A Sorokin ◽  
D S Bormotov ◽  
K V Bocharov ◽  
D S Zavorotnyuk ◽  
...  
Keyword(s):  
Outlier Detection ◽  
Mass Spectra ◽  
Variance Estimation ◽  
Ion Sources ◽  
Supplementary Information ◽  
Operator Experience ◽  
Machine Learning Classification ◽  
Other Information ◽  
The Cost

Abstract Summary Mass spectrometry (MS) methods are widely used for the analysis of biological and medical samples. Recently developed methods, such as DESI, REIMS and NESI allow fast analyses without sample preparation at the cost of higher variability of spectra. In biology and medicine, MS profiles are often used with machine learning (classification, regression, etc.) algorithms and statistical analysis, which are sensitive to outliers and intraclass variability. Here, we present spectra similarity matrix (SSM) Display software, a tool for fast visual outlier detection and variance estimation in mass spectrometric profiles. The tool speeds up the process of manual spectra inspection, improves accuracy and explainability of outlier detection, and decreases the requirements to the operator experience. It was shown that the batch effect could be revealed through SSM analysis and that the SSM calculation can also be used for tuning novel ion sources concerning the quality of obtained mass spectra. Availability and implementation Source code, example datasets, binaries and other information are available at https://github.com/EvgenyZhvansky/R_matrix. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DrawGlycan-SNFG and gpAnnotate: rendering glycans and annotating glycopeptide mass spectra

2019 ◽  
Author(s):  
Kai Cheng ◽  
Gabrielle Pawlowski ◽  
Xinheng Yu ◽  
Yusen Zhou ◽  
Sriram Neelamegham
Keyword(s):  
Mass Spectrometry ◽  
Open Source ◽  
Mass Spectra ◽  
Supplementary Information ◽  
Supplementary Data ◽  
International Union ◽  
Open Source Program ◽  
Source Program ◽  
Wide Range ◽  
Peptide Modifications

Abstract Summary This manuscript describes an open-source program, DrawGlycan-SNFG (version 2), that accepts IUPAC (International Union of Pure and Applied Chemist)-condensed inputs to render Symbol Nomenclature For Glycans (SNFG) drawings. A wide range of local and global options enable display of various glycan/peptide modifications including bond breakages, adducts, repeat structures, ambiguous identifications etc. These facilities make DrawGlycan-SNFG ideal for integration into various glycoinformatics software, including glycomics and glycoproteomics mass spectrometry (MS) applications. As a demonstration of such usage, we incorporated DrawGlycan-SNFG into gpAnnotate, a standalone application to score and annotate individual MS/MS glycopeptide spectrum in different fragmentation modes. Availability and implementation DrawGlycan-SNFG and gpAnnotate are platform independent. While originally coded using MATLAB, compiled packages are also provided to enable DrawGlycan-SNFG implementation in Python and Java. All programs are available from https://virtualglycome.org/drawglycan; https://virtualglycome.org/gpAnnotate. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Understanding the limit of open search in the identification of peptides with post-translational modifications — A simulation-based study

2018 ◽  
Author(s):  
Jiaan Dai ◽  
Fengchao Yu ◽  
Ning Li ◽  
Weichuan Yu
Keyword(s):  
Analytical Model ◽  
Mass Spectra ◽  
Mass Spectrometry Data ◽  
Supplementary Information ◽  
Necessary Condition ◽  
Tandem Mass ◽  
Search Methods ◽  
Post Translational Modifications ◽  
Tandem Mass Spectra ◽  
The Relationship

AbstractMotivationAnalyzing tandem mass spectrometry data to recognize peptides in a sample is the fundamental task in computational proteomics. Traditional peptide identification algorithms perform well when identifying unmodified peptides. However, when peptides have post-translational modifications (PTMs), these methods cannot provide satisfactory results. Recently, Chick et al., 2015 and Yu et al., 2016 proposed the spectrum-based and tag-based open search methods, respectively, to identify peptides with PTMs. While the performance of these two methods is promising, the identification results vary greatly with respect to the quality of tandem mass spectra and the number of PTMs in peptides. This motivates us to systematically study the relationship between the performance of open search methods and quality parameters of tandem mass spectrum data, as well as the number of PTMs in peptides.ResultsThrough large-scale simulations, we obtain the performance trend when simulated tandem mass spectra are of different quality. We propose an analytical model to describe the relationship between the probability of obtaining correct identifications and the spectrum quality as well as the number of PTMs. Based on the analytical model, we can quantitatively describe the necessary condition to effectively apply open search methods.AvailabilitySource codes of the simulation are available at http://bioinformatics.ust.hk/[email protected] or [email protected] informationSupplementary data are available at Bioinformatics online.

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