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75 results for “lc-ms”
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>
Targeted and untargeted LC-MS copepodamide datasets for marine and freshwater copepods
<p>This repository contains the datasets, analysis code and output generated and used in the scientific article titled "Mass spectroscopy reveals compositional differences in copepodamides from limnic and marine copepods" published in Scientific Reports (https://doi.org/10.1038/s41598-024-53247-1)<em>.</em></p> <p>Detailed information about the datasets are available in the README.txt.</p> <p>The source dataset created from the sampling effort, with targeted liquid chromatography coupled mass spectrometry (LC-MS) data, taxonomic information of individual copepods, their length measurements, estimated biomass etc is available in <em><strong>Masterfile_targeted_data_final.xlsx</strong></em>.</p> <p>The source dataset for precursor LC-MS scan data is available in <em><strong>Precursor_data_Deisotoped.xlsx</strong></em>. </p> <p>The resulting analysis data frames (last sheet in each .xlsx file) are available as separate .csv files (<strong>Arnoldt_targeted_analysis_data.csv</strong> & <strong>Arnoldt_targeted_analysis_data.xlsx</strong>). These files are denoted "Supplementary Data. 2" and "Supplementary Data. 1" respectively in the main article. Data files <strong>Chromatography.csv</strong> and <strong>zooplankton_composition_bulk.csv</strong> are used to create chromatograph line plots (Figures 3a & 3b in the article) and one of the supplementary figures (S1), respectively.</p> <p>A R-markdown file (<strong>Arnoldt_R_Code</strong><em><strong>.Rmd</strong></em>) with the code to analyse and visualise all data, and its html-output file (<strong>Arnoldt_R_Code_Output</strong><em><strong>.html</strong></em>) are also available here. The markdown files uses the four csv-files described in the paragraph above to generate all analyses and figures. The output (.html) file is denoted "Supplementary Code" in the main article.</p>
BioDeep/metabolomics-report-standards: BioDeep LC-MS Metabolite Identification Demo Report
<p><em>A Metabolomics unknown feature identification report industry standards from <a href="http://www.bionovogene.com/">BioNovoGene</a> corporation.</em></p> <p>2019.08.16# at Suzhou, China</p> <p>There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics data sets. Reporting of standard metadata provides a biological and empirical context for the data, enables the reinterrogation and comparison of data by others, which is also could let us interpret the result in a more clearly way.</p> <p>This article is mainly address at the unknown metabolite identification in LC-MS experiment, and proposes the reporting standards related to the chemical analysis aspects of metabolomics experiments its metabolite identification.</p> <p>Some terms in this article that address to:</p> <ul> <li>feature, the term feature in this article is refer to a parent ion in LC-MS experiment result raw data. Where a parent ion feature is a peak in chromatography data, which is consist of mass to charge ratio in ms1 level and its retention time (with a range of lower bound and upper bound) in chromatography experiment result.</li> <li>annotation, the term annotation in this article is refer to the multidimensional information about the metabolite that assigned to a unknown feature, which such multidimensional information consist with the metabolite its cross reference id in different database, common name, basic chemical data like mass and formula composition and its molecule structure information, etc.</li> <li>alignment, the term alignment means a kind of operation that use to compare the similarity of the mass spectrum data between user sample and the reference standard library. Such similarity comparison result is the most important evidence that use for unknown feature its identification.</li> <li>score, the term score is a kind of numeric value that produced by the alignment comparison calculation. Literally, the higher score the alignment it produce, the better the result it is.</li> </ul> <p>Our metabolite identification report consist with two parts of data which present to our user:</p> <ol> <li>Report excel table that contains the raw sample information and the meta annotation information of the metabolite.</li> <li>Data visual plot for the mass spectrum alignment details.</li> </ol>
Lipidomics LC-MS analysis support tools for outlier detection
<p>Identification of features with high levels of confidence in liquid chromatography-mass spectrometry (LC MS) lipidomics research is an essential part of biomarker discovery, but existing software platforms can give inconsistent results, even from identical spectral data. This poses a clear challenge for reproducibility in bioinformatics work, and highlights the importance of data-driven outlier detection in assessing spectral outputs – here demonstrated using a machine learning approach based on support vector machine regression combined with leave-one-out cross validation – as well as manual curation, in order to identify software-driven errors driven by closely related lipids and by co-elution issues.</p> <p>The lipidomics case study dataset used in this work analysed a lipid extraction of a human pancreatic adenocarcinoma cell line (PANC-1, Merck, UK, cat no. 87092802) analysed using an Acquity M-Class UPLC system (Waters, UK) coupled to a ZenoToF 7600 mass spectrometer (Sciex, UK). Raw output files are included alongside processed data using MS DIAL (v4.9.221218) and Lipostar (v2.1.4) and a Jupyter notebook with Python code to analyse the outputs for outlier detection.</p>
Amoxicillin degradation pathways and mass spectra raw data (using LC-MS orbitrap)
<p>The link provides five documents namely:</p> <p>File No.1 (Proposed Chemical Structures-tabulated)</p> <p>File No.2 (MS and MS2 images) support for File no.1</p> <p>File No.3 Transformation Products Pathway</p> <p>File No.4 Explanation + Justification of proposed chemical structures</p> <p>Raw Data obtained from compound discoverer</p>
Data for: "Unlocking the potential of LC-MS through an XIC-based algorithm for chromatographic optimisation"
<p>This dataset is for upload of supplementary info and data for my master research thesis at the University of Amsterdam.</p> <p>All the compounds in each pesticide mix of the RESTEK multiresidue kit can be found along with some descriptors.</p> <p>For easy use of the developed algorithm without having to generate any mzxml files, a few files are included on which SAFD and CompCreate have already been performed using three different LC methods, Their gradients are also provided. To run the code, a package has been developed and is ready for installation at: https://github.com/tobihul/LC_MS_Resolved_Peaks. </p> <p> </p>
LC-MS/MS, PSM and BLG properties data from "Benchmarking the identification of a single degraded protein to explore optimal search strategies for ancient proteins"
<p>This dataset contains the data analyzed in:</p> <p>Rodriguez Palomo I, Nair B, Chang Y, Dartigues B, Dekker K, Mackie M, Evans M, Macleod R, Olsen JV, Collins MJ. (2023) "<em>Benchmarking the identification of a single degraded protein to explore optimal search strategies for ancient proteins"</em></p> <p>It contains the following data:</p> <ul> <li>raw_files.zip Thermo RAW files for the 0, 4 and 128 days samples</li> <li>benchmark_results.zip PSMs data from the analysis of the RAW files <ul> <li>Data from runs in Mascot, Fragpipe, pFind, Metamorpheus, MaxQuant and DeNovoGUI</li> <li>Parameters and workflow files for MaxQuant and Fragpipe</li> </ul> </li> <li>bovin_blg_prop.zip BLG properties files: amyloid formation, 3D structure and solvent accessibility</li> <li>benchmark_table.csv Table with runs settings for benchmarking</li> <li>parameters_table.xlsx Spreadsheet with software parameters, derived from files used to run each software</li> </ul> <p> </p>
Portimine A toxin causes skin pathology through ZAKα-dependent NLRP1 inflammasome activation: LC-MS/MS raw data for Figure 1. C
<p>This dataset pertains to the LC-MS/MS analyses conducted as part of a study on microalgal toxins present in samples from Senegal, published in the paper entitled <em>"Portimine A toxin causes skin pathology through ZAK</em><em>α</em><em>-dependent NLRP1 inflammasome activation."</em> The data correspond to the quantification results of environmental samples presented in Figure 1C.</p> <p>The raw data were acquired using Analyst software (Applied Biosystems proprietary software). The materials and methods used to generate these data are detailed in the associated publication in <em>EMBO Molecular Medicine</em> (ISSN: 1757-4676, 2024).</p>
LC-MS raw data_Lysophosphatidic Acid Shifts Metabolic and Transcriptional Landscapes to Induce a Distinct Cellular State in Human Pluripotent Stem Cells
<p><strong>LC-MS/MS analysis</strong></p> <p><strong>Metabolite extraction</strong></p> <p>For LC-MS/MS quantification, cell sample preparation was conducted as described in the previous literatures (Ying, Kimmelman et al. 2012, Zhang, Badur et al. 2016). Briefly, the spent medium was removed, and cells were rinsed with 1 mL/well 0.9% (w/v) saline twice. Then 0.5 mL/well -80°C 0.2 μg/mL norvaline containing 80% methanol was added to quench the metabolism. Cells were scraped off into 1.5-mL eppendorf tube and stored in -80℃ overnight. The mixtures were vortexed and then centrifuged 12500 × <em>g </em>for 15 min at 4℃. The supernatant was used for LC-MS analysis.</p> <p><strong>LC-MS/MS method</strong></p> <p>Waters Xevo TQD coupled with Waters Acquity UPLC system was used for quantification. Acquity UPLC BEH HILIC column (2.1 × 100 mm, 1.7 μm), Acquity UPLC BEH C18 column (2.1 × 100 mm, 1.7 μm), and Acquity UPLC BEH amide column (2.1 × 100 mm, 1.7 μm) were used for the separation of metabolites. Column temperature was set at 40 °C.</p> <p>For the quantification of norvaline, amino acids, GSH, GSSG, SAH, SAM, ascorbic acid and myo-inositol, amide column was used for the separation. Acetonitrile with 0.1% formic acid (A) and water with 0.1% formic acid (B) were used as mobile phases. The gradient setting is: 0-4 min, 99% A to 90% A; 4-10 min, 90% A to 67% A; 10-13 min, 67% A to 1% A; 13-15 min, 1% A; 15-16.5 min, 1% A to 99% A; 16.5-20 min, 99% A. Flowrate was set as 0.4 mL/min.</p> <p>For the quantification of metabolites involved in TCA cycle, energy related and ribonucleotides, an amide column was used for the separation. Acetonitrile with 0.1% formic acid (A) and water with 0.1% formic acid (B) were used as mobile phases. The gradient setting is: 0-2 min, 80% A; 2-3 min, 80% A to 20% A; 3-5 min, 20% A; 5-6 min, 20% A to 80% A; 6-10 min, 80% A. Flowrate was set as 0.4 mL/min.</p> <p>For the quantification of acetate, acetyl-CoA and metabolites involved in glycolysis and pentose phosphate pathway, HILIC column was used for the separation. Acetonitrile (A) and 10 mM ammonium bicarbonate were used as mobile phases. The gradient setting is: 0-2 min, 10% A; 2-5 min, 10% A to 5% A; 5-6 min, 5% A to 10% A; 6-10 min, 10% A. Flowrate was set as 0.2 mL/min.</p> <p>For the quantification of LPA, LPC and PC, HILIC column was used for the separation. Acetonitrile (A) and 10 mM ammonium bicarbonate aqueous solution (B) were used as mobile phases. The gradient setting is: 0-2 min, 95% A; 2-4 min, 95% A to 10% A; 4-7 min, 10% A; 7-9 min, 10% A to 95% A; 9-15 min, 95% A. Flowrate was set as 0.2 mL/min.</p> <p>For the quantification of CDL lipids, C18 column was used for the separation. 98% Acetonitrile aqueous solution (A) and 10 mM ammonium acetate 90% acetonitrile aqueous solution (B) were used as mobile phases. The gradient setting is: 0-5 min, 0.1% A; 5-6 min, 0.1% A to 99.9% A; 6-11 min, 99.9% A; 11-12 min, 99.9% A to 0.1% A; 12-15 min, 0.1% A. Flowrate was set as 0.4 mL/min.</p> <p>Argon was used as source gas, capillary voltage was 3500 V, and desolvation temperature was 500 °C. Multiple reaction monitoring (MRM) was conducted, and the ion transitions are listed in the supplemental Table S2. Selected ion recording (SIR) was conducted for the detection of CDL-related lipids, and the setting is listed in the supplemental Table S3.</p> <p>Standard solutions of TCA metabolites (100 μg/mL) and intermediates of glycolysis and pentose phosphate pathway (10 μg/mL) were prepared to confirm the retention time. Peak intensity of product ion was used for the quantification. Data analysis was performed by TargetLynx software (Waters) with statistical analysis in Graphpad Prism (version 8.4.0) and R.</p>
LC-MS² meta data for each MassBank (MB) subset
<p>The CSV-file (tab used as separator) provides the Liquid-chromatography (LC) and Tandem-mass spectrometry (MS²) configurations for each MassBank (MB) subset used in the publication: "Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data" by Bach et al. (2022).</p>
Fingerprint Matrix Files for "Machine Learning-based Bioactivity Classification of Natural Products Using LC-MS/MS Metabolomics"
<p>These files are the necessary dataset to reproduce the observed machine learning metrics in the paper "Machine Learning-based Bioactivity Classification of Natural Products Using LC-MS/MS Metabolomics" in review at the Journal of Natural Products. </p> <ul> <li>Multiclassifier_23_Drug_Class_Train-Test_Fingerprint_Matrix.tsv is the accumulated positive training set for the 23 different classes demonstrated in the training and testing sets.</li> <li>Negative_Train-Test_Fingerprint_Matrix.tsv is the negatives training and testing examples derived from the RIKEN NP Depo which represent a diverse set of natural product compounds that serve as the counter points to the positive examples.</li> <li>GNPS_23_Drug_Class_Fingerprints_Matrix.tsv is the dataset of fingerprints generated from the publically available GNPS MSMS dataset. These training examples serve to confirm the ability of the machine learning model to generalize to experimental data. </li> <li> Negative_Train-Test_Fingerprint_Matrix.tsv is the dataset of negative training examples derived from the publically available spectra from the GNPS dataset. It is composed of nearly 2,800 random MSMS spectra to compose a diverse negative evaluation set. </li> <li>Random_GNPS_Fingerprints.tsv is the dataset of fingeprints of 9,443 random spectra from GNPS used to evaluate the false positive rate of each model.</li> </ul>
Data analysis of an LC-MS dataset from a human urine biofluid cohort study
<p>Supplementary dataset and tutorials for the "<strong>Statistical analysis in metabolic phenotyping"</strong></p> <p> </p> <p>This repository contains Jupyter Notebooks with two examplar metabolomic data analysis workflows, applied to a liquid chromatography mass spectrometry dataset (LC-MS). The LC-MS dataset used comes from a metabolic phenotyping investigation of human urine biofluid samples from a dementia cohort. In this sample set, baseline spot urine samples (first sample collected after recruitment to the study) were collected as part of the AddNeuroMed<sup>1</sup> and ART/DCR study consortia, with the aim of identifying biomarkers of neurocognitive decline and Alzheimer’s disease. These samples were analysed by LC-MS and <sup>1</sup>H NMR, using the methods described by Lewis <em>et al</em><sup>2</sup> and Dona <em>et al</em>. Detailed information about this cohort and other available phenotypic measurements can be found in Lovestone and the ANMERGE<sup>3</sup> repository, which can be accessed via the Sage BioNetworks portal (<a href="https://doi.org/10.7303/syn22252881">https://doi.org/10.7303/syn22252881</a>). Information about the metabolic profiling experiments can be found in the study's MetaboLights entry: <a href="https://www.ebi.ac.uk/metabolights/MTBLS719">https://www.ebi.ac.uk/metabolights/MTBLS719</a>.</p> <p> </p> <p>1. Lovestone, S. <em>et al.</em> AddNeuroMed - The european collaboration for the discovery of novel biomarkers for alzheimer’s disease. in <em>Annals of the New York Academy of Sciences</em> (2009). doi:10.1111/j.1749-6632.2009.05064.x</p> <p>2. Lewis, M. R. <em>et al.</em> Development and Application of UPLC-ToF MS for Precision Large Scale Urinary Metabolic Phenotyping. <em>Anal. Chem.</em> <strong>88</strong>, acs.analchem.6b01481 (2016).</p> <p>3. Birkenbihl, C. <em>et al.</em> ANMerge: A comprehensive and accessible Alzheimer’s disease patient-level dataset. <em>medRxiv</em> (2020). doi:10.1101/2020.08.04.20168229</p>
Proteomic data (LC-MS/MS) of human plasma samples
<p>LC-MS/MS analysis of 8 different samples of plasma: 4 samples correspond to the activated platelet-rich plasma (PRP) fractions from 4 different patients with infertility due to Asherman's syndrome and/or endometrial atrophy; 2 samples correspond to the activated and not-activated, respectively, PRP fractions from a control fertile patient; 2 samples correspond to the activated and not-activated, respectively, fractions from a commercial umbilical cord plasma.</p>
EMERGE 2016 Autochamber Sites LC-MS
METHODS:<br> Water soluble metabolites were extracted from peat by adding 7 mL of autoclaved milliQ water to 1g of peat in a sterile 15 mL Eppendorf tube. Tubes were vortexed twice for 30 seconds, and then the peat-water mixture was sonicated for 2 hours at 22˚C. Samples were then centrifuged to separate the supernatant, which served as the water extract. <br> <br> Water extracted metabolites were thawed at room temperature and centrifuged again to remove any potential particles that formed after thawing. Next, each sample was split into two 2ml glass tube vials (1 ml each), one for hydrophilic interaction liquid chromatography (HILIC) and the other for reverse-phase (RP) liquid chromatography. Samples in both vials were then dried down completely on a Vacufuge plus (Eppendorf, USA). Samples were resuspended in a solution of 50% Acetonitrile and 50% water for HILIC and a solution of 80% water and 20% HPLC grade methanol for RP.<br><br>A Thermo Scientific Vanquish Duo ultra-high performance liquid chromatography system (UHPLC) was used for the liquid chromatography step. Extracts were separated using a Waters ACQUITY HSS T3 C18 column for RP separation and a Waters ACQUITY BEH amide column for HILIC separation.<br><br>Samples were injected in a 1 μL volume on column and eluted as follows: for RP the gradient went from 99% mobile phase A (0.1% formic acid in H2O) to 95% mobile phase B (0.1% formic acid in methanol) over 16 minutes. For HILIC the gradient went from 99% mobile phase A (0.1% formic acid, 10 mM ammonium acetate, 90% acetonitrile, 10% H¬2O) to 95% mobile phase B (0.1% formic acid, 10 mM ammonium acetate, 50% acetonitrile, 50% H2O). Both columns were run at 45 °C with a flowrate of 300 μL/min.<br>A Thermo Scientific Orbitrap Exploris 480 was used for spectral data collection with a spray voltage of 3500 V for positive mode (for RP) and 2500 V for negative mode (for HILIC) using the H-ESI source. The ion transfer tube and vaporizer temperature were both 350 °C. Compounds were fragmented using data-dependent MS/MS with HCD collision energies of 20, 40, and 80.<br><br>The Compound Discoverer 3.2 software (Thermo Fisher Scientific) was used to analyze the data using the untargeted metabolomics workflow. Briefly, the spectra were first aligned followed by a peak picking step. Putative elemental compositions of unknown compounds were predicted using the exact mass, isotopic pattern, fine isotopic pattern, and MS/MS data using the built in HighChem Fragmentation Library of reference fragmentation mechanisms. Metabolite annotation was performed using spectral libraries and compound databases. First, fragmentation scans (MS2) searches in mzCloud were performed , which is a curated database of MSn spectra containing more than 9 million spectra and 20000 compounds.<br><br> Second, predicted compositions were obtained based on mass error, matched isotopes, missing number of matched fragments, spectral similarity score (calculated by matching theoretical and measured isotope pattern), matched intensity percentage of the theoretical pattern, the relevant portion of MS, and the MS/MS scan. The mass tolerance used for estimating predicted composition was 5 ppm. Finally, annotation was complemented by searching MS1 scans on different online databases with ChemSpider (using either the exact mass or the predicted formula). Based on the annotation results, metabolites were divided into three categories: 1) full match on the three methods used (mzCloud, predicted composition, and ChemSpider), 2) full match by two methods (Predicted composition and ChemSpider) and 3) annotated only by one method (ChemSpider).<br><br><br>COLUMN DEFINITIONS:<br>For both files: <br>Columns A-N : Annotation information<br>Columns O-P: KEGG pathway using molecular formula<br>Columns S-AW : Normalized peak areas per sample<br><br>FUNDING:<br>This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.<br>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.<br>This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0010580 and DE-SC0016440.
Algoa Bay 2018/2019 LC-MS/MS DOM study
<p>This dataset contains the raw and processed data for a nontargeted tandem mass spectrometry study of dissolved organic matter (DOM) in surface water collected from the Algoa Bay system in the Eastern Cape of South Africa in 2018/2019</p>
LC-MS raw data for proteomic elucidation of the targets and primary functions of picornavirus 2A protease
<p>This dataset contains LC-MS raw data for pulldowns from the project "Proteomic elucidation of the targets and primary functions of picornavirus 2A protease." The descriptions of the raw data files are in Summary_Table_MS_RawData.pdf.</p>
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>
LC-MS raw dataset of Dendrobium extracts
<p class="MsoNormal"><span>Orbitrap mass spectrometry is one kind of high-</span><span>resolution mass analyzer containing </span><span>multistage </span><span>fragment monitoring, a selective fragmentation mode, and a personalized data acquisition method</span><span><span>, </span></span><span><span>which </span></span><span><span>i</span></span><span><span>s </span></span><span>widely applied in </span><span>p</span><span>harmacokinetic analysis, </span><span><span>t</span></span><span><span>he </span></span><span>food </span><span>industry</span><span>, environmental monitoring</span><span><span>,</span></span><span> and proteomi</span><span>cs. </span><span><span>Alkaloids are an important active ingredient in Dendrobium officinale, but there are few studies on their isolation and identification. Here, </span></span><span>a </span><span><span>c</span></span><span><span>om</span></span><span><span>bin</span></span><span><span>atio</span></span><span><span>n </span></span><span><span>meth</span></span><span><span>od </span></span><span>for </span><span><span>s</span></span><span><span>ep</span></span><span><span>a</span></span><span><span>ra</span></span><span><span>tin</span></span><span><span>g</span></span><span> and </span><span><span>de</span></span><span><span>tect</span></span><span><span>ing </span></span><span>alkaloids and </span><span><span>the</span></span><span><span>ir </span></span><span>precursors</span><span> </span><span>was </span><span>established. </span><span>T</span><span>he accurate mass </span><span>of the </span><span>components</span><span> separated by HPLC were determined using an </span><span>high resolution</span><span><span> </span></span><span>mass analyzer. </span><span>T</span><span>he multistage fragmentation </span><span><span>p</span></span><span><span>at</span></span><span><span>hway</span></span><span> of </span><span>alkaloids</span><span> w</span><span><span>as</span></span><span> </span><span>speculated</span><span><span> </span></span><span>based on a database-dependent retrieval method.</span></p> <p class="MsoNormal"><span><span>By using a SPE-HP</span></span><span><span>LC</span></span><span><span>-</span></span><span><span>M</span></span><span><span>S</span></span><span><span>/MS </span></span><span><span>method, the potential compounds were tentatively identified by aligning the accurate molecular weight with the METLIN and Dictionary of Natural Products databases. The chemical structures and main characteristic fragments of the potential compounds were further confirmed by retrieving the multistage mass spectra from the MassBank and METLIN databases.The Mass Frontier software was used to speculate the fragmentation pathway of the identified compounds. Seven alkaloids were separated and identified from </span></span><em><span><span>D. officinale</span></span></em><span><span>, which were mainly classified into five types (tropane alkaloids, tetrahydroisoquinoline alkaloids, quinolizidine alkaloids, piperidine alkaloids and spermidine alkaloids). Besides the alkaloids, forty-nine chemical substances, including guanidines, nucleotides, dipeptides, sphingolipids and nitrogen-containing glucosides, were concurrently identified. </span></span></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 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>
Proteomics LC-MS/MS test dataset for protein quantitation via stable isotope labelling
<p>The provided mzML file can be used as a test dataset for protein identification and quantitation software. It was generated from human embryonic kidney (HEK) cells that were either unlabelled or labelled with heavy SILAC (K6R6, unimod accession 188, PSI-MS Name: "Label:13C(6)"). Apart from different labelling, the HEK cells were kept in exactly the same conditions and harvested simultaneously. Light and heavy labelled proteins from HEK cell lysate were mixed in a certain ratio, digested with Trypsin and measured on a ThermoFisher QExactive mass spectrometer. A more detailed description on the generation of the dataset will soon be accessible at PRIDE.</p> <p>The provided mzML file has been converted from Thermo RAW and slightly modified via msConvert (ProteoWizard). To reduce the filesize and to speed up analysis, it has further been filtered to contain only the data measured between 2,000 sec and 3,000 sec of the original LC-MS/MS run.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.