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377 results for “Mass spectrometry”
Molecular composition of dissolved organic matter in NTL-LTER lakes detected by Fourier-transform ion cyclotron resonance mass spectrometry
The composition of dissolved organic matter (DOM) varies widely in the environment due to distinct sources of the material and subsequent processing. DOM composition drives its reactivity in terms of many processes including photochemical reactions, microbial metabolism, and carbon cycling within water bodies. This study uses ultra-high resolution mass spectrometry via a Fourier-transform ion cyclotron resonance mass spectrometer (FT-ICR MS) to evaluate DOM composition at the molecular level to determine differences in DOM composition among the NTL-LTER lakes. Whole water samples were collected from the surface of each lake near the shore on August 18th and 19th in 2016 in. Ultraviolet-visible spectra were recorded as light absorbance can also give information about DOM composition. Additionally, concentrations of anions, cations, and pH were measured waters because these can all alter DOM reactivity in the environment. Both water chemistry and DOM composition vary widely among the lakes with the bogs displaying the most terrestrial-like signature in DOM and the oligotrophic lakes show more microbial-like or environmentally processed DOM.
Molecular composition of dissolved organic matter from Lake Mendota from June – November 2017, analyzed by Fourier-transform ion cyclotron resonance mass spectrometry
Dissolved organic matter (DOM) is a complex mixture of organic compounds found in all natural waters. Its composition affects its reactivity towards numerous processes. Its composition is a function of both its source (e.g., allochthonous or autochthonous) as well as the extent of environmental processing it has undergone (e.g., chemical or biological degradation). Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS) allows for the characterization of dissolved organic matter at the molecular level. The water sample was collected near the NTL-LTER research buoy on Lake Mendota. Formula assignments were made to raw mass to charge ratios detected in the mass spectrum using a custom processing script and resulting in a list of chemical formulas making up the DOM sample.
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>
DOM composition changes in Altamaha River and Sapelo Sound estuaries measured by FT-ICR mass spectrometry from September 2015 to September 2016
Dissolved organic matter (DOM) is a large and complex mixture of compounds with source inputs that differ with location, season and environmental conditions. Here, we investigated drivers of DOM composition changes in a marsh-dominated estuary off the southeastern U.S. Monthly water samples were collected at a riverine and estuarine site from September 2015 to September 2016, and bulk, optical, and molecular analyses were conducted on samples before and after dark incubations. Results showed that river discharge was the primary driver changing the DOM composition at the mouth of the Altamaha River. For discharge higher than ~ 150 m3 s-1, DOC concentrations and the terrigenous character of the DOM increased approximately linearly with river flow. For low discharge conditions, a clear signature of salt marsh-derived compounds was observed in the river. At the head of Sapelo Sound, changes in DOM composition were primarily driven by river discharge and possibly by summer algae blooms. Microbial consumption of DOC was larger during periods of high discharge at both sites, potentially due to the higher mobilization and influx of fresh material to the system. The Georgia coast was hit by Hurricane Matthew in October 2016, which resulted in a large input of carbon to the estuary. The DOC concentration was ~ 2 times higher and DOM composition was more aromatic with a stronger terrigenous signature compared to the seasonal maximum observed earlier in the year during peak river discharge conditions. This suggests that extreme events notably impact DOM quantity and quality in estuarine regions.
DOM composition in Altamaha River and Sapelo Sound estuaries measured by FT-ICR mass spectrometry in 2017 (April, July, October) and 2018 (January and October)
Extreme events such as hurricanes and tropical storms often result in large fluxes of dissolved organic carbon (DOC) to estuaries. Precipitation associated with tropical storms may be increasing in the southeastern U.S., which can potentially impact dissolved organic matter (DOM) dynamics and cycling in coastal systems. Here, DOM composition at the Altamaha River and Estuary (Georgia, U.S.A.) was investigated over multiple years capturing seasonal variations in river discharge, high precipitation events, and the passage of two hurricanes which resulted in substantial storm surges. Optical measurements of DOM indicate that the terrigenous signature in the estuary is linearly related to freshwater content and is similar after extreme events with or without a storm surge and during peak river flow. Molecular level analysis revealed significant differences, however, with a large increase of highly aromatic compounds after extreme events exceeding what would be expected by freshwater content alone. Although extreme events are often followed by increased DOC biodegradation, the terrigenous material added during those events does not appear to be more labile than the remainder of the DOM pool that was captured by ultrahigh-resolution mass spectrometry analysis. This suggests that the added terrigenous organic matter may be exported to the coastal ocean, while a fraction of the organic matter that co-varied with the terrigenous DOM may contribute to the increased biomineralization in the estuary, with implications to carbon processing in coastal areas.
Oxygen-argon dissolved gas ratios using Equilibrator Inlet Mass Spectrometry (EIMS) and triple oxygen isotopes (TOI) from NES-LTER Transect cruises, ongoing since 2018
In order to calculate net community production (NCP) rates on Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER) transect cruises, gas tracer data were collected with a continuous at-sea mass spectrometer. The ratio of O2/Ar, measured continuously from underway water, yields 8,000-15,000 rates of NCP per cruise. Discrete water samples (50 to 150 per cruise) were collected for triple oxygen isotope (TOI) analysis to estimate gross primary production (GPP) rates and ratios of NCP/GPP. Along-shelf (upstream-downstream) transects were conducted in addition to the main across-shelf transect. This data package provides two types of data tables for NES-LTER transect cruises beginning in 2018: a high-frequency continuous Equilibration Inlet Mass Spectrometer (EIMS) table, provided by year, and a low-frequency discrete triple oxygen isotope (TOI) table with all years combined. Rates calculated from these measurements are provided as separate packages, per year, in the EDI repository.
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>
Sensitivity enhancement using chemically reactive gas cluster ion beams in secondary ion mass spectrometry (SIMS)
<p>We report for the first time on significant molecular secondary ion yield increases by modifying the chemistry of a water cluster primary ion beam. This was demonstrated using 70 keV ion beams of 0.15 eV/amu. For the neutral drug Bezafibrate, secondary ion yield enhancements ×5-10 were observed when replacing the Ar carrier gas in a water gas cluster ion beam (GCIB) source with a mixture containing 12% CO2 and 2% O2 in Ar. For the cationic drug Ranitidine the ion yield enhancements using the CO2-containing carrier gas were up to ×20-50 in positive mode and ×2-4 in negative mode. The extent of molecular fragmentation was very similar from both cluster beams. We conclude that additional chemically reactive species are present in the impact zone using the (H2O/CO2)n projectile which promote the formation of secondary ions of both polarity through projectile impact-induced chemical reactions. This methodology can be applied to further extend the capabilities of high-resolution 3-dimensional mass spectral imaging using reactive GCIB-SIMS.</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>
Oral Squamous Cell Carcinoma - Mass Spectrometry Imaging
<p>The dataset was first featured in <a href="https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/pmic.201500458">Widlak, Piotr, et al. "Detection of molecular signatures of oral squamous cell carcinoma and normal epithelium–application of a novel methodology for unsupervised segmentation of imaging mass spectrometry data." <em>Proteomics</em> 16.11-12 (2016): 1613-1621</a>. For the tissue sample's biochemical preparation details, please refer to the original publication.</p> <p>The biological material was collected from five patients who underwent surgery due to Oral Squamous Cell Carcinoma (OSCC). Tissue samples contained both tumor and surrounding healthy tissue.</p> <p>Each specimen was cut into 10 µm sections in a cryostat. During the sample preparation for the MS imaging, a high-resolution optical scan of each section was captured.</p> <p>Tissue sections were subjected to peptide imaging with the use of a MALDI ToF mass spectrometer. Spectra were recorded within <em>m/z</em> range of 800-4,000. A raster width of 100 µm was applied, and 400 shots were collected from each ablation point. The obtained dataset consisted of 45,738 raw spectra with 109,568 mass channels.</p> <p>An experienced pathologist analyzed the optical scan obtained during the data acquisition process, and tissue regions were annotated. For the highest confidence of the results obtained in this work, we will focus on the two tissue samples out of the entire dataset (8,005 and 11,869 spectra), which have the highest confidence labels, as explained by the pathologist.</p> <p>The preprocessing of the spectra was conducted in MATLAB. Standard preprocessing steps were applied to the spectra. Spectra were resampled to unify the <em>m/z</em> axis across the dataset. The baseline was removed with MATLAB procedure <em>msbackadj()</em> from the Bioinformatics Toolbox. Peaks were aligned using Fast Fourier Transform-based spectral alignment. The TIC normalization ensured a similar intensity level for all spectra. Finally, a GMM approach was used to model the spectra. GMM locates the peak but also estimates the peak area instead of a raw magnitude provided by most methods. Note that the peaks in MSI spectra are right-skewed, so the neighboring GMM components resulting from that phenomenon were identified and merged to better correspond to actual chemical compounds. The resulting dataset is characterized by 3,714 GMM components corresponding to MSI spectrum peaks.</p>
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>
Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging (LD-LTP MSI) of tobacco seedlings
<p>Mass spectrometry imaging (MSI) data set in imzML format, of complete tobacco (<em>Nicotiana tabacum</em>) seedling using Laser Desorption Low-Temperature Plasma ionization. Mapping the ion that corresponds to nicotine shows accumulation in the roots and at the borders of leaves.</p> <p>The experiment is described in:</p> <p>Elucidating the Distribution of Plant Metabolites from Native Tissues with Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging, Abigail Moreno-Pedraza, Ignacio Rosas-Román, Nancy Shyrley Garcia-Rojas, Héctor Guillén-Alonso, Cesaré Ovando-Vázquez, David Díaz-Ramírez, Jessica Cuevas-Contreras, Fredd Vergara, Nayelli Marsch-Martínez, Jorge Molina-Torres, and Robert Winkler, Analytical Chemistry <strong>2019</strong> <em>91</em> (4), 2734-2743</p>
Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging (LD-LTP MSI) of San Pedro cactus
<p>Mass spectrometry imaging (MSI) data set in imzML format, obtained from San Pedro cactus (<em>Echinopsis pachanoi</em>) cross-section using Laser Desorption Low-Temperature Plasma ionization. Mapping the ion that corresponds to mescaline shows a star-like distribution of this interesting alkaloid.</p> <p>The experiment is described in:</p> <p>Elucidating the Distribution of Plant Metabolites from Native Tissues with Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging, Abigail Moreno-Pedraza, Ignacio Rosas-Román, Nancy Shyrley Garcia-Rojas, Héctor Guillén-Alonso, Cesaré Ovando-Vázquez, David Díaz-Ramírez, Jessica Cuevas-Contreras, Fredd Vergara, Nayelli Marsch-Martínez, Jorge Molina-Torres, and Robert Winkler, Analytical Chemistry <strong>2019</strong> <em>91</em> (4), 2734-2743</p> <p>DOI: 10.1021/acs.analchem.8b04406</p> <p> </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>
Version 4.2 (20230306) of the MALDI-ToF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI)
<p><em>(Version </em>20230306<em>, </em>btmsp files modified May 31, 2023, additional taxonomic information added Dec 27, 2024<em>) </em></p> <p>Version 4.2 (20230306) of the RKI MALDI-ToF mass spectra database represents the third update of the original database (version 20161027, <a href="http://doi.org/10.5281/zenodo.163517">https://doi.org/10.5281/zenodo.163517</a>). The RKI Database v.4.2 now contains a total of 11055 MALDI-ToF mass spectra from 1601 microbial strains of highly pathogenic (i.e. biosafety level 3, BSL-3) bacteria such as <em>Bacillus anthracis</em>, <em>Brucella melitensis</em>, <em>Yersinia pestis</em>, <em>Burkholderia mallei / pseudomallei</em> and <em>Francisella tularensis</em> as well as a selection of spectra of their close and distant relatives. The database can be used as a reference for the diagnosis of BSL-3 bacteria using proprietary and free software packages for MALDI-ToF MS-based microbial identification. The spectral data are provided as a zip archive (<a href="https://zenodo.org/records/14562231/files/zenodo%20db%20230306.zip?download=1&preview=1">zenodo db 230306.zip</a>) containing the original mass spectra in their native data format (Bruker Daltonics). Please refer to the pdf file (<a href="https://zenodo.org/records/14562231/files/230306-ZENODO-Metadata.pdf?download=1&preview=1">230306-ZENODO-Metadata.pdf</a>) for information on cultivation conditions, sample preparation and details of the spectra acquisition. Please do not try to print this document (>1600 pages!).</p> <p>Version 20230306 of the RKI database contains for the first time files in the <em>btmsp</em> format (e.g. <a href="https://zenodo.org/records/14562231/files/2023-May-23-Bacillus-RKI-Database-568.btmsp?download=1&preview=1">2023-May-23-Bacillus-RKI-Database-568.btmsp </a> <a href="https://zenodo.org/api/files/35e90a0c-653d-4ba4-bf93-50b2bd80d073/2023-May-23-Bacillus-RKI-Database-570.btmsp"> </a>and others). These files were generated using the MALDI Biotyper software (Bruker Daltonics) and contain a total of 1601 main spectra (msp) from the BSL-3 database in the proprietary data format of the MALDI Biotyper software. *.<em>btmsp </em>files can be imported and used for identification with this software solution. Please refer to the manufacturer's manual for details on importing <em>btmsp </em>files. Note that the btmsp file available in database version 4 is broken and cannot be imported.</p> <p>The pkf files (<a href="https://zenodo.org/records/14562231/files/230306_ZENODO_30Peaks_0.75.pkf?download=1&preview=1">230306_ZENODO_30Peaks_0.75.pkf</a>, <a href="https://zenodo.org/records/14562231/files/230306_ZENODO_45Peaks_0.75.pkf?download=1&preview=1">230306_ZENODO_45Peaks_0.75.pkf</a>) represent two versions of the MS peak list data in a Matlab compatible format. The latter data can be imported into MicrobeMS, a free Matlab-based software solution developed at the RKI. MicrobeMS can be used for the identification of microorganisms by MALDI-ToF MS and is available at <a href="https://wiki-ms.microbe-ms.com">https://wiki-ms.microbe-ms.com</a>.</p> <p>The Excel file <a href="https://zenodo.org/records/14562231/files/Taxonomy%20information%20-%20RKI%20MALDI-ToF%20MS%20database%20of%20HPB%20at%20ZENODO%20v.4.xlsx?download=1&preview=1">Taxonomy information - RKI MALDI-ToF MS database of HPB at ZENODO v.4.xlsx</a> contains additional taxonomic information such as a detailed list of bacterial MALDI-ToF mass spectra (sheet #1), overviews on the number of spectra per strain, species or bacterial genus (sheet #2), numbers of strains per species, or genus (sheet #3), etc.</p> <p>The RKI mass spectrometry database is updated regularly.</p> <p>The author would like to thank the following individuals for providing microbial strains and species or mass spectra thereof. Without their help, this work would not have been possible.</p> <ul> <li><strong>Wolfgang Beyer</strong> - University of Hohenheim, Faculty of Agricultural Sciences, Stuttgart, Germany</li> <li><strong>Guido Werner</strong> - Robert Koch-Institute, Nosocomial Pathogens and Antibiotic Resistances (FG13), Wernigerode, Germany</li> <li><strong>Alejandra Bosch</strong> - CINDEFI, CONICET-CCT La Plata, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, La Plata, Buenos Aires, Argentina</li> <li><strong>Michal Drevinek</strong> - National Institute for Nuclear, Biological and Chemical Protection, Milin, Czech Republic</li> <li><strong>Roland Grunow, Daniela Jacob, Silke Klee, Susann Dupke </strong>and <strong>Holger Scholz</strong> - Robert Koch-Institute, Highly Pathogenic Microorganisms (ZBS2), Berlin, Germany</li> <li><strong>Jörg Rau </strong>- Chemisches und Veterinäruntersuchungsamt Stuttgart, Fellbach, Germany</li> <li><strong>Jens Jacob</strong> - Robert Koch-Institute, Hospital Hygiene, Infection Prevention and Control (FG14), Berlin, Germany</li> <li><strong>Martin Mielke</strong> - Robert Koch-Institute, Department 1 - Infectious Diseases, Berlin, Germany</li> <li><strong>Monika Ehling-Schulz</strong> - Functional Microbiology, Institute of Microbiology, University of Veterinary Medicine, Vienna, Austria</li> <li><strong>Armand Paauw</strong> - Department of Medical Microbiology, CBRN protection, Universitair Medisch Centrum Utrecht, TNO, Rijswijk, The Netherlands</li> <li><strong>Herbert Tomaso</strong><strong> </strong>– Friedrich-Löffler-Institut (FLI), Federal Research Institute for Animal Health, Jena, Germany</li> <li><strong>Gabriel Karner</strong><strong> </strong>- Karner Düngerproduktion GmbH, Research & Development, Neulengbach, Austria</li> <li><strong>Rainer </strong><strong>Borriss</strong><strong> </strong>- Institute of Marine Biotechnology e.V. (IMaB), Greifswald, Germany</li> <li><strong>Le Thi Thanh Tam</strong><strong> </strong>- Division of Plant Pathology and Phyto-Immunology, Plant Protection Research Institute, Hanoi, Socialist Republic of Vietnam</li> <li><strong>Xuewen</strong><strong> Gao</strong><strong> </strong>- College of Plant Protection, Nanjing Agricultural University, Key Laboratory of Integrated Management of Crop Diseases and Pests, Nanjing, People’s Republic of China</li> </ul> <p>For a detailed description of the database see: Lasch, P., Beyer, W., Bosch, A. <em>et al.</em> A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria. <em>Sci Data</em> <strong>12</strong>, 187 (2025). <a href="https://doi.org/10.1038/s41597-025-04504-z">https://doi.org/10.1038/s41597-025-04504-z</a></p>
Tomato Classification using Mass Spectrometry-Machine Learning Technique: a Food Safety-enhancing Platform
<p>Food safety and quality assessment mechanisms are unmet needs that industries and countries have been continuously facing in recent years. Our study aimed at developing a platform using Machine Learning algorithms to analyze Mass Spectrometry data for classification of tomatoes on organic and non-organic. Tomato samples were analyzed using silica gel plates and direct-infusion electrospray-ionization mass spectrometry technique. Decision Tree algorithm was tailored for data analysis. This model achieved 92% accuracy, 94% sensitivity and 90% precision in determining to which group each fruit belonged. Potential biomarkers evidenced differences in treatment and production for each group.</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>
Identification of Southeast Asian Anopheles mosquito species with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry using a cross-correlation approach
<p>This is the dataset used in the analysis "Identification of Southeast Asian <em>Anopheles </em>mosquito species with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry using a cross-correlation approach". It consists in 3584 raw mass spectra (mzXML file format) of the head of 359 <em>Anopheles </em>mosquito specimens collected in Karen (Kayin state) in Myanmar between 2020 and 2022 and associated metadata (Rdata file format) including sample information (taxonomy.Rdata) and spectra information (metadata.Rdata).</p>
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>
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.