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39 results for “tandem mass spectrometry”
Liquid Chromatography - Tandem Mass Spectrometry (LC-MS/MS) and Gas Chromatography - Mass Spectrometry (GC-MS) Reference Libraries from Global Natural Products Social Molecular Networking (GNPS) and National Institute of Standards and Technology (NIST) WebBook Processed for Spectral Library Matching
<div>In order to obtain a high-quality LC-MS/MS reference database for spectral library matching, we selected 22 high-quality GNPS tandem mass spectrometry databases generated under the positive ion mode. Further preprocessing similar to Huber et al involving mass-to-charge (m/z) and intensity filtering yields the database found in the file LCMS_GNPS_reference_library.csv which contains 14,705 electrospray ionization (ESI) mass spectra, each of which corresponds to a unique compound. The NIST WebBook database was used to construct GC-MS database contained in the file GCMS_NIST_WebBook.csv. This database contains 23,721 electron ionization (EI) mass spectra, each of which corresponds to a unique non-hyphenated Chemical Abstract Service (CAS) Registry Number.</div> <div> </div> <div>Both LC-MS/MS and GC-MS databases are organized into three columns: one for the identifier, one for the m/z values, and one for the intensity values. For example, if spectrum A has 20 ion fragments, then there will be 20 rows corresponding to spectrum A in the corresponding database with the identifier A repeated 20 times with the corresponding m/z and intensity values.</div>
Predicting glycan structure from tandem mass spectrometry via deep learning
<p>Curated set of LC-MS/MS data from glycomics studies. Used for training and applying CandyCrunch, a deep learning model to predict glycan structure from LC-MS/MS data, described in Urban et al., Nat Methods, 2024 and https://github.com/BojarLab/CandyCrunch.</p> <p>Files:</p> <p>full_dataset.xlsx: Full dataset with all annotated LC-MS/MS glycan spectra</p> <p>X_train.pkl: spectra and metadata from our training set</p> <p>y_train.pkl: labels from our training set</p> <p>X_test.pkl: spectra and metadata from our independent test set</p> <p>y_test.pkl: labels from our independent test set</p> <p>glycans.pkl: glycans in IUPAC-condensed nomenclature in the same order as the label-encoding</p>
Supporting files for Turečková et al. 2024 "A New Abscisic Acid Conjugate, ABA‑L‑Glutamate, Determined in Different Plant Species by Combined Immunoaffinity Chromatography‑Tandem Mass Spectrometry"
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Dataset: "Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data"
<p>Dataset used in the experiments of the publication: "Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data" by Bach et al.</p> <p><strong>File description:</strong></p> <ul> <li> <p>cfmid4.tar: MS² spectra simulated using <a href="https://bitbucket.org/wishartlab/cfm-id-code/src/CFM-ID_4.0.7/">CFM-ID (v4.0.7)</a> for all molecular candidate structures</p> </li> <li> <p>db_layout.png: Visualization of the SQLite database (DB) layout</p> </li> <li> <p>massbank.sqlite.gz: DB containing all needed data to (re-)run the experiments shown in the paper. Please read "DB_README.md" for further details. The database file can be unpacked using gzip.</p> </li> <li> <p>metfrag.tar: MetFrag input files and MS² scores for all candidate sets computed using the <a href="https://ipb-halle.github.io/MetFrag/projects/metfragcl/">MetFrag software</a>.</p> </li> <li> <p>sirius_scores.tar: MS² scores for all candidates and measured spectra using the <a href="https://bio.informatik.uni-jena.de/software/sirius/">SIRIUS software</a>.</p> </li> <li> <p>sirius_inputs.tar: Input (ms-files) for the SIRIUS software.</p> </li> <li> <p>DB_README.md: Description of each table in the "massbank.sqlite" SQLite DB.</p> </li> <li> <p>db_processing_scripts.tar: Scripts to re-produce the "massbank.sqlite" and a README.md providing further information on the process.</p> </li> <li> <p>massbank__2020.11__v0.6.1.sqlite: Base SQLite DB from which the "massbank.sqlite" was build up. It was created using the "<a href="https://github.com/bachi55/massbank2db">massbank2db</a>" (v0.6.1) Python package using the <a href="https://github.com/bachi55/MassBank-data/tree/2020.11-branch">MassBank release 2020.11</a>.</p> </li> <li> <p>substructure_fingerprints.tar: Pre-computed substructure counting fingerprints for all candidates related to our experiments.</p> </li> </ul> <p><strong>Instructions:</strong></p> <p>The "massbank.sqlite" can be directly used with the Structure Support Vector Machine Model (SSVM) described in the manuscript and implemented in the "<a href="https://github.com/aalto-ics-kepaco/msms_rt_ssvm">ssvm</a>" Python package.</p> <p>If desired, the database can be re-produced using the scripts provided in "db_processing_scripts.tar":</p> <ol> <li>Create a directory for all data</li> <li>Download and extract the ... <ol> <li>Processing scripts</li> <li>MS² scorer outputs (e.g. metfrag.tar)</li> <li>Pre-computed substructure fingerprints</li> </ol> </li> <li>Follow the instructions given in the "README.md" of the "db_processing_scripts.tar"</li> </ol>
Figure 5 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 5. KEGG pathways of the differentially expressed phosphorylated proteins. The abscissa indicates the first 10 significantly enriched KEGG pathways and the ordinate indicates the significance of enriched KEGG pathways, the more left, the more significant.
Figure 4 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 4. Gene ontology annotations of the differentially expressed phosphorylated proteins. The abscissa indicates the enriched GO functional classification, including biological process (A), cellular component (B), and molecular function (C). The ordinate indicates the size of the significance of corresponding to each entry, the more left, the more significant.
Figure 3 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 3. Clustering heatmap of different expression phosphorylated peptides. Each row represents a phosphorylated peptide segment, each column represents a group of samples. The logarithmic value (logarithmic transformation based on 2) of the significantly differentially expressed phosphorylated peptides in different samples is displayed in the clustering heatmap in different colors. Red represents significant upregulation of phosphorylated peptides; blue represents significant down-regulation of phosphorylated peptides.
Figure 2 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 2. Volcano plots from different group comparisons. The abscissa indicates difference multiple (logarithmic transformation based on 2), the ordinate indicates the significant of difference (logarithmic transformation based on 10). The red point is significantly upregulated phosphorylated peptide segment, the blue point is significantly downregulated phosphorylated peptide segment and the gray point is a phosphorylated peptide segment with no significant difference.
Quantification of phosphorylated metabolites, organic acids, and intermediates of the TCA cycle using capillary ion chromatography tandem mass spectrometry (capIC-MS/MS) following treatment of Escherichia coli with ciprofloxacin
<p>Capillary ion chromatography tandem mass spectrometry (capIC-MS/MS) was used to quantify phosphorylated metabolites, organic acids, and intermediates of the TCA cycle of Escherichia coli treated with ciprofloxacin, BTP-001 (a novel antimicrobial peptide), and a combination of the two . Metabolite extracts were analyzed with a Xevo TQ-XS triple quadrupole mass spectrometer (Waters, USA).</p><p>Samples were gathered from E. coli cultures grown in batch cultivations using 1 liter bioreactors. Briefly, intracellular metabolites were extracted by cycling samples between −20 °C EtOH and N2 (<i>l</i>) in three consecutive freeze–thaw cycles, with vortexing every 10 min during the thawing phase. Filters were removed and the cell debris was pelleted (4500 rcf, 10 min, -9 °C). The supernatants were transferred to a new tube, snap frozen in N2 (<i>l</i>), and lyophilized. Lyophilized extracts were reconstituted in 500 µL cold Milli-Q H2O and cleared by spin-filtration with a 10 kDa molecular cutoff (20817 rcf, 10 min, 0 °C). A mix of 80 µL centrifuged sample and 20 µL 13C-labeled ISTD extract from yeast was sent to analysis. </p><p>Data processing and absolute quantification was performed as earlier described using the TargetLynx application manager of MassLynx v 4.1 (Waters) to interpolate calibration curves made with appropriate dilutions of analytical grade standards (Sigma-Aldrich). The response factor of the corresponding U13C-isotopologues were used to correct the standard and sample extract response factors. Extract concentrations were normalized to the CDW, which was calculated from interpolation of the OD600 vs. CDW (g/L) curve. </p><p>Further statistical analysis in MetaboAnalyst v 5.0 replaced missing values with 1/5 of the minimum value of the respective metabolite. An unpaired T-test with unequal variance determined differential enriched metabolites with a false discovery rate (FDR) < 0.05 which are presented as log2 fold-change compared to control. </p>
Tandem Mass Spectrometry Data (LCMS-2) from Microalgal Co-culture of Skeletonema marinoi and Prymnesium parvum
<p>The mzML files in this dataset are the Liquid Chromatography Tandem Mass Spectrometry (LCMS-2) data files, derived from the RAW MS-2 files using GNPS file convertor. These files contain unprocessed features fragmented features from the MS-1 data files available as <10.5281/zenodo.10143127> acquired from the monocultures (single species: <em>Skeletonema marinoi</em> and <em>Prymnesium parvum</em> separately) and co-culture conditions of (<em>Skeletonema marinoi</em> and <em>Prymnesium parvum</em>). These files are used for structure annotations. </p> <p>The results of metabolomics annotation are available on Zenodo with DOI: 10.5281/zenodo.10143554</p>
Tandem Mass Spectrometry Dataset for Machine Learning in Metabolomics
<p>This dataset contains tandem mass spectrometry data cleaned and processed from the publicly available GNPS Spectral Library. We aim to continuously update this dataset with new data points as the spectral libraries expand.</p>
Result files (ONLYSTEREO): "Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data"
<p>Result files associated with the publication: "<strong>Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data</strong>" by Bach et al.</p> <p>The following files are included in the archive:</p> <ul> <li>Raw max-marginal predictions using LC-MS²Struct for all LC-MS² experiments of the ONLYSTEREO setup</li> <li>Averaged max-marginals for the LC-MS²Struct over all SSVM models</li> <li>Ranks for the ground-truth structures predicted by Only MS² and LC-MS²Struct (molecule class analysis)</li> </ul> <p>Instructions:</p> <ul> <li>clone the repository containing the experimental scripts and analysis notebooks: <a href="https://github.com/aalto-ics-kepaco/lcms2struct_exp">https://github.com/aalto-ics-kepaco/lcms2struct_exp</a></li> <li>download the archive in this repository</li> <li>unpack the archive in the git-repository root directory</li> <li>follow the instructions given in the <a href="https://github.com/aalto-ics-kepaco/lcms2struct_exp/blob/main/README.md">README.md</a> of the git-repository to reproduce the figures, etc.</li> </ul>
Result files (ALLDATA): "Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data with LC-MS²Struct"
<p>Result files associated with the publication: "<strong>Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data</strong>" by Bach et al.</p> <p>The following files are included in the archive:</p> <ul> <li>Raw max-marginal predictions using LC-MS²Struct for all LC-MS² experiments of the ALLDATA setup</li> <li>Averaged max-marginals for the LC-MS²Struct over all SSVM models</li> <li>Top-k accuracies for the comparison methods (MS²+RT, ...)</li> <li>Ranks for the ground-truth structures predicted by Only MS² and LC-MS²Struct (molecule class analysis)</li> </ul> <p>Instructions:</p> <ul> <li>clone the repository containing the experimental scripts and analysis notebooks: <a href="https://github.com/aalto-ics-kepaco/lcms2struct_exp">https://github.com/aalto-ics-kepaco/lcms2struct_exp</a></li> <li>download the archive in this repository</li> <li>unpack the archive in the git-repository root directory</li> <li>follow the instructions given in the <a href="https://github.com/aalto-ics-kepaco/lcms2struct_exp/blob/main/README.md">README.md</a> of the git-repository to reproduce the figures, etc.</li> </ul> <p>Version history:</p> <ul> <li><strong>Version 1</strong>: Experimental results for "Method comparison" and "Molecule classification analysis" where performed with <strong>2D fingerprints</strong> (<a href="https://www.biorxiv.org/content/10.1101/2022.02.11.480137v1">preprint v1</a>)</li> <li><strong>Version 2</strong> <em>(this version)</em>: Experimental results for "Method comparison" and "Molecule classification analysis" where performed with <strong>3D fingerprints</strong></li> </ul>
Proteomic Profiling for Identification of Animal Skin Species in Ancient Egyptian Archaeological Leather using Liquid Chromatography Coupled with Tandem Mass Spectrometry (Nano LC-MS/MS)
<p><strong>Proteomic Profiling Dataset for Identification of Animal Skin Species in Ancient Egyptian Archaeological Leather using Liquid Chromatography Coupled with Tandem Mass Spectrometry (Nano LC-MS/MS)</strong></p>
Tandem Mass Spectrometry Dataset for Machine Learning in Metabolomics
<p>This dataset contains tandem mass spectrometry data cleaned and processed from the publicly available GNPS Spectral Library. We aim to continuously update this dataset with new data points as the spectral libraries expand.</p>
Figure 1 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 1. Proportion of serine, threonine, and tyrosine in phosphorylation sites.
Raw data for the submitted manuscript entitled "Novel strategies for the determination of plastic additives derived from agricultural plastics in soil using ultrahigh-performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS)"
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Validation data Set: Development and validation of a quantitative method for 15 antiviral drugs in poultry muscle using liquid chromatography coupled to tandem mass spectrometry
<p>Validation dataset for paper published in the Journal of Chromatography A.</p> <p> </p> <p>Clément Douillet, Mary Moloney, Melissa Di Rocco, Christopher Elliott, Martin Danaher,<br> Development and validation of a quantitative method for 15 antiviral drugs in poultry muscle using liquid chromatography coupled to tandem mass spectrometry, Journal of Chromatography A, Volume 1665, 2022, 462793, ISSN 0021-9673,</p> <p><br> Abstract:</p> <p>The objective of this work was to develop a quantitative multi-residue method for analysing antiviral drug residues and their metabolites in poultry meat samples. Antiviral drugs are not licensed for the treatment of influenza in food producing animals. However, there have been some reports indicating their illegal use in poultry. In this study, a method was developed for the analysis of 15 antiviral drug residues in poultry muscle (chicken, duck, quail and turkey) using liquid chromatography coupled to tandem mass spectrometry. This included 13 drugs against influenza and associated metabolites, but also two drugs employed for the treatment of herpes (acyclovir and ganciclovir). The method required the development of a novel chromatographic separation using a hydrophilic interaction chromatographic (HILIC) BEH amide column, which was necessary to retain the highly polar compounds. The analytes were detected using a triple quadrupole mass spectrometer operating in positive electrospray ionization mode. A range of different sample preparation protocols suitable for polar compounds were evaluated. The most effective procedure was based on a simple acetonitrile-based protein precipitation step followed by a further dilution in a methanol/water solution. The confirmatory method was validated according to the EU 2021/808 guidelines on different species including chicken, duck, turkey and quail. The validation was performed using various calibration curves ranging from 0.1 µg kg−1to 200 µg kg−1, according to the analyte. Depending on the analyte sensitivity, decision limits achieved ranged from 0.12 µg kg−1 for arbidol to 34.7 µg kg−1 for ribavirin. Overall, the reproducibility precision values ranged from 2.8% to 22.7% and the recoveries from 84% to 127%. The method was applied to 120 commercial poultry samples from the Irish market, which were all found to be residue-free.<br> Keywords: Antiviral drug residues; Influenza; HILIC; LC-MS/MS; Poultry muscle</p>
Proteomic Profiling for Identification of Animal Skin Species in Ancient Egyptian Archaeological Leather using Liquid Chromatography Coupled with Tandem Mass Spectrometry (Nano LC-MS/MS)
<p><strong>Proteomic Profiling for Identification of Animal Skin Species in Ancient Egyptian Archaeological Leather using Liquid Chromatography Coupled with Tandem Mass Spectrometry (Nano LC-MS/MS)</strong></p>
Liquid chromatography tandem mass spectrometry of AMPA receptor containing vesicles
<p>Regulated delivery of AMPA receptors (AMPARs) to the postsynaptic membrane is an essential step in synaptic strength modification, and in particular, long-term potentiation (LTP). While LTP has been extensively studied using electrophysiology and light microscopy, several questions regarding the molecular mechanisms of AMPAR delivery via trafficking vesicles remain outstanding, including the gross molecular make up of AMPAR trafficking organelles and identification and location of calcium sensors required for SNARE complex-dependent membrane fusion of such trafficking vesicles with the plasma membrane. Here, we isolated AMPAR containing vesicles (ACVs) from whole mouse brains via immunoisolation and characterized them using immunoelectron microscopy, immunoblotting, and liquid chromatography tandem mass spectrometry (LC-MS/MS). We identified several proteins on ACVs that were previously found to play a role in AMPAR trafficking, including synaptobrevin-2, Rabs, the SM protein Munc18-1, the calcium-sensor synaptotagmin-1, as well as several new candidates, including synaptophysin and synaptogyrin on ACV membranes. Here, we present three biological replicates of liquid chromatography tandem mass spectrometry of AMPA receptor containing vesicles.</p>
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