UPLC N-IgG glycans
Supplementary materials for "Glycosylation of immunoglobulin G is regulated by a large network of genes pleiotropic with inflammatory diseases". DOI: 10.1126/sciadv.aax0301
View on Edinburgh Research Explorer.
Calculate Your Scores
This app allows for the secure upload of blood methylation data and quickly calculates estimates or scores for a variety of human traits. These traits include age, body mass index and smoking behaviour.
Example input and output files are available at: https://doi.org/10.5281/zenodo.7086723.
Instructions for use and background information are available at: https://www.ed.ac.uk/centre-genomic-medicine/research-groups/marioni-group/methyldetectr.
MethylDetectR
Users can upload methylation-derived estimates or scores for human traits. Users can select individuals to view how their scores compare against other individuals in their dataset. Users can also upload optional case/control data for binary traits of interest and see how methylation-based scores for traits vary across case and control groups.
Example input and output files are available at https://doi.org/10.5281/zenodo.7086723.
Instructions for use and background information are available at: https://www.ed.ac.uk/centre-genomic-medicine/research-groups/marioni-group/methyldetectr.
MethylDetectR Demo
This application does not require the upload of data. It has in-built data for over 9,000 individuals who are members of the Generation Scotland study (https://www.ed.ac.uk/generation-scotland). Users can use this demo version to see how scores vary according to cases vs. controls for a number of binary traits as well as by different age ranges and sex.
Single-Cell Transcriptomics Uncovers Zonation of Function in the Mesenchyme during Liver Fibrosis
This app provides an open-access gene browser that allows assessment of mesenchymal cell gene expression between uninjured and fibrotic (6 weeks CCL4) mouse liver.
Dobie et al. Cell Reports (2019). DOI - 10.1016/j.celrep.2019.10.024
Resolving the fibrotic niche of human liver cirrhosis at single-cell level
This app provides an open-access gene browser that allows assessment of non-parenchymal cell gene expression between healthy and cirrhotic human livers.
Ramachandran et al. Nature (2019). DOI - 10.1038/s41586-019-1631-3
SteatoSITE Gene Explorer
This app allows users to visualise the RNA-seq data in SteatoSITE, a retrospective multicentre national cohort of 940 patients across the complete non-alcoholic fatty liver disease spectrum. SteatoSITE integrates quantitative digital pathology, hepatic bulk RNA-seq and 5.67 million days of longitudinal electronic health record follow-up into a secure, searchable platform to accelerate new biomedical discoveries in NAFLD.
Further information and to request access to the full SteatoSITE resource, please visit https://steatosite.com/.
Stator: From higher-order gene interactions to cell states
Stator takes in scRNA-seq count matrix, estimates higher-order gene interactions and defines cell states. This app allows users to perform downstream analyses for cell states.
Further information and request access to Stator Nextflow pipeline, please contact: ava.khamseh@ed.ac.uk
Generation Scotland Data Explorer
Generation Scotland (GS) is a biobank with rich genetic, sample, questionnaire and health record linkage data for 20,000+ Scottish participants.
The Explorer App has been designed for researchers to browse the available GS datasets.
Find out more about using the Generation Scotland data here: https://genscot.ed.ac.uk/for-researchers/access or email genscot@ed.ac.uk for further information.
Epigenetic Clocks and incident disease associations in Generation Scotland
This app displays associations between 14 epigenetic clocks and 175 incident disease outcomes in Generation Scotland. The disease outcomes were ascertained via data linkage to primary and secondary care health records over the first 10 years after the blood draw for DNA methylation. Epigenetic clock data were pre-adjusted for estimated white blood cell proportions and family relatedness. Cox PH and logistic regression models were run for each clock - disease pairing, adjusting for age, sex, alcohol, BMI, deprivation, smoking and education. The first panel of the App displays the Cox PH results. The second panel displays the difference in area under the curve (AUC) for logistic regression models with covariates and with/out an epigenetic clock predictor variable. All results are fully displayed in the Supplementary Material of Mavrommatis et al. "An unbiased comparison of 14 epigenetic clocks in relation to 10-year onset of 175 disease outcomes in 18,859 individuals." doi: TBC