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226 results for “proteomics data”
Data for 'Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis'
<p>Additional data for 'Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis'</p>
Data from: Genotype-by-environment interactions influence the composition of the Drosophila seminal proteome
<p>Ejaculate proteins are key mediators of post-mating sexual selection and sexual conflict, as they can influence both male fertilization success and female reproductive physiology. However, the extent and sources of genetic variation and condition dependence of the ejaculate proteome are largely unknown. Such knowledge could reveal the targets and mechanisms of post-mating selection and inform about the relative costs and allocation of different ejaculate components, each with its own potential fitness consequences. Here, we used liquid chromatography coupled with tandem mass spectrometry to characterize the whole-ejaculate protein composition across twelve isogenic lines of Drosophila melanogaster that were reared on a high- or low-quality diet. We discovered new proteins in the transferred ejaculate and inferred their origin in the male reproductive system. We further found that the ejaculate composition was mainly determined by genotype identity and genotype-specific responses to larval diet, with no clear overall diet effect. Nutrient restriction increased proteolytic protein activity and shifted the balance between reproductive function and RNA metabolism. Our results open new avenues for exploring the intricate role of genotypes and their environment in shaping ejaculate composition, or for studying the functional dynamics and evolutionary potential of the ejaculate in its multivariate complexity.</p>
Data from: Genotype-by-environment interactions influence the composition of the Drosophila seminal proteome
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A simplified method for comprehensive capture of the Staphylococcus aureus proteome: S. aureus proteome data table
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Combining time-resolved transcriptomics and proteomics data for Adverse Outcome Pathway refinement in ecotoxicology
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Dysregulation in mTOR/HIF-1 signaling identified by proteo-transcriptomics of SARS- CoV-2 infected cells - Proteomic data obtained with Huh-7 cells
<p>Cultured human Huh-7 cells were infected with SARS-CoV-2 and harvested after 24, 48 and 72 h. The extracted proteins were processed in triplicates preparing for mass spectrometric analysis. Data acquisition was completed, including control samples of non-infected cells, following isobaric tandem mass tag (TMT) chemical labeling and on-line fractionation of the 12 combined biological replicates. The resulted vendor specific raw files (Thermo Scientific) of 12 fractions are provided.</p> <p>The data is further analyzed in order to identify regulated proteins upon SARS-CoV-2 infection to understand the underlying biological processes through pathway analysis. Additional details about the study is going to be completed in the manuscript already submitted for publication.</p>
Data from: Quantitative proteomics reveals rapid divergence in the postmating response of female reproductive tracts among sibling species
<p><span><span><span><span><span><span><span><span><span><span><span>Fertility depends, in part, on interactions between male and female reproductive proteins inside the female reproductive tract (FRT) that mediate postmating changes in female behavior, morphology, and physiology. Coevolution between interacting proteins within species may drive reproductive incompatibilities between species, yet the mechanisms underlying postmating-prezygotic isolating barriers remain poorly resolved. Here, we used quantitative proteomics in sibling <i>Drosophila</i> species to investigate the molecular composition of the FRT environment and its role in mediating species-specific postmating responses. We found that (1) FRT proteomes in <i>D. simulans</i> and<i> D. mauritiana</i> virgin females express unique combinations of secreted proteins and are enriched for distinct functional categories, (2) mating induces substantial changes to the FRT proteome in <i>D. mauritiana</i> but not in <i>D. simulans</i>, and (3) the <i>D. simulans </i>FRT proteome exhibits limited postmating changes irrespective of whether females mate with conspecific or heterospecific males, suggesting an active female role in mediating reproductive interactions. Comparisons with similar data in the closely related outgroup species <i>D. melanogaster </i>suggest that divergence is concentrated on the <i>D. simulans </i>lineage. Our study suggests that divergence in the FRT extracellular environment and postmating response contribute to previously described patterns of postmating-prezygotic isolation and the maintenance of species boundaries.</span></span></span></span></span></span></span></span></span></span></span></p>
Raoultella sp. KDF8 proteome data
<p>A comparative proteomic study was performed to identify proteins and pathways involved in diclofenac and codeine degradation steps in <em>Raoultella</em> sp. KDF8. Cultures of the <em>Raoultella</em> sp. KDF8 were grown in shaken flasks in mineral medium (BSBTE medium) supplemented with glycerol or one of pharmaceuticals. Growing cells were subjected to analysis.</p>
Data from: A universal tool for marine metazoan species identification – Towards best practices in proteomic fingerprinting
<p><span>Proteomic fingerprinting using MALDI-TOF mass spectrometry is a well-established tool for identifying microorganisms and has shown promising results for identification of animal species, particularly disease vectors and marine organisms. However, few studies have tested species identification across different orders and classes. In this study, we collected data from 1,246 specimens and 198 species to test species identification in a diverse dataset. We also evaluated different specimen preparation and data processing approaches for machine learning and developed a workflow to optimize classification using random forest. Our results showed high success rates of over 90%, but we also found that the size of the reference library affects classification error. Additionally, we demonstrated the ability of the method to differentiate marine cryptic-species complexes and to distinguish sexes within species.</span></p>
Data from: Pre- and post- treatment fold change plasma proteomics in metastatic NSCLC patients
<p>Blood plasma samples were collected from advanced-stage NSCLC patients as part of a clinical study (PROPHETIC; NCT04056247). All clinical sites received IRB approval for the study protocol. Patient blood samples were drawn at baseline (referred to as T0) and, on average, 4 weeks after the treatment commenced, prior to the second dose of treatment (referred to as T1). Blood samples were drawn into tubes containing EDTA as an anticoagulant, and plasma was separated from the whole blood. The protocol adheres to the Clinical and Laboratory Standards Institute (CLSI) guidelines.</p>
Data repository associated with 'A Functional Map of the Human Intrinsically Disordered Proteome'
<p><strong>ES_MAP.zip</strong></p> <ul> <li>a hierarchically clustered map of the human IDR-ome</li> <li>.cdt and .gtr files - outputs of Cluster3.0 software</li> <li>can be visualized using JavaTreeView (see Tutorial_ES.pdf)</li> </ul> <p><strong>TUTORIAL.zip</strong>, information on:</p> <ul> <li>visualization and analysis of the human IDR-ome map</li> <li>search for proteins of interest and exploratory analyses of clusters</li> <li>automatic export and analysis of exported clusters (code available at https://github.com/IPritisanac/ES_PW)</li> </ul> <p><strong>IDROME_SEQUENCES.zip</strong></p> <ul> <li>human proteome fasta file</li> <li>IDRome fasta file</li> <li>SPOT-Disorder v1.0 disorder boundaries <ul> <li>13 044 unique protein sequences with at least one IDR (>=30 amino acids)</li> <li>21 252 total unique human IDRs</li> </ul> </li> </ul> <p><strong>IDR_ALN.zip</strong></p> <ul> <li>alignments of IDR sequences across ENSEMBL orthologs</li> <li>19 459 IDR alignments</li> <li>UniProt ID and IDR boundaries for the human sequence are indicated in the name of the file</li> </ul> <p><strong>FAIDR_TSTATS.zip</strong></p> <ul> <li>hierarchical clustering of FAIDR t-statistics for 148 GO terms<br> <ul> <li>.cdt, .gtr files from Cluster3.0</li> <li>can be visualized using JavaTreeView</li> <li>reveals the most predictive molecular features for the top performing 148 models</li> </ul> </li> </ul> <p><strong>CLUSTERS_EXPLORE.zip</strong></p> <ul> <li>clusters obtained through exploratory analysis of the map provided in ES_MAP.zip</li> <li>93 exported clusters in .cdt file format</li> </ul> <p><strong>CLUSTERS_AUTO.zip</strong></p> <ul> <li>clusters extracted from the hierarchically clustered IDR-ome map at a range of distance thresholds (0.4 - 0.8) in .cdt file format</li> <li>distance refers to the uncentered correlation distance between vectors of Z-scores representing human IDRs</li> <li>clusters extracted at different distance thresholds are split into separate archives</li> <li>AUTO_GO_FEATS.xlsx - summary of GO-term overrepresentation and feature enrichment analyses; each distance threshold is in a separate sheet</li> </ul> <p><strong>FAIDR_HIGH_AUC_PPV_GO.zip</strong></p> <ul> <li>target files with annotations of 148 GO terms for which good quality FAIDR models could be obtained (AUC >= 0.7, PPV >= 0.4)</li> <li>file format: three columns; 1st: IDR ID (includes IDR boundaries); 2nd: protein UniProt ID; 3rd: annotation of the protein to a GO term (1 if known to be associated with the GO term, 0 if not)</li> </ul> <p> </p> <p> </p>
Processed OLINK serum proteomics data MIS-C patients versus healthy controls
<p>This dataset contains processed OLINK serum proteomics data MIS-C patients versus healthy controls. Data was generated by Diorio et al. (Diorio, C., Shraim, R., Vella, L.A. <em>et al.</em> Proteomic profiling of MIS-C patients indicates heterogeneity relating to interferon gamma dysregulation and vascular endothelial dysfunction. <em>Nat Commun</em> <strong>12</strong>, 7222 (2021). https://doi.org/10.1038/s41467-021-27544-6). Processing in format provided here was done by dr. Levi Hoste. This table is used in the MultiNicheNet package (https://github.com/saeyslab/multinichenetr) and mentioned in the updated corresponding manuscript. </p>
Data from: Combined analysis of micro RNA and proteomic profiles and interactions in patients with primary lung adenocarcinoma and lung adenocarcinoma brain metastases
<p>We carried out an analysis of miRNAs expression profiles and protein spectrums of non-metastatic primary lung adenocarcinoma (LP) and patients with brain metastases (BM) to better explore the molecular basis of BM. Files containing raw data of miRNA expression and proteomic profiles in the manuscript "Combined analysis of micro RNA and proteomic profiles and interactions in patients with primary lung adenocarcinoma and lung adenocarcinoma brain metastases".</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>
Input features and benchmark data sets for protein complex prediction and E. coli proteome application by AF2Complex
<p>Benchmark data sets of AF2Complex, input features for application to E. coli proteome, and predicted structural models of E. coli Ccm I as described in</p> <p><strong>Predicting direct physical interactions in multimeric proteins with deep learning</strong></p> <p><em>Mu Gao, Davi Nakajima An, Jerry M. Parks, Jeffrey Skolnick</em></p> <ol> <li><a href="https://zenodo.org/api/files/087ae188-6f7a-4a71-a586-bbc7bdcc6843/af2complex_bench.tar.gz">af2complex_bench.tar.gz</a>: Benchmark data sets CP17, Dimer1193 and Oligomer562, including input features for AF2Complex/AF-Multimer, both paired and unpaired MSAs, as well as sequences, experimental structures, and results presented in the AF2Complex work (~90GB de-compressed size)</li> <li><a href="https://zenodo.org/api/files/087ae188-6f7a-4a71-a586-bbc7bdcc6843/ecoli_Ccm_I.tar.gz">ecoli_Ccm_I.tar.gz</a>: Computational models of the<em> E. coli</em> Ccm I system</li> <li><a href="https://zenodo.org/api/files/087ae188-6f7a-4a71-a586-bbc7bdcc6843/ecoli_set.tar.gz">ecoli_sets.tar.gz</a>: Lists of benchmark sets of positive and negative PPIs from <em>E. coli</em></li> <li><a href="https://zenodo.org/api/files/087ae188-6f7a-4a71-a586-bbc7bdcc6843/ecoli_af_fea.tar.gz">ecoli_af_fea.tar.gz: </a>Pre-generated input features of <em>E. coli</em> proteome for protein complex prediction and modeling by AF2Complex (4,429 proteins, ~800 GB de-compressed size). This data set can be used with AF2Complex to probe the interactions of any combinations among the 4,429 proteins of E. coli.</li> </ol> <p> </p>
LC-MS raw data for proteomic elucidation of the targets and primary functions of picornavirus 2A protease
<p>This dataset contains LC-MS raw data for pulldowns from the project "Proteomic elucidation of the targets and primary functions of picornavirus 2A protease." The descriptions of the raw data files are in Summary_Table_MS_RawData.pdf.</p>
MALDI-TOF MS data: Species delimitation of Hexacorallia and Octocorallia around Iceland using nuclear and mitochondrial DNA and proteome fingerprinting
<p>Cold-water corals build up reef structures or coral gardens and play an important role for many organisms in the deep sea. Climate change, deep-sea mining, and bottom trawling are severely compromising these ecosystems, making it all the more important to document the diversity, distribution, and impacts on corals. This goes hand in hand with species identification, which is morphologically and genetically challenging for Hexa- and Octocorallia. Morphological variation and slowly evolving molecular markers both contribute to the difficulty of species identification. In this study, a fast and cheap species delimitation tool for Octocorallia and Scleractinia of the Northeast Atlantic was tested based on 49 specimens. Two nuclear markers (ITS2 and 28S rDNA) and two mitochondrial markers (COI and mtMutS) were sequenced. The sequences formed the basis of a reference library for comparison to the results of species delimitation based on proteomic analysis using the MALDI-TOF MS method. The genetic methods were able to distinguish 17 of 18 presumed species. The MALDI-TOF MS method was able to distinguish 7 species. Species that could not be distinguished from one another still achieved good signals but were not represented by enough specimens for comparison. Therefore, it is predicted that with an extensive reference library of proteome spectra for Scleractinia and Octocorallia, MALDI-TOF MS may provide a rapid and cost-effective alternative for species discrimination in corals.</p>
The proteomics data of Paris polyphylla var. yunnanensis
<p>The mass spectrometry proteomics data and the protein/peptide identifications of seed coat, mature seed and germianting seeds from<strong><em> </em></strong><em>Paris polyphylla</em> var. <em>yunnanensis.</em></p>
Supplementary code and data for: Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE
<p>This repository contains code and data to reproduce the figures in the manuscript: Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE.</p> <p>Please refer to the readme in the repository. </p>
Supplementary table of PRIDE datasets analyzed for "FAVA: High-quality functional association networks inferred from scRNA-seq and proteomics data"
<p>Our proteomics dataset comes from The PRoteomics IDEntifications (PRIDE) database, the world’s largest data repository of mass spectrometry-based proteomics data. Specifically, we used 633 human proteomics project experiments with a total of 32,546 runs and reanalyzed them using ionbot with an FDR threshold of 0.01 [16], resulting in a total of 154,885,151 peptide spectrum matches for 18,846 proteins. Here is the full list of projects, runs, and general statistics.</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.