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60 results for “liquid chromatography”
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>
Photosynthetic pigments of water column samples analyzed using High Performance Liquid Chromatography (HPLC), sampled during the Palmer LTER field seasons at Palmer Station, Antarctica, 1991 – 2023.
Phytoplankton pigment sampling was led by Prezelin from the 1991-1992 season through the 1993-1994 season, and then by Vernet from the 1994-1995 season through the 2006-2007 season. Schofield is the third, and current lead, beginning in the 2008-2009 season. Methods have been kept consistent as much as possible over the full time series and different Principal Investigators. Phytoplankton have a suite of accessory pigments in addition to Chlorophyll a, including other Chlorophyll’s (e.g. Chlorophyll b), Xanthophylls, and Carotenes. These accessory pigments can be used as chemotaxonomic markers to assess the composition and distribution of the phytoplankton community. For example, Fucoxanthin is a marker pigment of Diatoms, whereas Alloxanthin is a marker pigment of Cryptophytes. Accessory pigments also assist in photoacclimation and photoprotective processes. Water samples are collected throughout the water column at stations within the Palmer LTER region (primarily B and E, to 50m and 65m respectively). Water samples are filtered onto GF/F filters, and filters kept frozen at -80C until analysis. HPLC analysis is completed following Wright et al (1991). Following the guidelines set by NASA SeaHARRE, we use an internal standard and replicate injects on the HPLC to track recovery and replicability of the pigment extraction methods. Data is unavailable for the Palmer 2009-2010 season due to instrumentation problems and for the Palmer 2011-2012 season due to a freezer failure which resulted in the loss of samples. There is a temporary data gap for the Palmer 2015-2016, Palmer 2016-2017, Palmer 2019-2020, Palmer 2020-2021, and Palmer 2023-2024 seasons because those samples have not been analyzed yet.
Photosynthetic pigments of water column samples and analyzed with High Performance Liquid Chromatography (HPLC), collected aboard Palmer LTER annual cruises off the coast of the Western Antarctica Peninsula, 1991-2024.
Phytoplankton pigment sampling was led by Prezelin from 1991-1994, and then by Vernet from 1995-2008. Schofield is the third, and current lead, beginning in 2009. Methods have been kept consistent as much as possible over the full time series and different Principal Investigators. Phytoplankton have a suite of accessory pigments in addition to Chlorophyll a, including other Chlorophyll's (e.g. Chlorophyll b), Xanthophylls, and Carotenes. These accessory pigments can be used as chemotaxonomic markers to assess the composition and distribution of the phytoplankton community. For example, Fucoxanthin is a marker pigment of Diatoms, whereas Alloxanthin is a marker pigment of Cryptophytes. Accessory pigments also assist in photoacclimation and photoprotective processes. Water samples are collected throughout the water column along the Western Antarctic Peninsula at regular LTER grid stations where CTD casts are preformed and in surface waters at underway stations, where CTD casts are not done, using the ship's flow-through seawater system. Water samples are filtered onto GF/F filters, and filters kept frozen at -80C until analysis. HPLC analysis is completed following Wright et al (1991). Following the guidelines set by NASA SeaHARRE, we use an internal standard and replicate injects on the HPLC to track recovery and replicability of the pigment extraction methods and the HPLC. Data is unavailable for the LMG10-01 cruise due to instrumentation problems and for the LMG12-01 cruise due to a freezer failure which resulted in the loss of samples. There is no data for 2021 because there was no LTER cruise.
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>
Dataset: Assessing Background Contamination of Sample Tubes used in Human Biomonitoring by Non-targeted Liquid Chromatography–High Resolution Mass Spectrometry
<p>Data set of the Publication: </p> <div> <div>Krauss, Martin, Carolin Huber, Tobias Schulze, Martina Bartel-Steinbach, Till Weber, Marike Kolossa-Gehring, und Dominik Lermen (2024): Assessing background contamination of sample tubes used in human biomonitoring by non-targeted liquid chromatography–high resolution mass spectrometry. <em>Environment International</em> 183: 108426. <a href="https://doi.org/10.1016/j.envint.2024.108426">https://doi.org/10.1016/j.envint.2024.108426</a>.</div> </div> <p>- raw LC-HRMS data in mzML format for positive and negative mode.</p> <p>- merged MS/MS spectra of whole data set after MZMine 2.53 processing in mgf format.</p> <p> </p>
Quantification of ADHD Medication in Biological Fluids with Liquid Chromatography: A Comprehensive Review - Metadata
<p>This file is the metadata related to the publication "Quantification of ADHD Medication in Biological Fluids with Liquid Chromatography: A Comprehensive Review".</p>
Liquid chromatography mass spectrometry data of HMCES SRAP domain
<p>Liquid chromatography mass spectrometry data for the HMCES SRAP domain alone (Apo_SRAPd) and for the SRAP domain incubated with abasic site containing DNA (SRAPd_DPC). All LC/MS data were acquired according to the previously published protocol (Chalk R., Springer New York, 2017).</p>
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>
Dataset for Unified acidity of liquid chromatography mobile phases with methanol and acetonitrile
<p>Dataset for article "Unified acidity of liquid chromatography mobile phases with methanol and acetonitrile".</p> <p>Here we report the data of 78 reversed-phase liquid chromatography-mass spectrometry mobile phases that were determined by potential differences in a symmetric cell with two glass electrode half-cells and almost ideal ionic liquid triethylamylammonium bis((trifluoromethyl)sulfonyl)imide [N<sub>2225</sub>][NTf<sub>2</sub>] salt bridge with multiple overlapping measurements. In addition, for 45 of these mobile phases, the potential difference between a glass and a double junction reference electrode were measured.</p> <p>Procedures used with Keysight B2987A Electrometer are given in <a href="https://dx.doi.org/10.17504/protocols.io.n92ld9dj8g5b/v2">dx.doi.org/10.17504/protocols.io.n92ld9dj8g5b/v2</a> and <a href="https://dx.doi.org/10.17504/protocols.io.byh2pt8e">dx.doi.org/10.17504/protocols.io.byh2pt8e</a>. For other instruments, the procedures differ by software and instrument connections.</p> <p> </p>
High Performance Liquid Chromatography (HPLC) pigment analysis from rosette bottle samples at various depths from CalCOFI-CCE Augmented cruises in the California Current System, 2002 to 2023 (ongoing).
High Performance Liquid Chromatography (HPLC) samples are collected from rosette bottles (from three to eight different depths in the photic zone) at stations located within the CCE region on CalCOFI cruises (since 2002, ongoing). The HPLC method is used to measure concentrations of chlorophylls and carotenoids in samples of particulate matter, which includes filtering and freezing the filter while at sea. The taxon-specific phyto-pigments are extracted back onshore. Concentrations of chlorophyll a are used as a proxy for phytoplankton biomass and concentrations of other taxon-specific pigments are used to determine contributions of phytoplankton taxa to total phytoplankton biomass.
MS data set: Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data
<p>Data set consisting of raw LC-MS2 data, LC-MS1 peak data and a description</p> <p>For unreviewed publication preprint: <strong>Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS<sup>1</sup>) and <em>in silico </em>Peptide Mass Data</strong></p> <p>ABSTRACT</p> <p>Over the past decade, modern methods of mass spectrometry (MS) have emerged that allow reliable, fast and cost-effective identification of pathogenic microorganisms. While MALDI-TOF MS has already revolutionized the way microorganisms are identified, recent years have witnessed also substantial progress in the development of liquid chromatography (LC)-MS based proteomics for microbiological applications. For example, LC-tandem mass spectrometry (LC-MS<sup>2</sup>) has been proposed for microbial characterization by means of multiple discriminative peptides that enable identification at the species, or sometimes at the strain level. However, such investigations can be very time-consuming, especially if the experimental LC-MS<sup>2</sup> data are tested against sequence databases covering a broad panel of different microbiological taxa.</p> <p>In this proof of concept study, we present an alternative bottom-up proteomics method for microbial identification. The proposed approach involves efficient extraction of proteins from cultivated microbial cells, digestion by trypsin and LC-MS measurements. MS<sup>1</sup> data are then extracted and systematically tested against an in silico library of peptide mass data compiled in house. The library has been computed from the UniProt Knowledgebase Swiss-Prot and TrEMBL databases and comprises more than 12,000 strain-specific in silico profiles, each containing tens of thousands of peptide mass entries. Identification analysis involves computation of score values derived from spectral distances between experimental and in silico peptide mass data and compilation of score ranking lists. The taxonomic positions of the microbial samples are then determined by using the best-matching database entries. The suggested method is computationally efficient – less than two minutes per sample - and has been successfully tested by a set of 19 different microbial pathogens. The approach is rapid, accurate and automatable and holds great potential for future microbiological applications.</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> <p> </p>
Example run of the Biognosys iRT standard for liquid chromatography - mass spectrometry
<p>Depending on the composition of QC samples the LC performance can be monitored using peptides that elute over the entire gradient, and the dynamic range can be monitored if peptides are present in varying concentrations. Depicted here is the Biognosys iRT standard which consists of eleven peptides with varying chromatographic retention.</p>
Ishikawa diagram of sources of variability impacting a liquid chromatography - mass spectrometry experiment
<p>An Ishikawa diagram (non-exhaustively) highlighting some of the major sources of variability in each of the stages of an LC-MS experiment. These and other sources of variability will impact the results and should be considered in a comprehensive quality control workflow.</p>
Liquid chromatography - mass spectrometry workflow
<p>A typical LC-MS experiment consists of a sample preparation, a liquid chromatography, a mass spectrometry, and a bioinformatics stage. The sample preparation includes the proteolytic digestion of proteins into peptides. Next, consecutively the peptides are separated through liquid chromatography and measured through mass spectrometry. Finally, the acquired spectra are interpreted through bioinformatics means.</p>
Liquid Chromatography Mass Spectrometry Data (LCMS-1) from Microalgal Co-culture of Skeletonema marinoi and Prymnesium parvum
<p>The mzML files in this dataset are the Liquid Chromatography Mass Spectrometry Data (LCMS-1) data files, derived from the RAW MS files using GNPS file convertor. These files contain unprocessed features 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>). The microalgae were grown in co-culture chambers, so the naming convention A, and B refer to the two sides of the chamber. So, 1a and 1b are <em>S. marinoi</em> monocultures, but 11a and 11b refer to s. marinoi and <em>P. parvum</em> respectively.</p> <p>The results of metabolomics data analysis are available on Zenodo with DOI: 10.5281/zenodo.10143554</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>
Raw data for optimization and calibration of Micellar liquid chromatography as a sustainable tool to quantify three statins in oral solid dosage forms
<p>A method based on micellar liquid chromatography has been developed to determine rosuvastatin, lovas- tatin and simvastatin in oral solid dosage forms. Samples were solved in mobile phase up to the target concentration, filtered and directly injected. The three statins were resolved in 30 min, using an aqueous solution of 0.10 M sodium dodecyl sulfate –7.0% 1-butanol, buffered at pH 3 with 0.01 M phosphate salt as mobile phase, running under isocratic mode at 1 mL/min through a C 18 column. Detection was at 240 nm. The effect of sodium dodecyl sulfate on elution strength was more important than that of the organic solvent. The procedure was successfully validated by the guidelines of the International Coun- cil for Harmonization in terms of: specificity, linearity ( r 2 > 0.990), calibration range (1.5 - 15 mg/L for rosuvastatin, 0.5–10 mg/L for lovastatin and simvastatin), limit of detection (0.4, 0.2 and 0.15 mg/L for ro- suvastatin, lovastatin and simvastatin, respectively), trueness (98.8–101.7%), precision ( < 2.7%), carry-over effect, robustness, and stability. Values were inside the acceptance criteria of the Methods, Method Veri- fication and Validation, Food and Drug Administration-Office of Regulatory Affairs, thus ensuring the re- liability of the results. The main feature was the low proportion of organic solvent used, thus making the procedure sustainable and green. Besides, it was easy-to-conduct and with high sample-throughput, and then useful for routine analysis in pharmaceutical quality control. Finally, it was applied to commercial pharmaceutical preparations.</p>
Ability of Mayo Clinic High-performance Liquid Chromatography (HPLC) Method to Measure Fecal Bile Acids
ClinicalTrials.gov study NCT02111603. IPD Sharing: YES. Countries: 1. Publications: 1.
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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)
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
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