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Dataset results
102 results for “spectrometry data”
mzrtsim: Raw Data Simulation for Reproducible Gas/Liquid Chromatography–Mass Spectrometry Based Non-targeted Metabolomics Data Analysis
<p>All the data for 'mzrtsim: Raw Data Simulation for Reproducible Gas/Liquid Chromatography–Mass Spectrometry Based Non-targeted Metabolomics Data Analysis'</p> <p>sim.zip is stimulated data for intensity cutoff 0.05. simxcms.csv is peak intensity profiles for their simulated peaks.</p> <p>sim3.zip are simulated data for normal/leading/tailing peaks with tailing factor of 1, 0.8, and 1.5, respectively.</p> <p>All the csv files begin with sim3 are extracted peaks list from the sim3.zip with corresponding data analysis software.</p> <p>csv.zip recorded the m/z, retention time, intensity, and compounds name for simulated compound for each condition (sim.zip and sim3.zip).</p> <p>sep1.mzML: simulation for 8 isomers with similar m/z while different retention times. 7 peaks are non baseline separation peaks. Peaks profile is saved in spe1.csv file.</p> <p>xcms.csv, mzmine.csv, openms.csv: peaks found in sep1.mzML by xcms, mzmine 4.5 and openms, respectively.</p> <p>R code: <a href="https://github.com/yufree/democode/blob/master/meta/simfin.R">https://github.com/yufree/democode/blob/master/meta/simfin.R</a></p> <p>Website of mzrtsim package: https://yufree.github.io/mzrtsim/</p>
Mass spectrometry raw data for "Proteomics reveals substantial differences between in vitro matured abattoir-derived and in vivo matured oocytes in cattle"
<p><em><span>In vitro</span></em><span> production (IVP) of bovine embryos still has its limitations such as low blastocyst rate and lower embryo quality, resulting in lower pregnancy rates following the transfer of IVP embryos compared to <em>in vivo</em> produced embryos. </span><span>Given these differences in developmental competence, RNA sequencing and microarray technology have been applied to describe the differences in transcriptional activity between <em>in vitro</em> and <em>in vivo</em> produced embryos. All but one of these studies solely utilized oocytes obtained from slaughterhouse material for the <em>in vitro</em> production of embryos, thereby introducing the possibility, that differences between IVP and <em>in vivo</em> embryos are in part attributable to differing sources of oocytes. The aim of the present study was therefore to compare the proteome of oocytes retrieved from slaughterhouse material, with and without a period of <em>in vitro</em> maturation and <em>in vivo</em> matured oocytes obtained from donor cattle following superovulation. <span>For each group the protein pattern of four biological replicates containing ten oocytes each were analyzed via SWATH<sup>TM</sup>-MS.</span></span></p>
MALDI-TOF-MS reference spectra and sequence data for domesticated equids (horse and donkey) collagen for Zooarchaeology by Mass Spectrometry (ZooMS)
<p>MALDI-TOF-MS spectra of extracted collagen from modern reference and archaeological bone samples to develop markers for Zooarchaeology by Mass Spectrometry (ZooMS) to distinguish between Equus species. For each sample digestions were done in both trypsin and chymotrypsin separately. Information about the species of the samples can be found in 'sample metadata.csv' file. Information on the extraction and digestion protocol can be found in the associated manuscript. The sequence data contains alignments of the proteins COL1A1 and COL1A2 for available Equus collagen protein sequences. More information on these files can be found in the corresponding manuscript to this dataset.<br> </p>
Supplementary data - Simultaneous polyclonal antibody sequencing and epitope mapping by cryo electron microscopy and mass spectrometry – a perspective
<p>Analysis files and scripts for <a href="https://doi.org/10.1101/2024.06.21.600107" target="_blank" rel="noopener">associated manuscript</a>. </p> <ul> <li>CR3022.zip: script (in Rust) and necessary data to run said script for CR3022 analysis with the results from running the script.</li> <li>MA-analysis-script.zip: script (in Rust) and necessary data to run said script for automated analysis of MA benchmark results.</li> <li>MA-analysis-data.zip: data from running the MA-analysis-script, containing all MA and Stitch output files.</li> <li>MA-analysis-data-EMPEM.zip: data from running MA and Stitch on the EMPEM benchmark.</li> </ul>
Data from: Normalizing gas-chromatography–mass spectrometry data: method choice can alter biological inference
<p>Gas-Chromatography Mass Spectrometry data from European badger (<em>Meles meles</em>) sub-caudal gland secretion used in:</p> <p>Noonan, M.J., Tinnesand, H.V.,<sup> </sup>and Buesching, C.D. (2018). Normalizing gas-chromatography–mass spectrometry data: method choice can alter biological inference. BioEssays, 40(6): 0-0. DOI: 10.1002/bies.201700210.</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>
Galaxy Training Material for Mass spectrometry: GC-MS data processing (with XCMS, RAMClustR, RIAssigner, and matchms)
<p>This dataset contains the training data for the <strong>Mass spectrometry: GC-MS data processing (with XCMS, RAMClustR, RIAssigner, and matchms)</strong> GTN tutorial. It includes 3 GC-[EI+]-HRMS files from seminal plasma samples, the RECETOX Metabolome HR-[EI+]-MS library collected from mostly endogoenous compounds from MetaSci Human Metabolite Library, reference alkanes, sample metadata table, and preprocessed XCMS object.</p>
Raw data for the Article "Cyclopentadienone Iron Complex-Catalyzed Hydrogenation of Ketones: An Operando Spectrometric Study Using Pressurized Sample Infusion-Electrospray Ionization-Mass Spectrometry"
<p>This data set contains the raw data (NMR, LC-MS, ESI-MS, HRMS, Elemental Analysis) for the article "Cyclopentadienone Iron Complex-Catalyzed Hydrogenation of Ketones: An <em>Operando</em> Spectrometric Study Using Pressurized Sample Infusion-Electrospray Ionization-Mass Spectrometry" published in <em>Organometallics</em>, DOI:</p> <p><a href="https://doi.org/10.1021/acs.organomet.2c00341">https://doi.org/10.1021/acs.organomet.2c00341</a></p> <p>The compound names correspond to the ones used in the article and its supporting information.</p>
Training dataset: Generation of a spectral library from HEK-Ecoli Spike-in mass spectrometry data
<p>The five raw files serve as a concise but meaningful training data set in the Galaxy training network (https://galaxyproject.github.io/training-material/).</p> <p>HEK and E.coli cell pellets were lysed with 5 % SDS, 50 mM triethylammonium bicarbonate (TEAB), pH 7.55. The obtained protein extracts were reduced by adding f.c. 5 mM TCEP and alkylated by the addition of f.c. 10 mM iodacetamide. Protein digestion and purification was performed on S-Trap columns. To ensure protein binding to the S-Trap columns, samples were acidified to a final concentration of 1.2 % phosphoric acid (~ pH 2). Six times the sample volume S-Trap buffer (90% aqueous methanol containing a final concentration of 100 mM TEAB, pH 7.1) was added to the samples which were then loaded on the columns and washed with S-Trap buffer. Protein digestion was performed with trypsin and LysC for one hour at 47 °C. Peptides were eluted in three steps with (1) 50 mM TEAB, (2) 0.2 % aqueous formic acid and (3) 50 % acetonitrile containing 0.2 % formic acid. Eluted peptides of HEK and E.coli were mixed in the following ratios (amount in µg):</p> <p>Sample HEK E.coli MS method<br> Sample1 2.5 0.00 DDA<br> Sample2 2.5 0.05 DDA<br> Sample3 2.5 0.15 DDA<br> Sample4 2.5 0.40 DDA<br> Sample5 2.5 0.80 DDA</p> <p>Additionally, iRT peptides were added and 1µg of each samples was measured with a Q-Exactive Plus mass spectrometer. Besides the five raw files, we uploaded two fasta files that serve as human and ecoli protein sequence databases, an transition list for the iRT peptides as well as an experimental design for the MaxQuant search.<br> Additionally, we uploaded the Galaxy MaxQuant training result files: protein groups, peptides, mqpar, msms, evidence and PTXQC.</p>
Data for "A learned score function improves the power of mass spectrometry database search"
<div> <h1>DATA for "A learned score function improves the power of mass spectrometry database search"</h1> <br> <div>These data files are associated with the following publication:</div> <br> <div> <ul> <li>Varun Ananth, Justin Sanders, Melih Yilmaz, Sewoong Oh and William Stafford Noble. "<a title="biorXiv Preprint Link" href="https://www.biorxiv.org/content/10.1101/2024.01.26.577425v2" target="_blank" rel="noopener">A learned score function improves the power of mass spectrometry database search</a>". Bioinformatics (Proceedings of the ISMB). 2024.</li> </ul> </div> <br> <div>For the benchmarking data, we used a dataset that is publicly available on ProteomeXchange (PXD028735). The paper that introduced this dataset is:</div> <br> <div> <ul> <li>Van Puyvelde, B., Daled, S., Willems, S., Gabriels, R., Gonzalez de Peredo, A., Chaoui, K., Mouton-Barbosa, E., Bouyssié, D., Boonen, K., Hughes, C. J., Gethings, L. A., Perez-Riverol, Y., Bloomfield, N., Tate, S., Schiltz, O., Martens, L., Deforce, D., & Dhaenens, M. (2022). A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics. In Scientific Data (Vol. 9, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41597-022-01216-6</li> </ul> </div> <br> <div>More specifically, the following `.raw` files were downloaded:</div> <br> <ul> <li><code>LFQ_Orbitrap_DDA_Ecoli_01.raw</code></li> <li><code>LFQ_Orbitrap_DDA_Human_01.raw</code></li> <li><code>LFQ_Orbitrap_DDA_Yeast_01.raw</code></li> </ul> <br> <div>Those files can be accessed via FTP <a title="Link to ProteomeXchange: PXD028735" href="https://ftp.pride.ebi.ac.uk/pride/data/archive/2022/02/PXD028735/" target="_blank" rel="noopener">here</a>.</div> <br> <div>We upload here the annotated <code>.mgf</code> files created from these <code>.raw</code> files, as described in our paper.</div> <br> <div>The human, yeast, and E. coli .fasta files used in all database searches were downloaded from UniProt on 11/6/23, 4:30 PM.</div> <br> <div> <ul> <li>Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., Bye-A-Jee, H., Cukura, A., Denny, P., Dogan, T., Ebenezer, T., Fan, J., Garmiri, P., da Costa Gonzales, L. J., Hatton-Ellis, E., Hussein, A., … Zhang, J. (2022). UniProt: the Universal Protein Knowledgebase in 2023. In Nucleic Acids Research (Vol. 51, Issue D1, pp. D523–D531). Oxford University Press (OUP). https://doi.org/10.1093/nar/gkac1052</li> </ul> </div> <br> <div>We include these files here, with only minor modifications to replace `U` amino acids with `X` so that all amino acids fall into Casanovo-DB's vocabulary.</div> </div>
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>
Data and code for publication: A simple preparation protocol for shipping and storage of tissue sections for laser ablation-inductively coupled plasma-mass spectrometry imaging
<p>Data & Code release for publication:</p> <p>Rebecca Buchholz, Sebastian Krossa, Maria K Andersen, Michael Holtkamp, Michael Sperling, Uwe Karst, May-Britt Tessem, A simple preparation protocol for shipping and storage of tissue sections for laser ablation-inductively coupled plasma-mass spectrometry imaging, <em>Metallomics</em>, Volume 14, Issue 3, March 2022, mfac013, <a href="https://doi.org/10.1093/mtomcs/mfac013">https://doi.org/10.1093/mtomcs/mfac013</a></p> <p>Python code for LA ICP MS imaging data segmentation</p> <p>Code & Data also on <a href="https://github.com/sekro/la-icp-msi_segmentation">github</a></p> <p>Thresholding based segmentation of LA-ICP-MS imaging data</p> <p>Description</p> <p><a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/src/main.py">src/main.py</a> - run this to process LA ICP MS data in data folder - generates matplotlib.figures - project specific setup <a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/src/laicpms_data_handler.py">src/laicpms_data_handler.py</a> - contains object to import, handle and segment (shimadzu) raw data</p> <p>Dependencies</p> <p>Python 3.8.1 or newer</p> <p>For packages see <a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/requirements.txt">requirements.txt</a></p> <p>Data</p> <p>LA-ICP-MS imaging data of human prostate tissue of the elements Zn, Fe & P. Details on data generation & collection in <a href="https://doi.org/10.1093/mtomcs/mfac013">publication</a>. LA-ICP-MS imaging data as plain text files (comma-separated values)</p> <ul> <li>Condition 1 = fresh frozen (FF)</li> <li>Condition 2 = room temperature vacuum dried and sealed (RTV)</li> <li>Condition 3 = formalin fixed (FFix)</li> <li>Condition 4 = formalin fixed, paraffin sealed (FFPS)</li> </ul> <p>3 replicate sectioning sets named A, B, C</p> <p>File-naming: LA_Data_CISN1.csv, where I = [1, 2, 3, 4] is indicating the condition used and N = [A, B, C] is indicating the replicate set</p> <p>License</p> <p>Data</p> <p>CC-BY 4.0 - respective <a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/data/LICENSE">LICENSE</a> file in data folder</p> <p>Source code</p> <p>MIT - respective <a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/src/LICENSE">LICENSE</a> file in src folder</p>
Data for Scan-Centric, Frequency-Based Method for Characterizing Peaks from Direct Injection Fourier transform Mass Spectrometry Experiments
<p>Input and output files from the manuscript analysis titled "Scan-Centric, Frequency-Based Method for Characterizing Peaks from Direct Injection Fourier transform Mass Spectrometry Experiments"</p>
Data from: Operando Proton Transfer Reaction-Time of Flight-Mass Spectrometry of Carbon Dioxide Reduction Electrocatalysis
<p>Seven top-level folders</p> <p>GC-PTR-TOF-MS<br> - Raw data and Jupyter Notebook used for analysis of GC-PTR-TOF-MS data</p> <p>LSV-PTR-TOF-MS<br> - Raw data and Jupyter Notebook used for analysis of PTR-TOF-MS data under linear sweep voltammetry</p> <p>MSCP-PTR-TOF-MS<br> - Raw data and Jupyter Notebook used for analysis of PTR-TOF-MS data under multi-step chronopotentiometry</p> <p>PTR-TOF-MS-Calibration<br> - Raw data and Jupyter Notebook used for analysis of PTR-TOF-MS calibration data</p> <p>SEM<br> - Raw images from scanning electron microscope</p> <p>Stability<br> - Raw data of electrochemical stability</p> <p>TEM<br> - Raw images from transmission electron microscopy</p>
Mass Spectrometry Imaging Raw Data Files
<p>The enclosed ZIP file contains raw data generated using the Waters MALDI SYNAPT G2-Si High-Definition MS System. Imaging experiments were performed on fresh-frozen human carotid plaque sections and fresh-frozen rabbit aorta sections. Data were collected in both positive and negative modes for each type of tissue.</p>
Raw data for evaluation of Floral Volatile-Patterns in the Genus Narcissus using Gas Chromatography coupled Ion Mobility Spectrometry
<p>We used a commercial gaschromatography coupled ion mobility spectrometer, equipped with an integrated in-line enrichment system for fast, sensitive and automated analysis of floral volatile patterns in the genus <em>Narcissus</em>. The raw data of individual measurements are stored together with corresponding telemetry data as data matrices. The determined retention times and ion mobilities (series and columns) can be used for the identification of substances. The measured values (intensities) are used for a (semi)-quantoitative determination of individual substances.Based on these raw data, heatmaps can be generated in this way, which allow a comparison and potentially identification of floral volatiles. </p> <p>This data set is part of a proof of concept study for the use of GC-IMS in the investigation of flower volatiles.</p>
LipidQuant 1.0: Automated data processing in lipid class separation - mass spectrometry quantitative workflows
Open the record for dataset details and reuse information.
Data from: SLICE-MSI: A machine learning interface for system suitability testing of mass spectrometry imaging platforms
Open the record for dataset details and reuse information.
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>
Raw mass spectrometry data for publication "Vertebrate cellular endolysosome modulating pore-forming protein is negatively regulated by its homologue under environmental oxidative conditions"
<p><strong>Abstract</strong>: Endolysosomes are key players in cell physiology, including material exchange, immunity and environmental adaptation etc. Bacterial pore-forming toxin aerolysin-like proteins (ALPs) are widely distributed in animals and plants. βγ-CAT is a complex of an ALP (BmALP1) and a trefoil factor (BmTFF3) in the frog <em>Bombina maxima</em>. It is the first example that a secreted endogenous pore-forming protein modulates the biochemical properties of endolysosomes via pore formation in these vesicles. Here, we report the identification of BmALP3, a homologue of BmALP1 that lacks membrane pore formation capacity. Both BmALP3 and BmALP1 contain a conserved cysteine in their C-terminal regions. BmALP3 was readily oxidized to disulfide bond linked homodimer, and the homodimer could then oxidize BmALP1 via disulfide bond exchange, resulting in the dissociation of βγ-CAT subunits and elimination of its biological activity. Consistent with its behavior <em>in vitro</em>, BmALP3 senses environmental oxygen tension <em>in vivo</em>, leading to modulation of βγ-CAT activity. Interestingly, this C-terminal cysteine site is well conserved in numerous vertebrate ALPs. These findings, for the first time, uncovered the existence of a regulatory ALP (BmALP3) and its modulating action on a cell executive ALP (BmALP1) in a redox-dependent manner, which is completely different from that of bacterial toxin aerolysins.</p>
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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.