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22 results for “glycome”
Genome-wide association summary statistics for human blood plasma glycome
<p>The dataset contains results of genome-wide association study of human blood plasma glycome. The 113 files contain association summary statistics for 113 glycome traits, of which 36 were directly measured by UPLC technology and 77 were derived glycome traits. Description of each glycome trait can be found in the <strong>Additional notes</strong> section. This dataset is also available for graphical exploration in the genomic context at <a href="http://gwasarchive.org">http://gwasarchive.org</a>. </p> <p>The data are provided on an "AS-IS" basis, without warranty of any type, expressed or implied, including but not limited to any warranty as to their performance, merchantability, or fitness for any particular purpose. If investigators use these data, any and all consequences are entirely their responsibility. By downloading and using these data, you agree that you will cite the appropriate publication in any communications or publications arising directly or indirectly from these data; for utilisation of data available prior to publication, you agree to respect the requested responsibilities of resource users under 2003 Fort Lauderdale principles; you agree that you will never attempt to identify any participant. This research has been conducted using the UK Biobank Resource and the use of the data is guided by the principles formulated by the UK Biobank.</p> <p><strong>When using downloaded data, please cite corresponding paper and this repository:</strong></p> <ol> <li>Sharapov, S. Z., Tsepilov, Y. A., Klaric, L., Mangino, M., Thareja, G., Shadrina, A. S., … Aulchenko, Y. (2019). Defining the genetic control of human blood plasma N-glycome using genome-wide association study. <em>Human Molecular Genetics</em>. http://doi.org/10.1093/hmg/ddz054</li> <li>Sodbo Sharapov, Yakov Tsepilov, Lucija Klaric, Massimo Mangino, Gaurav Thareja, Mirna Simurina, Concetta Dagostino, Julia Dmitrieva, Marija Vilaj, FranoVuckovic, Tamara Pavic, Jerko Stambuk, Irena Trbojevic-Akmacic, Jasminka Kristic, Jelena Simunovic, Ana Momcilovic, Harry Campbell, Malcolm Dunlop, Susan Farrington, Maria Pucic-Bakovic, Christian Gieger, Massimo Allegri, Edouard Louis, Michel Georges, Karsten Suhre, Tim Spector, Frances MK Williams, Gordan Lauc, Yurii Aulchenko. (2018). Genome-wide association summary statistics for human blood plasma glycome (Version 1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1298406</li> </ol> <p><strong>Funding</strong></p> <p>This work was supported by the European Community’s Seventh Framework Programme funded project PainOmics (Grant agreement # 602736) and by the European Structural and Investments funding for the "Croatian National Centre of Research Excellence in Personalized Healthcare" (contract #KK.01.1.1.01.0010).</p> <p>The work of SSh was supported by the Russian Ministry of Science and Education under the 5-100 Excellence Programme.</p> <p>The work of YT was supported by the Federal Agency of Scientific Organizations via the Institute of Cytology and Genetics (project #0324-2018-0017).</p> <p>Karsten Suhre and Gaurav Thareja are supported by ‘Biomedical Research Program’ funds at Weill Cornell Medicine - Qatar, a program funded by the Qatar Foundation. We thank all staff at Weill Cornell Medicine - Qatar and Hamad Medical Corporation, and especially all study participants who made the QMDiab study possible.</p> <p>The SOCCS study was supported by grants from Cancer Research UK (C348/A3758, C348/A8896, C348/ A18927); Scottish Government Chief Scientist Office (K/OPR/2/2/D333, CZB/4/94); Medical Research Council (G0000657-53203, MR/K018647/1); Centre Grant from CORE as part of the Digestive Cancer Campaign (<a href="http://www.corecharity.org.uk">http://www.corecharity.org.uk</a>).</p> <p>TwinsUK is funded by the Wellcome Trust, Medical Research Council, European Union, the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London.</p> <p><strong>Column headers:</strong></p> <ol> <li>SNP: SNP rsID</li> <li>CHR: chromosome</li> <li>POS: position (GRCh37 build) </li> <li>OTHER_ALLELE: reference allele (coded as "0")</li> <li>EFFECT_ALLELE: effective allele (coded as "1")</li> <li>EAF: effective allele frequency </li> <li>N: sample size</li> <li>BETA: effect size of effective allele</li> <li>SE: standard error of effect size</li> <li>PVAL: P-value of association (without GC correction)</li> <li>IMPUTATION: imputation quality</li> </ol>
Glycomics measurements of the retrospective study of the Pain-Omics project
<p>This deposit contains the QCed glycomics data for the patients in the retrospective study of the Pain-Omics FP7 project.</p>
Datasets used in: "Correcting for sparsity and interdependence in glycomics by accounting for glycan biosynthesis"
<p>Datasets included in: Bokan Bao+, Benjamin P. Kellman+, Austin W. T. Chiang, Austin K. York, Mahmoud A. Mohammad, Morey W. Haymond, Lars Bode, and Nathan E. Lewis. 2019. “<strong>Correcting for Sparsity and Non-Independence in Glycomic Data through a System Biology Framework.</strong>” bioRxiv. <a href="https://doi.org/10.1101/693507">https://doi.org/10.1101/693507</a></p> <p><strong>Central Datasets</strong><br> - Github_Yang2019_EPO<br> - paper_hmo<br> These are the HMO and EPO datasets used throughout the majority of the manuscript. They are formatted consistent with the github code repository: https://github.com/LewisLabUCSD/GlyCompare</p> <p><strong>Additional Datasets</strong><br> - Webapp_Jin2017_Mucin<br> - Webapp_Riley2019_SiteSpecN<br> - Webapp_Sibile2016_Glycolipid<br> These are additional datasets explored in the final figure and supplement of the manuscript formatted for the webapp: https://glycompare.herokuapp.com/</p> <p>All datasets but Riley2019 have structural data, Riley2019 only contains compositional data</p> <p><strong>Detailed Descriptions</strong></p> <ul> <li>Github_Yang2019_EPO <ul> <li>Sixteen MALDI-TOF glycoprofiles of EPO, where each EPO glycoprofile was produced in a different glycoengineered</li> </ul> </li> <li>CHO cell line- paper_hmo <ul> <li>Forty-eight HPLC glycoprofiles of HMO from six mothers22.</li> </ul> </li> <li>Webapp_Jin2017_Mucin <ul> <li>Mucin-type O-glycans from tumor and normal samples from gastrointestinal cancers</li> </ul> </li> <li>Webapp_Riley2019_SiteSpecN <ul> <li>Site-specific N-glycosylation in mouse brain</li> </ul> </li> <li>Webapp_Sibile2016_Glycolipid <ul> <li>Glycolipid abundance in Rat eye, brain and blood<br> </li> </ul> </li> </ul>
Proteomic and N-glycomic comparison of synthetic and bovine whey proteins and their effect on human gut microbiomes in vitro
Open the record for dataset details and reuse information.
Single-cell glycomics analysis by CyTOF-Lec reveals glycan features defining cells differentially susceptible to HIV
Open the record for dataset details and reuse information.
Assessing Response to Neoadjuvant Chemotherapy With HD PET/CT, Serum Glycomic Markers and Tissue Muc-1 Isoforms
ClinicalTrials.gov study NCT01110174. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Targeting Glycomes Sensitizes Ovarian Tumors to Immune Checkpoint Blockade
GEO Series GSE244012. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
N-Glycome regulation limits the transdifferentiation of endothelial cells into hematopoietic stem cells
GEO Series GSE135246. Danio rerio. 2 samples. Type: Expression profiling by high throughput sequencing.
Single-cell glycome and transcriptome profiling uncovers the glycan signature of each cell subpopulation of human iPSC-derived neurons
GEO Series GSE304173. Homo sapiens. 3 samples. Type: Expression profiling by high throughput sequencing.
Serum Glycomics as Prognostic and Diagnostic Biomarkers of Disease Recurrence in Liver Transplant Recipients With Hepatocellular Carcinoma
ClinicalTrials.gov study NCT05866783. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Acute Effects of a Urate-lowering Bacterial Therapeutic on Small Intestinal Transcriptomics and Glycomics in Healthy Subjects
ClinicalTrials.gov study NCT07366749. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Serum GLYcomics to Predict Graft Loss and Mortality After Liver Transplantation
ClinicalTrials.gov study NCT05866796. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Registry of Coronavirus Complications - CORRELATION WITH GLYCOMIC PROFILE in COVID-19 Patients
ClinicalTrials.gov study NCT04788433. IPD Sharing: NO. Countries: 1. Publications: 0.
PSA Glycomics Assay for Early Detection of Prostate Cancer
ClinicalTrials.gov study NCT04393376. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
scGR-seq: simulatenous profiling of glycomic and transcriptomic in single cell [scRNA-Seq]
GEO Series GSE218310. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing; Other.
Glycomic gene comparison between human epidermal melanocytes and human metastatic melanoma cells
GEO Series GSE94837. Homo sapiens. 6 samples. Type: Expression profiling by array.
A Focused Microarray Approach to Functional Glycomics: Transcriptional Regulation of the Glycome
GEO Series GSE2781. Mus musculus; Homo sapiens. 27 samples. Type: Expression profiling by array.
A defined glycosylation regulatory network modulates total glycome dynamics during pluripotency state transition
GEO Series GSE161278. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Glycome profiling and immunohistochemistry uncover changes in cell walls of Arabidopsis thaliana roots during spaceflight: Advanced Plant EXperiment (APEX) 03-1 Raw Dataset
This study was conducted to uncover the underlying molecular mechanisms by which microgravity impacts cell wall architecture in the model plant Arabidopsis thaliana using the Vegetable Production System (Veggie) housed on the International Space Station (ISS). Based on our previous space flight studies using the Biological Research in Canisters (BRIC) hardware on the STS-131 Space Shuttle mission, we found that plants respond to microgravity in large part through the transcriptional reprogramming of genes that control cell wall remodeling. This follow-up spaceflight experiment called the Advanced Plant EXperiments (APEX 03-1) in Space was designed to use Veggie unit on the ISS and on the ground unit on the Space Station Processing Facility (SSPF) to probe deeper into spaceflight-induced cell wall associated transcript expression changes. In this study we used RNA-Seq in combination with glycome profiling (glycomics) to correlate spaceflight-induced gene expression changes with actual cell wall modifications within seedlings.
Biological Research in Canisters-16 (BRIC-16): Transcriptomics, Glycomics, and Morphometric photography.
These investigations studied the fundamentals of how plants perceive gravity and develop in microgravity. It informs how gene regulation is altered by spaceflight conditions. We noted expression changes in genes involved in hypoxia and heat shock responses, DNA repair, and cell wall structure between spaceflight samples compared to the ground controls. In addition, glycome profiling supported our expression analyses in that there was a difference in cell wall components between ground control and spaceflight-grown plants. Comparing our studies to those of the other BRIC-16 experiments demonstrated that, even with the same hardware and similar biological materials, differences in results in gene expression were found among these spaceflight experiments.
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