OA Functional Genomics
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00010001746 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:The aim of this work is to apply an integrated systems approach to understand the biological underpinnings of hip osteoarthritis that culminates in the need for total joint replacement (TJR). This study is a feasibility pilot that integrates functional genomics data from diseased and non-diseased tissues of OA patients who have undergone TJR. For each tissue, we characterised epigenetic marks (methylation), gene transcription (RNASeq) and expression (quantitative proteomics). We also generated genotype data on the HumanCoreExome array for each individual. This data is part of a pre-publication release.
2018-05-14 | PXD006673 | Pride
Project description:The aim of this work is to apply an integrated systems approach to understand the biological underpinnings of hip osteoarthritis that culminates in the need for total joint replacement (TJR). This study is a feasibility pilot that integrates functional genomics data from diseased and non-diseased tissues of OA patients who have undergone TJR. For each tissue, we characterised epigenetic marks (methylation), gene transcription (RNASeq) and expression (quantitative proteomics). We also generated genotype data on the HumanCoreExome array for each individual.
| EGAS00001002483 | EGA
Project description:The aim of this work is to apply an integrated systems approach to understand the biological underpinnings of knee osteoarthritis that culminates in the need for total joint replacement (TJR). This study is a feasibility pilot that integrates functional genomics data from diseased and non-diseased tissues of OA patients who have undergone TJR. For each tissue, we characterised epigenetic marks (methylation), gene transcription (RNASeq) and expression (quantitative proteomics). We also generated genotype data on the HumanCoreExome array for each individual.
| EGAS00001001899 | EGA
Project description:An overall goal of functional genomics has been to measure the impact of variants on molecular endophenotypes (e.g. gene expression levels or the degree of TF binding) and relate this to organismal traits and disease phenotypes. However, all the experiments to date have been described relative to a generic reference genome, significantly hobbling their interpretation. Here, we describe a strategy for finding significant relationships between disease variation and genomic annotation via personal functional genomics, by performing personal genome sequencing and paired functional genomics experiments, on the same individual.
2024-05-22 | PXD022787 | Pride
Project description:Functional genomics of SNRPA1/B2
| PRJNA674889 | ENA
Project description:Functional genomics of TFEB overexpression
| PRJNA1013363 | ENA
Project description:Functional genomics of human colon organoids
| PRJNA589838 | ENA
Project description:This clinical trial studies the effectiveness of a web-based cancer education tool called Helping Oncology Patients Explore Genomics (HOPE-Genomics) in improving patient knowledge of personal genomic testing results and cancer and genomics in general. HOPE-Genomics is a web-based education tool that teaches cancer/leukemia patients, and patients who may be at high-risk for developing cancer, about genomic testing and provide patients with information about their own genomic test results. The HOPE-Genomics tool may improve patient’s genomic knowledge and quality of patient-centered care. In addition, it may also improve education and care quality for future patients.
| 2378933 | ecrin-mdr-crc
Project description:Functional genomics of GxE in cardiovascular disease
| PRJNA871344 | ENA
Project description:The aim of this work is to apply an integrated systems approach to understand the biological underpinnings of large joint (hip and knee) osteoarthritis which culminates in the need for total joint replacement (TJR). We will obtain diseased and non-diseased cartilage as well as other disease-relevant tissue following TJR, coupled with a blood sample. We will generate genotype data and will characterise the pairs of diseased and non-diseased tissue samples in terms of methylation, transcription (RNASeq) and expression (quantitative proteomics). We will apply integrative approaches to combine information across the –omics levels to characterise genes, pathways, and networks that underlie osteoarthritis progression.
This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute please see http://www.sanger.ac.uk/datasharing/
| EGAS00001002322 | EGA