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257 results for “Proteomic analysis”
Fig. 1 in Proteomic analysis of the venom of the social wasp Apoica pallens (Hymenoptera: Vespidae)
Fig. 1. Two-dimensional gel (14%) in triplicate (three extracts from a colony), with the three gels (A, B, and C) of the Apoica pallens wasp venom.
Fig. 3. A in Differential proteomic analysis of date palm leaves infested with the red palm weevil (Coleoptera: Curculionidae)
Fig. 3. A pie chart presenting the classification of identified proteins according to their biological functions, expressed in percentage.
Fig. 1 in Differential proteomic analysis of date palm leaves infested with the red palm weevil (Coleoptera: Curculionidae)
Fig. 1. Two-dimensional differential gel electrophoresis representative images of date palm proteins. The protein sample of control, wounded, infested, and internal standard (pooled of all the samples) are individually labeled with Cy dyes, mixed together and separated by two-dimensional differential gel electrophoresis followed by image scanning. (A) image of date palm control sample and labeled with cy3 dye; (B) image of date palm artificially wounded sample labeled with cy5 dye; (C) image of date palm sample infested with red palm weevil and labeled with cy3 dye; (D) image of date palm sample pooled from all and labeled with cy2 dye; (E) overlay gel of control, infested, and wounded along with internal standard.
Fig. 2 in Differential proteomic analysis of date palm leaves infested with the red palm weevil (Coleoptera: Curculionidae)
Fig. 2. Venn diagram for the relative distribution of proteins spots in control, mechanically wounded, and red palm weevil infested date palm samples. The non-overlapping segment of diagram represent the number of proteins which were significantly up-regulated (> 1.5-fold) in the corresponding group when compared with the other two groups. The overlapping region between any two groups represents the number of protein spots significantly up-regulated (> 1.5-fold) compared to the third one. The central overlapping region depicts the protein spots where no statistically significant change in up- or down-regulation was observed.
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>
Comprehensive Single Point Mutational Landscape Analysis of the Monkeypox Virus Proteome
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Proteomic and metabolomic analysis of COVID-19 nasal swabs
<p>The epithelial barrier's primary role is to protect against entry of foreign and pathogenic elements. Global and targeted approaches were applied to nasal swabs from healthy and COVID-19-confirmed cases within 24 hours post-positive-confirmation and at 3 weeks post-infection to observe changes in proteome and metabolome.</p> <p>We found that the tryptophan/kynurenine metabolism pathway is a pinch-point regulator of canonical and non-canonical transcription activation, macrophage release of cytokines and significant changes in the immune and metabolic status with increasing severity and disease course.</p>
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>
Pan-cancer Proteomics Analysis to Identify Tumor-Enriched and Highly Expressed Cell Surface Antigens as Potential Targets for Cancer Therapeutics
<p>CPTAC PAN-cancer Data Repository</p> <p>Welcome to the CPTAC PAN-cancer Data Repository! This repository serves as a data repository for the CPTAC PAN-cancer effort, which focuses on cancer target discovery. It contains various data sets related to protein abundance estimation, derived TMT-TPA, iBAQ, iBAQ-derived copy number, and differential protein expression for CPTAC ten indications.</p> <p>## Contents</p> <p>The repository includes the following data:</p> <p>- FragPipe Output: Protein abundance estimation data generated using the FragPipe software.<br> - Derived TMT-TPA: Data derived from Tandem Mass Tag (TMT) based Total Protein Approach (TPA).<br> - iBAQ: Data representing intensity-based absolute quantification (iBAQ) of proteins.<br> - iBAQ-derived Copy Number: Data derived from iBAQ analysis for copy number estimation.<br> - Differential Protein Expression: Data indicating differential expression of proteins between tumor and NAT.</p> <p>## Data Organization</p> <p>The data in this repository is organized in a structured manner to facilitate easy access and navigation. The repository structure is as follows:</p> <p>FragPipe/<br> [fragpipe_data_files]<br> Derived_TMT_TPA/<br> [derived_tmt_tpa_data_files]<br> iBAQ/<br> [ibaq_data_files]<br> iBAQ-derived_copy_number/<br> [ibaq_copy_number_data_files]<br> Differential_protein_expression/<br> [differential_expression_data_files]</p>
Proteomic and metabolomic analysis of COVID-19 nasal swabs
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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>
Proteomic analysis of serum markers in patients maintained on Antipsychotics
<p><strong>Background:</strong> Schizophrenia (SZ) and bipolar disorder (BD) share many features: overlap in mood and psychotic symptoms, common genetic predisposition, treatment with antipsychotics (APs), and similar metabolic comorbidities. The pathophysiology of both is still not well defined, and no biomarkers can be used clinically for diagnosis and management. This study aimed to assess the plasma proteomics profile of patients with SZ and BD maintained on APs compared to those who had been off APs for six months and to healthy controls (HCs).</p> <p><strong>Methods:</strong> We analyzed the data using functional enrichment, random forest modeling to identify potential biomarkers, and multivariate regression for the associations with metabolic abnormalities. </p> <p><strong>Results:</strong> We identified several proteins known to play roles in the differentiation of the nervous system like NTRK2, CNTN1, ROBO2, and PLXNC1, which were downregulated in AP-free SZ and BD patients but were "normalized" in those on APs. Other proteins (like NCAM1 and TNFRSF17) were "normal" in AP-free patients but downregulated in patients on APs, suggesting that these changes are related to medications' effects. We found significant enrichment of proteins involved in neuronal plasticity, mainly in SZ patients on APs. Most of the proteins associated with metabolic abnormalities were more related to APs use than having SZ or BD. The biomarkers identification showed specific and sensitive results for schizophrenia, where two proteins (PRL and MRC2) produced adequate results. </p> <p><strong>Conclusions:</strong> Our results confirmed the utility of blood samples to identify protein signatures and mechanisms involved in the pathophysiology and treatment of SZ and BD.</p>
Proteomic analysis of Entamoeba histolytica in vivo assembled pre-mRNA splicing complexes
<p>Dataset for publication "Proteomic analysis of <em>Entamoeba histolytica</em> in vivo assembled pre-mRNA splicing complexes"</p>
Proteomics analysis reveals the mechanism of anemoside B4 in treating clinical mastitis in dairy cows
<p><span>(1) 背景:AB4 对 CM 奶牛的治疗效果已得到证实,但具体机制仍有待探索。本研究基于无标记相对定量蛋白质组学技术,揭示 AB4 治疗 CM 的内在机制;(2) 方法: 根据日龄、体重、泌乳年龄、产奶量和病度等因素,选取 12 头健康的中国荷斯坦奶牛(C 组)和 12 头患有 CM 的中国荷斯坦奶牛(T 组)。将 AB4 注射到 CM 30 mL/次/天的奶牛体内,持续 7 天。收集 C 组任意一天的尾静脉血,第 1 天用药前和 7 天的用药后 T 组。将 C 组 1 个时间点和 T 组 2 个时间点的血浆样品随机分为 3 组 (3 个生物学重复),每个重复取 4 份血浆样品均匀混合。它们被标记为 C1、C2、C3 和 T1-1、T1-2、T1-3、T2-1、T2-2、T2-3。筛选 AB4 治疗 CM 的差异蛋白并进行生物信息学分析;(3) 结果: AB4 可调节 HSPs、RAC1、TGFB1 等蛋白水平,参与趋化因子信号转导、白细胞跨内皮细胞迁移、MAPKs 通路等通路,改善机体炎症反应。此外,与患病和健康奶牛相比,治愈奶牛表现出显著更高的补体水平、β-防御素 12 水平以及调节脂质代谢的能力,表明 AB4 可以直接激活 C2,触发下游反应激活补体系统,同时刺激防御素分泌和调节脂质代谢。</span></p>
Proteome analysis of seven tissues of Riptortus pedestris
<p>Seven tissues, including guts, fat body, muscles, carcass, testis, salivary glands and ovaries, were collected from Riptortus pedestris. LC-MS/MS was performed by Novogene (Beijing, China)</p>
The analysis of composition and abundance of the raft proteome of microglia using a tandem mass tag (TMT)-based quantitative proteomic analysis.
<p>To determine the proteins in the membrane raft, we used the TMT-labeling and nano-liquid chromatography mass spectrometry (nano-LC-MS/MS) analysis by Creative Proteomics (NY, USA; https://www.creative-proteomics.com/). Rat primary microglia were treated with IL-6 (25 ng/ml) for 15 min. Membrane rafts were obtained by flotation assay. Samples were prepared from three independent experiments. Proteins in equal volumes of raft fractions were digested with trypsin, desalted, and labeled with a TMT reagent (Thermo Fisher Science). The TMT-labeled peptides were fractionated and analyzed by nano-LC-MS/MS. The resulting MS/MS data were analyzed and searched against the rat protein database using Proteome Discoverer 2.1.</p>
KidDO project update - quantitative proteomic and metabolomic analysis of five mouse models with chronic kidney disease
<p>Chronic kidney disease (CKD) is one of the most deadly diseases faced by patients and is a major global health and socioeconomic burden.CKD increases cardiovascular morbidity and premature mortality and decreases quality of life. Hypertension (HTN) and type 2 diabetes mellitus (T2DM), which are reaching epidemic levels, are major risk factors for CKD. CKD diagnosis and progression is based on estimated GFR (eGFR) and urinary albumin excretion. However, eGFR only has a predictive value in advanced disease and there is risk of progressive CKD in non-albuminuric individuals. Thus, there is an urgent need for new approaches for early detection of the most “at risk” individuals and identification of CKD signatures to aid in designing novel drugs and preventive measures that could ameliorate progression of CKD.</p> <p>Our overarching goal is to identify metabolites that predict kidney cell phenotypes during CKD and how crosstalk of these metabolites with the proteome drive CKD progression. We will integrate metabolomics and proteomic information from animal models of CKD with human CKD patient biopsies to identify common signatures in the tubulointerstitium that correlate with human pathophysiology.</p> <p>Here we provide quantitative proteomic and metabolomic datasets, as well as plasma and urine electrolyte measurements on five CKD mouse models.</p>
Processed results supporting MSFragger-Labile: A Flexible Method to Improve Labile PTM Analysis in Proteomics
<p>Search results supporting the manuscript "MSFragger-Labile: A Flexible Method to Improve Labile PTM Analysis in Proteomics". Processed PSM, ion, peptide, and protein tables for each search are provided, sorted by figure within the zip file. FragPipe workflows with parameters are also provided for all searches. </p>
Data and analysis of proteomic responses to hexokinase-II depletion in GAL80 and gal80Δ Saccharomyces cerevisiae with an engineered sesquiterpene-pathway
<p>Dataset 1: <a href="https://zenodo.org/api/files/ece3309f-0ca2-4773-b2e3-b1c5c839faa4/GAL80_HXK2_Vs._dhxk2p_20200324_T2_004.xlsx">GAL80_HXK2_Vs._dhxk2p_20200324_T2_004.xlsx</a></p> <p>The comparison between strain ILHA o128R+pJT9RFR (dHxk2p) and ILHA o401R+ pJT9RFR (HXK2) under the conditions with the addition of 1-Naphthaleneacetic acid and in the exponential growth phase and the ethanol growth phase. </p> <p> </p> <p>Dataset 2: <a href="https://zenodo.org/api/files/ece3309f-0ca2-4773-b2e3-b1c5c839faa4/gal80%CE%94_HXK2_Vs._dhxk2p_20200219_T1_004.xlsx">gal80Δ_HXK2_Vs._dhxk2p_20200219_T1_004.xlsx</a></p> <p>The comparison between strain ILHA NLD128-1 (dHxk2p) and ILHA NLD401 (HXK2) under the conditions with the addition of 1-Naphthaleneacetic acid and in the exponential growth phase (EXP) and the ethanol growth phase (ETH). </p> <p> </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.