91c15f6d-f88e-452e-a58a-77b8a92c1540 - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001007814 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:Although genome-wide association study is an important tool for linking genetic variation to common complex diseases, it remains difficult to identify the causal variants underlying susceptibility loci. Recent work demonstrated mapping chromatin accessibility across different individuals can be a powerful method for interpreting genetic variant function, existing assays to measure chromatin landscapes (e.g., DNaseI-seq) are labor intensive and require very large numbers of cells preventing their application in real-world settings. This project aims to assess the ability of a novel assay for chromatin accessibility, ATAC-seq, to detect chromatin structure variation among individuals. We will apply ATAC-seq in 24 GBR HapMap lymphoblastoid cell lines (LCLs), which are a model system for studying the function of human genetic variation. We will also employ a novel approach to molecular quantitative trait locus identification which utilizes both population quantitative trait locus and allele-specific signatures, and gives increased mapping power in small sample sizes.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 (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/
2015-07-28 | E-ERAD-418 | biostudies-arrayexpress
Project description:Several ovarian cancer tumors from different anatomical sites and therapy statuses. A GUI to explore the data can be found at: https://visium-shiny-9hzfl.ondigitalocean.app/ Code to run the shiny app (same output as the GUI): https://github.com/kwells4/visium_public_shiny Code to replicate the analysis can be found: https://github.com/kwells4/visium_ovarian_cancer
2022-10-10 | GSE213699 | GEO
Project description:Expression quantitative trait locus (QTL) mapping of 127 adult femoral bone samples from a Red Junglefowl x White Leghorn advanced intercross line. Bone trait QTL have been mapped in the same cross, allowing a genetical genomics approach to quantitative trait gene identification
2015-10-31 | E-MTAB-3141 | biostudies-arrayexpress
Project description:Multiple myeloma (MM) is characterized by recurrent chromosomal translocations. The multiple myeloma SET domain (MMSET), identified by its fusion to the IgH locus in t(4;14) MM, is universally overexpressed and has been suggested to play an important role in tumorigenicity in t(4;14) MM. In order to identify downstream functional targets of MMSET, we knocked down MMSET expression with shRNAs in KMS11, a t(4;14) MM cell line, and identified differentially expressed genes by gene expression microarray analysis.
2014-07-16 | GSE59424 | GEO
Project description:We present a machine-learning method to prioritize the cell types most responsive to biological perturbations within high-dimensional single-cell data. We validate our method, Augur (https://github.com/neurorestore/Augur), on a compendium of single-cell RNA-seq, chromatin accessibility, and imaging transcriptomics datasets. We apply Augur to expose the neural circuits that enable walking after paralysis in response to spinal cord neurostimulation.
2020-06-05 | GSE142245 | GEO
Project description:To investigate whether large mitochondrial DNA (mtDNA) deletions can be quantified and detected using the high-throughput bioinformatics tool Splice-Break (https://github.com/brookehjelm/Splice-Break2). This dataset contains 8 human middle temporal gyrus (MTG) samples (from 4 patients) that underwent spatial transcriptomic RNA-Sequencing following 10x Genomics visium protocols as well as 2 samples (from spatial transcriptomics patient 144107/144108) that underwent bulk RNA-Sequencing (one with a ribosomal depletion library preparation and one without).
2023-10-16 | GSE226663 | GEO
Project description:This dataset contains paired-end RNA sequencing data of blood CD34+ cells from random blood donors (155 paired-end FastQ files).
The data were used to perform expression quantitative trait locus (eQTL) analysis.
| EGAD00001008194 | EGA
Project description:We systematically benchmarked 8 single-cell ATAC sequencing technologies for their capacity to generate high-quality single-cell open chromatin profiles, and to elucidate the regulatory landscape of complex samples. Our study contains 47 individual human PBMC scATAC-seq experiments from a reference male and female donor. To streamline data analysis, we devised PUMATAC (https://github.com/aertslab/PUMATAC), a flexible and universal data analysis pipeline and best practices repository for scATAC-seq. Systematic technology-specific differences in sequencing library complexity and biases in tagmentation specificity were found to impact the accuracy of cell type annotation, genotype demultiplexing, peak calling, differential region accessibility, and motif enrichment. Together, our data forms a new scATAC-seq reference of more than 169, 000 PBMC cells with matched single-cell multiome and RNA-seq data.
2023-05-22 | GSE194028 | GEO
Project description:PURPOSE. The deregulation of CCND1, CCND2 and CCND3 genes represents a common event in multiple myeloma (MM). The recently proposed TC classification grouped MM patients into five classes on the basis of their cyclins D expression profiles and the presence of the main translocations involving the immunoglobulin heavy-chain locus (IGH) at 14q32. In this study, we provide a molecular characterization of the identified TC groups. MATERIALS AND METHODS. The gene expression profiles of purified plasma cells from 50 MM cases were used to stratify the samples into the five TC classes and identify their transcriptional fingerprints. The cyclin D expression data were validated by means of real-time quantitative PCR analysis; fluorescence in-situ hybridization was used to investigate the cyclin D loci arrangements, and to detect the main IGH translocations and the chromosome 13q deletion. RESULTS. Class-prediction analysis identified 112 probe sets as characterizing the TC1, TC2, TC4 and TC5 groups, whereas the TC3 samples showed heterogeneous phenotypes and no marker genes. The TC2 group, which showed extra copies of the CCND1 locus and no IGH translocations or the chromosome 13q deletion, was characterized by the overexpression of genes involved in protein biosynthesis at translational level. A meta-analysis of published datasets validated the identified gene expression signatures. CONCLUSIONS. Our data contribute to the understanding of the molecular and biological features of distinct MM subtypes. The identification of a distinctive gene expression pattern in TC2 patients may improve risk stratification and indicate novel therapeutic targets. Experiment Overall Design: This series of microarray experiments contains the gene expression profiles of purified plasma cells (PCs) obtained from 50 newly diagnosed multiple myeloma (MM). PCs were purified from bone marrow specimens, after red blood cell lysis with 0.86% ammonium chloride, using CD138 immunomagnetic microbeads. The purity of the positively selected PCs was assessed by morphology and flow cytometry and was > 90% in all cases. 5 micrograms of total RNA was processed and hybridized to the Affymetrix HG-U133A chip following the manufacturer's instructions.
2007-10-27 | E-GEOD-2912 | biostudies-arrayexpress
Project description:Recent genome-wide association studies have identified single nucleotide polymorphisms (SNPs) at 7 loci influencing multiple myeloma (MM) risk. We generated expression quantitative trait loci (eQTL) data on malignant plasma cells from two patient series (totaling 848) to fine map the risk loci associations and gain insight into their functional basis. At 7p11.2 the strongest association with MM risk is provided by rs4487645, which is associated with allele–specific cis-regulation of the MYC-interacting gene CDCA7L (P=4.1x10-25). These data are compatible with increased expression of CDCA7L being the functional basis of the 7p11.2 association exerting its effects through an extended pathway involving IRF4 and MYC. Note: This dataset re-uses files from ArrayExpress accessions E-TABM-937, E-MTAB-362, E-MTAB-372.
2014-12-09 | E-MTAB-2299 | biostudies-arrayexpress