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493 results for “LC”
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>
In silico Database for Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1)
<p>Modern methods of mass spectrometry have emerged recently allowing reliable, fast and cost-effective identification of pathogenic microorganisms. For example, matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has revolutionized the way pathogenic microorganisms are identified in today’s routine clinical microbiology. Furthermore, recent years have witnessed also substantial progress in the development of liquid chromatography-mass spectrometry (LC-MS) based proteomics for microbiological applications.</p> <p>In this context, we introduce a new concept for microbial identification by mass spectrometry. The proposed approach involves efficient extraction of proteins from cultivated microbial cells, digestion by trypsin and LC-MS measurements. MS1 data are then extracted and systematically tested against <em>in silico</em> libraries of peptide mass data. The first version of such a database has been computed from UniProt Knowledgebase [Swiss-Prot and TrEMBL] and contains more than 12,000 strain-specific synthetic mass profiles. The database is stored in the pkf data format which is interpretable by the MicrobeMS software package (requires MicrobeMS version 0.82, or later).</p> <p><em>For details see the following preprint: Lasch, P. Schneider, A. Blumenscheit, C. and Doellinger, J. “Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data”. bioRxiv preprint, http://dx.doi.org/10.1101/870089.</em></p>
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>
Hourly LC impacts - Acidification - current mix and future scenarios
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Acidifcation, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
Hourly LC impacts - Primary Non-renewable energy - current mix and future scenarios, average demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Primary Non-renewable Energy, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
Hourly LC impacts - Particulate Matter - current mix and future scenarios, average demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Particulate Matter, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
Hourly LC impacts - Ozone Layer Depletion - current mix and future scenarios, average demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Ozone Layer Depletion, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
Hourly LC impacts - Fresh water Eutrophication - current mix and future scenarios
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Fresh water Eutrophication, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
Hourly LC impacts - Terrestrial Eutrophication - current mix and future scenarios, average demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Terrestrial Eutrophication, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
Hourly LC impacts - Marine Eutrophication - current mix and future scenarios, average demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Marine Eutrophication, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
Hourly LC impacts - Global Warming - current mix and future scenarios, average demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Global Warming, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
Hourly LC impacts - Resource use - minerals and metals - current mix and future scenarios, average demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Resource use - minerals and metals, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
Hourly LC impacts - Soil Quality Index - current mix and future scenarios, average demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Soil Quality Index, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
LC impacts -24h profiles - current mix and future scenarios, marginal demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - marginal demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
[MetFrag] MoNA Export LC-MS-MS Spectra for MetFrag
<p>This is an updated version of the LC-MS/MS MoNA library for use in <a href="https://ipb-halle.github.io/MetFrag/">MetFrag</a>. </p> <p>Once you download this file, you can use it in <a href="https://github.com/ipb-halle/MetFragRelaunched/releases/latest">MetFrag Command Line</a> with the following command: </p> <pre><code>OfflineSpectralDatabaseFile = ~/MoNA-export-LC-MS-MS_Spectra-20241014-0.005.mb</code></pre> <p>Thanks to Bego for thoroughly testing this file and to Christoph for his tips throughout the years!</p>
Clinical outcome for TheraCal LC group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months
<p>Clinical outcome for TheraCal LC group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months</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>
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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)
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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.