Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
226
datasets available to search
ShareScore release 0.7.1
Dataset results
226 results for “proteomics data”
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>
R script and data files for Oakley et al (2017) Journal of Proteome Research. DOI: 10.1021/acs.jproteome.6b00797
<p>This R script and data replicates the analysis of Oakley et al (2017) Thermal shock induces host proteostasis disruption and endoplasmic reticulum stress in the model symbiotic Cnidarian <em>Aiptasia</em>. <em>Journal of Proteome Research</em>. 16:2121-2134. DOI: 10.1021/acs.jproteome.6b00797. </p>
A Bioconductor workflow for processing, evaluating and interpreting expression proteomics data
<p>Files for users of the workflow "A Bioconductor workflow for processing, evaluating and interpreting expression proteomics data". Files include Proteome Discoverer (v2.5) processing and consensus workflows for both TMT and LFQ expression proteomics data. Also provided are the output .txt files of a corresponding Proteome Discoverer identification search, as required for users to follow the workflow themselves. For raw data please refer to PRIDE. Appendix is provided as a PDF.</p>
Supplementary Data for Stabilizing the Proteomes of Acute Myeloid Leukemia Cells: Implications for Cancer Proteomics
<p>Supplementary data for: <br>Stabilizing the Proteomes of Acute Myeloid Leukemia Cells: Implications for Cancer Proteomics<br>Authors: Robert Sprung, Qiang Zhang, Michael H. Kramer, Matthew C. Christopher, Petra Erdmann-Gilmore, Yiling Mi, James P. Malone, Timothy J. Ley, and R. Reid Townsend.</p><p>Table S1 - AML Case descriptors and LC-MS data files<br>Table S2 - All Peptides by Case -LFQ<br>Table S3 - Identification of tryptic and non-tryptic peptides from five AML cases with high and low expression of ELANE<br>Table S4 - Number of proteins identified by LFQ proteomics with a minimum of 2 tryptic peptides<br>Table S5 - DFP Adduct Database Search Tryptic Peptides<br>Table S6 - Protein quantification from TMT 11-plex tryptic peptides with and without DFP<br>Table S7 - Tryptic peptides used for protein quantification from TMT 11-plex with and without DFP<br>Table S8 - Changes in TMT relative abund. with DFP treatment<br>Table S9 - Protein quantification from LFQ tryptic peptides with and without DFP<br>Table S10 - Proteins with significant change in abundance with DFP treatment using Label-Free Quantitation</p>
The Q-TOF proteomics data for the identification of mammalian L-fucose dehydrogenase (EC 1.1.1.122)
<p>The enclosed zip file contains data files (RAW format) from MS^E experiment. The experiment was performed with the use of Acquity nanoUPLC coupled with a Synapt G2 HDMS Q-TOF mass spectrometer (Waters) fitted with a nanospray source. It aimed at the identification of proteins present in the gel bands S1-S9 and the gel band Z1. The bands have come from SDS-PAGE and zymography analyses, respectively, of the most active enzyme fraction from the Reactive Red Agarose 120 purification step.</p>
Code to generate figures 3 and 4 of: "A comprehensive LFQ benchmark dataset to validate data analysis pipelines on modern day acquisition strategies in proteomics."
<p>Code to generate figures 3 and 4 of the manuscript titled "A comprehensive LFQ benchmark dataset to validate data analysis pipelines on modern day acquisition strategies in proteomics."</p> <p> </p>
Proteomics data of mitochondrial fraction of CRL-2097 cancer cell line model
<p>The cancer cell line model developed using human dermal fibroblasts CRL-2097 was used in these experiments:</p> <p>Sample 1 - CRL2097 + hTERT</p> <p>Sample 2 - CRL2097 + hTERT + LT</p> <p>Sample 2 - CRL2097 + hTERT + LT + Ras</p> <p>The mitochondrial fraction was prepared from each of these cell lines and analysed via mass spec for their proteomics. The experiment was done in duplicates. </p>
Source data to publication "Benchmarking of Analysis Strategies for Data-Independent Acquisition Proteomics Using a Large-Scale Dataset Comprising Inter-Patient Heterogeneity"
<p>Source data to publication "Benchmarking of Analysis Strategies for Data-Independent Acquisition Proteomics Using a Large-Scale Dataset Comprising Inter-Patient Heterogeneity".</p> <p>Data and further information at GitHub repository https://github.com/kreutz-lab/dia-benchmarking (DOI: 10.5281/zenodo.6371925)</p>
Combined network file for "FAVA: High-quality functional association networks inferred from scRNA-seq and proteomics data"
<p><strong>Combined network from scRNA-seq and proteomics data</strong></p> <p>Given the complementary nature of the networks based on scRNA-seq and proteomics data individually, we decided to combine them into a single network. As the Pearson Correlation Coefficient scores from FAVA cannot be assumed to be directly comparable across the two networks, we converted them to probabilistic scores based on the KEGG benchmarks. These calibrated scores were then combined to produce a single network based on scRNA-seq as well as proteomics data. As should be expected, this network outperforms the individual networks, combining the best aspects of both.</p>
Proteomic data sets after selecting mitochondrial proteins from the scaffold software for Ingenuity Pathway analysis (IPA Qiagen)
<p>List of fold change proteomic data sets of dFCM- 39 vs. 12Day and105 vs. 12Day, cFCM- 40 vs. 12Day and115 vs. 12Day , mouse heart 90 vs. 1 day after selecting mitochondrial proteins from the scaffold software for Ingenuity Pathway Analysis (IPA Qiagen)</p>
Proteomic data set of the analysis of black poplar (Populus nigra L.) seed storability
<p>Proteomic data set containing protein identification parameters (ESI MS/MS) and GO annotation functional classification (UniProt and QuickGO). Identification parameters of differentially abundant proteins of black poplar (<em>Populus nigra</em> L.) seeds stored in different temperature (3, -3, -20 and -196°C) and time (12 and 24 months) conditions. Proteins were extracted and separated according to their isoelectric point (pI) and mass using 2-dimensional electrophoresis. Proteins that varied in abundance for temperature and time of storage were identified by mass spectrometry (ESI MS/MS). The mascot search algorithm (http://www.matrixscience.com) was used for protein identification against the NCBInr (http://www.ncbi.nig.gov) databases.Identified proteins were grouped due to biological process, molecular function and subcellular localization according to the gene ontology (GO) annotation using UniProt database and QuickGO search (https://www.ebi.ac.uk/QuickGO/).</p>
UniSpec: Deep Learning for Predicting the Full Range of Peptide Fragment Ion Series to Enhance the Proteomics Data Analysis Workflow
<p>UniSpec is a comprehensive DL spectrum predictor that can predict the intensity of the entire HCD MS/MS fragment ion series, going beyond existing tools limited to b/y ion series. </p> <p>All datasets developed for UniSpec model are shared on Zenodo as part of the UniSpec publication, "UniSpec: Deep Learning for Predicting Comprehensive Peptide Fragment Ion Series to Improve Peptide-Spectrum Matches from Shotgun Proteomics Experiments".</p> <p>This includes UniSpec datasets, downstream evaluation and analysis, and application case studies.</p> <p>1. pre-processed training, evaluation and testing data for machine learning;</p> <p> UniSpec-Datasets.7z, Readme_UniSpecDatasets.txt</p> <p>2. Streamlined input datasets based on the fragmentation dictionary;</p> <p> Streamlined_inputdatasets.7z, Readme_Streamlined_inputdatasets.txt</p> <p>3. Predictions on the validation and test sets;</p> <p> UniSpecPred_Validation-Test.7z, Readme_Predictons_ValidationTest.txt</p> <p>4. Evaluation by comparison with Prosit;</p> <p> a. Predictions: prosit_and_unispec_predictions.7z, Readme_prosit_and_unispec_predictions.txt</p> <p> b. Cosine similarity scores: prosit_vs_unispec_CS.7z, Readme_prosit_vs_unispec_CS.txt</p> <p>5. CSS for Different HCD Fragment Ion Series;</p> <p> CS_for_ion_splits.tsv</p> <p>6. Application 1: PSM rescoring;</p> <p> PSM rescoring_zipfiles.7z, PSM rescoring_readme.txt</p> <p>7. Application 2: In-silico spectral library search </p> <p> in-silico_librarysearch.7z, in-silico_librarysearch_readme.txt</p> <p> </p>
Proteome Data for A. thaliana
<p>Datafile with calculated metrics and associated data for A. thaliana proteome</p>
Proteome Data for S. cerevisiae
<p>Datafile with calculated metrics and associated data for S. cerevisiae proteome</p>
Proteomic data (SWATH-MS) of mouse uterine horns treated with different types of plasma
<p>This dataset contains the proteomic data (SWATH-MS) from 48 mouse uterine horns corresponding to a murine model of Asherman'Syndrome (presence of intrauterine adhesions). These 48 uterine horns correspond to 26 NOD-SCID mice (mouse uterus are bicornuate - 2 uterine horns per mouse) distributed in 4 groups (n = 6 /group), attending to the treatment received: Control (n = 6; milliQ H2O was injected), non-activated umbilical cord plasma (n = 6), activated umbilical cord plasma (n = 6), and activated platelet-rich plasma from adult blood (n = 6). To simulate Asherman's Syndrome, we induced endometrial damage (using a needle) inside the lumen of left uterine horns from all animals, while right horns were left undamaged.</p>
Proteomic data (LC-MS/MS) of human plasma samples
<p>LC-MS/MS analysis of 8 different samples of plasma: 4 samples correspond to the activated platelet-rich plasma (PRP) fractions from 4 different patients with infertility due to Asherman's syndrome and/or endometrial atrophy; 2 samples correspond to the activated and not-activated, respectively, PRP fractions from a control fertile patient; 2 samples correspond to the activated and not-activated, respectively, fractions from a commercial umbilical cord plasma.</p>
Proteomics and metabolomics data associated with the end-of-life phenotype Smurf
<p>Data obtained from whole bodies of mated females of genotype Drs-GFP at 20 and 40 days for proteomics and 30 days for metabolomics.</p> <p>The data are cited in the following preprint :</p> <p>Smurfness-based two-phase model of ageing helps deconvolve the ageing transcriptional signature</p> <p>Flaminia Zane, Hayet Bouzid, Sofia Sosa Marmol, <a href="http://orcid.org/0000-0003-2448-4022"> View ORCID Profile</a>Savandara Besse, Julia Lisa Molina, <a href="http://orcid.org/0000-0002-9579-5250"> View ORCID Profile</a>Céline Cansell, Fanny Aprahamian, <a href="http://orcid.org/0000-0001-6356-1006"> View ORCID Profile</a>Sylvère Durand, Jessica Ayache, <a href="http://orcid.org/0000-0001-7709-2116"> View ORCID Profile</a>Christophe Antoniewski, <a href="http://orcid.org/0000-0002-6574-6511"> View ORCID Profile</a>Michael Rera</p> <p>doi: https://doi.org/10.1101/2022.11.22.517330</p>
pSCoPE: Prioritized Single-Cell Proteomics (data for generating publication figures)
<p>Major aims of single-cell proteomics include increasing the consistency, sensitivity, and depth of protein quantification, especially for proteins and modifications of biological interest. To simultaneously advance all these aims, we developed prioritized Single Cell ProtEomics (pSCoPE). pSCoPE consistently analyzes thousands of prioritized peptides across all single cells (thus increasing data completeness) while analyzing identifiable peptides at full duty-cycle, thus increasing proteome depth. These strategies increased the sensitivity, data completeness, and proteome coverage over 2-fold. The gains enabled quantifying protein variation in untreated and lipopolysaccharide-treated primary macrophages. Within each condition, proteins covaried within functional sets, including phagosome maturation and proton transport. This protein covariation within a treatment condition was similar across the treatment conditions and coupled to phenotypic variability in endocytic activity. pSCoPE also enabled quantifying proteolytic products, suggesting a gradient of cathepsin activities within a treatment condition. pSCoPE is freely available and widely applicable, especially for analyzing proteins of interest without sacrificing proteome coverage. Support for pSCoPE is available at: <a href="http://scp.slavovlab.net/pSCoPE">scp.slavovlab.net/pSCoPE</a></p> <p> </p> <p>The files contained in this .zip directory are necessary for replicating the analysis and figures associated with the pSCoPE manuscript.</p> <p> </p>
Data for manuscript, "An optimized workflow for MS-based quantitative proteomics of challenging clinical bronchoalveolar lavage fluid (BALF) samples"
<p>Clinical BALF samples are rich in biomolecules, including proteins, and useful for molecular studies of lung health and disease. However, MS based proteomic analysis of BALF is impeded by the dynamic range of protein abundance, and potential for interfering contaminants. We have developed a workflow that eliminates these challenges. By combining high abundance protein depletion, protein trapping, clean-up, and in-situ tryptic digestion, our workflow is compatible with both qualitative and quantitative MS-based proteomic analysis. The workflow includes collection of endogenous peptides for peptidomic analysis of BALF, if desired, as well as amenability to offline semi-preparative or microscale fractionation of peptide mixtures prior to LC-MS/MS analysis, for increased depth of analysis. We show the effectiveness of this workflow on BALF samples from COPD patients. Overall, our workflow should allow MS-based proteomics to be applied to a wide variety of studies focused on BALF clinical samples. </p> <p>Note: Due to the nature of some of the files, file <em>wendt005_ostr0103_18260_20210831_BALF_FAIMS_MS2_TMT16.msf, wendt005_ostr0103_18976_20230202_quantReport.msf, cmsptc_higgi022_18988_20230203_18976DW_EnF_hcdlT_1R.raw, cmsptc_higgi022_18988_20230203_18976DW_EnF_hcdlT_2R.raw, cmsptc_higgi022_18988_20230203_18976DW_EnF_hcdlT_3R.raw and cmsptc_higgi022_18988_20230203_18976DW_Eclipse_noFAIMS_quantReport.msf</em> were zipped into compressed folders before uploading.</p>
UniProt Human Proteome Benchmarking data for "aaHash: recursive amino acid hashing"
<p>aaHash is a rolling hash algorithm tailed for amino acids. Here, we provide the human proteome benchmarking data used in the aaHash paper "aaHash: recursive amino acid sequence hashing".</p>
ScienceDex guides
Understand access before you commit
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.