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102 results for “spectrometry data”
Supplemtary Data 2 - Panga ya Saidi Averaged Spectra for Zooarchaeology by Mass Spectrometry Analysis (ZooMS)
<p>Averaged ZooMS spectra from Iron Age deposits at Panga ya Saidi, Kenya 2020.</p>
Cross-linked Mass Spectrometry (MALDI) data of GR C3 NTD and TSG101cc
<p>The zip file contains three folders from three separate datasets. The proteins were cross-linked with DST and then run on SDS-PAGE to separate unlinked protein. Bands were cut from the gels and then proteolyses overnight before conducting MS. Most of the datasets were collected using trypsin to produce protein fragments or in one case trypsin + chymotrypsin. Each folder contains a large number of control samples including: blank portions of the gel, uncross-linked protein samples, cross-linked GR without TSG101, cross-linked TSG101 without GR (four different bands because of much self-cross-linking). The files were hand curated for analysis.</p>
Systematic reanalysis of co-fractionation mass spectrometry data: predicted interactomes
<p>This upload contains predicted interactomes for 27 species or clades: 17 individual organisms with at least three published CF-MS experiments, and 9 phylogenetic groupings of those organisms. </p> <p>The following individual organisms are represented:</p> <ul> <li>Arabidopsis thaliana</li> <li>Brassica oleracea</li> <li>Caenorhabditis elegans</li> <li>Chaetomium thermophilum</li> <li>Chlamydomonas reinhardtii</li> <li>Dictyostelium discoideum</li> <li>Drosophila melanogaster</li> <li>Nematostella vectensis</li> <li>Homo sapiens</li> <li>Mus musculus</li> <li>Oryza sativa</li> <li>Plasmodium berghei</li> <li>Plasmodium falciparum</li> <li>Plasmodium knowlesi</li> <li>Strongylocentrotus purpuratus</li> <li>Triticum aestivum</li> <li>Trypanosoma brucei</li> <li>Xenopus laevis</li> </ul> <p>The following clades are also represented:</p> <ul> <li>BOP clade</li> <li>Deuterostomia</li> <li>Eucharontoglires</li> <li>Eukaryota</li> <li>Mesangiospermae</li> <li>Opiskothonta</li> <li>Plasmodium</li> <li>Tetrapoda</li> <li>Viridaplantae</li> </ul> <p>The interactomes are provided in two forms. Files in the 'All interactions' directory include the complete classifier scores for every possible protein pair (sorted in descending order). Files in the '50% precision' directory include only those interactions identified above 50% precision, for convenience. </p> <p>Proteins were mapped to orthogroups using eggNOG. Maps from eggNOG orthogroups to UniProt accessions are available from https://github.com/skinnider/CF-MS-analysis/tree/master/data/resources/eggNOG. The phylogenetic tree used to group species into clades is also available from https://github.com/skinnider/CF-MS-analysis/tree/master/data/resources/TimeTree/species.nwk.</p> <p>The third and final directory, 'Human', contains the consensus CF-MS human interactome, in which proteins were merged across 46 human experiments by their gene names, rather than based on eggNOG orthogroups. The directory contains both complete classifier scores (file 'classifier-scores.tsv.gz') and the consensus CF-MS interactome, at 50% precision (file 'CF-MS-interactome.tsv').</p>
Data from: Long-lived metabolic enzymes in the crystalline lens identified by pulse-labeling of mice and mass spectrometry
<p>The lenticular fiber cells are comprised of extremely long-lived proteins while still maintaining an active biochemical state. Dysregulation of these activities has been implicated in age-related cataracts, and other lens diseases. However, the lenticular protein dynamics underlying health and disease is unclear. We sought to measure the global protein turnover rates in the eye using dietary nitrogen-15 (15N)-labeling of mice between 3 and 15 weeks of age. By performing mass spectrometry we measured the 14N- to 15N-peptide ratios of 248 lens proteins, including Crystallin, Aquaporin, Collagen and Laminin of the lens capsule, and enzymes that catalyze glycolysis as well as oxidation and reduction reactions. Unexpectedly, like the crystallin proteins, many of these enzymes are also exceedingly long-lived. The slow replacement of these enzymes in spite of young age of the mice suggests their potential roles in age-related metabolic changes in the lens.</p>
Mass spectrometry data for: A small protein coded within the mitochondrial canonical gene nd4 regulates mitochondrial bioenergetics
<p><span><strong>Background</strong>:</span> <span>Mitochondria have a central role in cellular functions, aging and in certain diseases. They possess their own genome, a vestige of their bacterial ancestor. Over the course of evolution, most of the genes of the ancestor have been lost or transferred to the nucleus. In humans, the mtDNA is a very small circular molecule with a functional repertoire limited to only 37 genes. Its extremely compact nature with genes arranged one after the other and separated by short non-coding regions suggests that there is little room for evolutionary novelties. This is radically different from bacterial genomes, which are also circular but much larger, and in which we can find genes inside other genes. These sequences, different from the reference coding sequences, are called alternative open reading frames or altORFs, and they are involved in key biological functions. </span><span>However, whether altORFs exist in mitochondrial protein-coding genes or elsewhere in the human mitogenome has not been fully addressed.</span></p> <p><span><strong>Results</strong>:</span> <span>We found a downstream alternative ATG initiation codon in the +3 reading frame of the human mitochondrial <em>nd4</em> gene. This newly characterized altORF encodes a 99-amino acids long polypeptide, MTALTND4, which is conserved in primates. Our custom antibody, but not the pre-immune serum, was able to immunoprecipitate MTALTND4 from HeLa cell lysates, confirming the existence of an endogenous MTALTND4 peptide. The protein is localized in mitochondria and cytoplasm and is also found in the plasma, </span><span>and it impacts cell and mitochondrial physiology. </span></p> <p><span><strong>Conclusions</strong>:</span> <span>Many human-mitochondrial-translated ORFs might have so far gone unnoticed. By ignoring mtaltORFs, we have underestimated the coding potential of the mitogenome.</span> <span>Alternative mitochondrial peptides such as MTALTND4 may offer </span><span>a new framework for the investigation of mitochondrial functions and diseases.</span></p>
Tandem Mass Spectrometry Data (LCMS-2) from Microalgal Co-culture of Skeletonema marinoi and Prymnesium parvum
<p>The mzML files in this dataset are the Liquid Chromatography Tandem Mass Spectrometry (LCMS-2) data files, derived from the RAW MS-2 files using GNPS file convertor. These files contain unprocessed features fragmented features from the MS-1 data files available as <10.5281/zenodo.10143127> acquired from the monocultures (single species: <em>Skeletonema marinoi</em> and <em>Prymnesium parvum</em> separately) and co-culture conditions of (<em>Skeletonema marinoi</em> and <em>Prymnesium parvum</em>). These files are used for structure annotations. </p> <p>The results of metabolomics annotation are available on Zenodo with DOI: 10.5281/zenodo.10143554</p>
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>
msiFlow: Automated Workflows for Reproducible and Scalable Multimodal Mass Spectrometry Imaging and Immunofluorescence Microscopy Data Processing and Analysis
<p>This record contains example and result data of msiFlow.</p> <p>msiFlow is a collection of automated workflows for reproducible and scalable multimodal mass spectrometry imaging (MSI) and immunofluorescence microscopy (IFM) data processing and analysis. Using an experimental mouse model for urinary tract infection, induced by uropathogenic E.coli (UPEC), we generated data by</p> <ul> <li>matrix-assisted laser desorption ionisation mass spectrometry imaging with laser-induced postionisation (MALDI-2 MSI) using the Bruker timsTOFfleX instrument</li> <li>transmission-mode MALDI-2 MSI (t-MALDI-2)</li> <li>immunofluorescence microscopy (IFM) using the MACSima system from Miltenyi </li> </ul> <p>msiFlow was tested on MALDI-2 MSI, t-MALDI-2 MSI and IFM data of control and UPEC-infected mouse bladder sections. In IFM we used Ly6G and actin for staining neutrophils and the muscle layer. We validated msiFlow on MALDI MSI data of bone marrow (BM)-derived neutrophils. Tentative lipid annotations were validated by MALDI DDA MSI and MALDI MS/MS. All data used and results generated by msiFlow are included in this dataset (besides the intermediate results of the MALDI-2 preprocessing due to data size).</p> <p>The dataset contains the following zip files:</p> <table> <tbody> <tr> <td><strong>zip file</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>ly6g_heterogeneity.zip</td> <td>example and result data (Ly6G clusters) for molecular_heterogeneity_flow</td> </tr> <tr> <td>if_segmentation.zip</td> <td>example and result data (Ly6G segmentation) for if_segmentation_flow</td> </tr> <tr> <td>ly6g_heterogeneity_signatures.zip</td> <td>example and result data (lipids for Ly6G clusters) for molecular_signatures_flow</td> </tr> <tr> <td>ly6g_molecular_signatures.zip</td> <td>example and result data (lipids for Ly6G) for molecular_signatures_flow</td> </tr> <tr> <td>msi_if_registration.zip</td> <td>example and result data for msi_if_registration_flow</td> </tr> <tr> <td>msi_segmentation.zip</td> <td>example and result data (segmented MSI bladder data) for msi_segmentation_flow</td> </tr> <tr> <td>region_group_analysis.zip</td> <td>example and result data (regulated lipids in different bladder tissue regions) for region_group_analysis_flow</td> </tr> <tr> <td>macsima.zip</td> <td>raw IFM data of UPEC-infected bladders containing Ly6G, actin and autofluorescence images</td> </tr> <tr> <td>maldi-bm-neutrophils.zip</td> <td>raw and pre-processed MALDI MSI data of BM-derived neutrophils</td> </tr> <tr> <td>t-maldi-2.zip</td> <td>raw t-MALDI-2 MSI data of a UPEC-infected bladder section</td> </tr> <tr> <td>maldi-2-<em>group-sampleno</em>.zip</td> <td>raw MALDI-2 MSI data of a control/UPEC bladder section</td> </tr> <tr> <td>MALDI_DDA_MSI.zip</td> <td>raw MALDI MSI data acquired in DDA mode</td> </tr> <tr> <td>TIMS_MS_MS.zip</td> <td>raw MALDI TIMS MS/MS data</td> </tr> </tbody> </table> <p> </p>
Estimation of sulfuric acid concentrations using ambient ion composition and concentration data obtained by ion mass spectrometry measurements (APi-TOF)
<p>The dataset has been used to estimate the sulfuric acid concentration from APi-TOF data. Using the bisulfate ion, the sulfuric acid molecule clustered with the bisulfate ion (dimer) and the trimer, we were able to determine the daytime concentration of sulfuric acid with the concentration of ambient ions, including the condensation sink, ion-ion recombination and collision rate of HSO4- with H2SO4.</p>
Mass spectrometry proteomics data obtained from analysis of the secretome of Anisakis simplex (sensu stricto) L3 larvae.
<p>Mass spectrometry proteomics data obtained from analysis of the secretome of <em>Anisakis simplex</em> (sensu stricto) L3 larvae.</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>
Data set for the figures in the manuscript "Real-Time Identification of Aerosol-Phase Carboxylic Acid Production Using Extractive Electrospray Ionization Mass Spectrometry"
Open the record for dataset details and reuse information.
Systematic discovery of subcellular RNA patterns in the gut epithelium - Mass spectrometry data
<p>This collection contains the raw data created from the mass spectrometry experiment and the spectral counts that were used in the manuscript for protein abundance comparisons.</p>
Mass Spectrometry Data
<p>Proteomics and Phosphoproteomics data from <span>DOI: 10.1016/j.cell.2024.05.025</span></p>
Data-Independent Acquisition Mass Spectrometry as a Tool for Metaproteomics: Interlaboratory Comparison Using a Model Microbiome
<p>Mass spectrometry (MS)-based metaproteomics is used to identify and quantify proteins in microbiome samples, with the frequently used methodology being Data-Dependent Acquisition mass spectrometry (DDA-MS). However, DDA-MS is limited in its ability to reproducibly identify and quantify lower abundant peptides and proteins. To address DDA-MS deficiencies, proteomics researchers have started using Data-Independent Acquisition Mass Spectrometry (DIA-MS) for reproducible detection and quantification of peptides and proteins. We sought to evaluate the reproducibility and accuracy of DIA-MS metaproteomic measurements relative to DDA-MS metaproteomic measurements using a mock community of known taxonomic composition. Artificial microbial communities of known composition were analyzed independently in three laboratories using DDA- and DIA-MS acquisition methods. DIA-MS yielded more protein and peptide identifications than DDA-MS in each laboratory. In addition, the protein and peptide identifications were more reproducible in all laboratories and provided an accurate quantification of proteins and taxonomic groups in the samples. We also identified some limitations of current DIA tools when applied to metaproteomic data highlighting specific needs to further improve DIA tools to enable analysis of metaproteomic datasets from complex microbiomes. Ultimately, DIA-MS represents a promising data collection strategy for MS-based metaproteomics due to its large number of detected proteins and peptides, reproducibility, deep sequencing capabilities, and accurate quantitation.</p>
Locality-sensitive hashing enables signal classification in high-throughput mass spectrometry raw data at scale
<p>Raw data of nanoLC-IMS-MS/MS (DDA-PASEF) from HeLa whole proteome digest. </p> <p>HeLa cells were lysed in a urea-based lysis buffer (7 M urea, 2 M thiourea, 5 mM dithiothreitol (DTT), 2% (w/v) CHAPS) assisted by sonication for 15 min at 4°C in high potency using a Bioruptor instrument (Diagenode). Proteins were digested with Trypsin using a filter-aided sample preparation (FASP) [Wisniewski <em>et al</em>., 2009] as previously detailed [Distler <em>et al</em>., 2016]. 200 ng of peptide digest were analyzed using a nanoElute UPLC coupled to a TimsTOF PRO MS (Bruker). Peptides injected directly in an Aurora 25 cm x 75 µm ID, 1.6 µm C18 column (Ionopticks) and separated using a 120 min. gradient method at 400 nL/min. Phase A consisted on water with 0.1% formic acid and phase B on acetonitrile with 0.1% formic acid. Sample was injected at 2% B, lineally increasing to 20% B at 90 min., 35% B at 105 min., 95% at 115 min. and hold at 95% until 120 min. before re-equilibrating the column at 2%B. The MS was operated in DDA-PASEF mode [Meier <em>et al</em>., 2018], scanning from 100 to 1700 m/z at the MS dimension and 0.60 to 1.60 1/k0 at the IMS dimension with a 100 ms TIMS ramp. Each 1.17 sec MS cycle comprised one MS1 and 10 MS2 PASEF ramps (frames). The source was operated at 1600 V, with dry gas at 3 L/min and 200°C, without nanoBooster gas. The instrument was operated using Compass Hystar version 5.1 and timsControl version 1.1.15 (Bruker).</p>
Data for: Oligonucleotide mapping via mass spectrometry to enable comprehensive primary structure characterization of an mRNA vaccine against SARS-CoV-2
<p>Oligonucleotide mapping via liquid chromatography mass spectrometry mass spectrometry (LC-MS/MS) was recently developed to support development of Comirnaty®, the world's first commercial mRNA vaccine which immunizes against the SARS-CoV-2 virus. Analogous to peptide mapping of therapeutic protein modalities, oligonucleotide mapping described here provides direct primary structure characterization of mRNA, through enzymatic digestion, accurate mass determinations, and optimized collisionally-induced fragmentation. Sample preparation for oligonucleotide mapping is a rapid, one-pot, one-enzyme digestion. The digest is analyzed via LC-MS/MS with an extended gradient and resulting data analysis employs semi-automated software. In a single method, oligonucleotide mapping readouts include a highly reproducible and completely annotated UV chromatogram with >98% sequence coverage and a microheterogeneity assessment of 5´ terminus capping and 3´ terminus poly(A) tail length. Oligonucleotide mapping was pivotal to ensure the quality, safety, and efficacy of mRNA vaccines by providing: confirmation of construct identity and primary structure and assessment of product comparability following manufacturing process changes. More broadly, this technique may be used to directly interrogate the primary structure of RNA molecules in general.</p>
Porcine cell-free system mass spectrometry compiled data sets
<p><span>The degradation of sperm-borne mitochondria after fertilization is a conserved event. This process known as post-fertilization sperm mitophagy, ensures exclusively maternal inheritance of the mitochondria-harbored mitochondrial DNA genome. This mitochondrial degradation is in part carried out by the ubiquitin proteasome system. In mammals, ubiquitin-binding pro-autophagic receptors such as SQSTM1 and GABARAP have also been shown to contribute to sperm mitophagy. These systems work in concert to ensure the timely degradation of the sperm-borne mitochondria after fertilization. We hypothesize that other receptors, cofactors, and substrates are involved in post-fertilization mitophagy. <span>Mass spectrometry was used in conjunction with a porcine cell-free system to identify other autophagic cofactors involved in post-fertilization sperm mitophagy. This porcine cell-free system is able to recapitulate early fertilization proteomic interactions. Altogether, 185 proteins were identified as statistically different between control and cell-free treated spermatozoa. Six of these proteins were further investigated, including MVP, PSMG2, PSMA3, FUNDC2, SAMM50, and BAG5. These proteins were phenotyped using porcine <em>in vitro </em>fertilization, cell imaging, proteomics, and the porcine cell-free system. The present data confirms the involvement of known mitophagy determinants in the regulation of mitochondrial inheritance and provides a master list of candidate mitophagy co-factors to validate in the future hypothesis-driven studies.</span></span></p>
Ground Gamma-ray Spectrometry Data of the Cerro do Jarau Impact Structure
<p>This is gamma-ray dataset that was collected during two field campaings in the Cerro do Jarau impact structure in Brazil. The Cerro do Jarau structure is a ~13.5 km diameter impact structure located in southern Brazil and formed on Cretaceous continental flood basalts of the Serra Geral Formation and underlying sedimentary strata. The available file provides the raw dataset in counts per unit of time; concentration values of K, U along with their respective histograms; and calibration crossplots for each channel.</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.