15075 results for "cancer studies using rna-seq"

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• BLUEPRINT: WGBS-seq of B cells

  Whole Genome Bisulfite-seq of four B cell samples

  Dataset EGAD00001000710

• release_2: ICGC PedBrain: whole exome sequencing and Target-Seq

  release_2: ICGC PedBrain: whole exome sequencing and Target-Seq

  Dataset EGAD00001001624

• Epigenetic profiles of neuroblastoma PDXs

  This dataset contains bam files for ChIP-seq experiments for 6 neuroblastoma PDXs (Patient Derived Xenograft). It includes the bam files for the H3K27ac mark as well as the bam files of the corresponding input DNA for each sample.

  Dataset EGAD00001003394

• Single-cell ATAC-Seq (Chromium Next GEM Single Cell ATAC kit v1.1 from 10X Genomics )

  Human data for chromatin accessibility (scATAC-Seq) in CD34+ B cell precursors.

  Dataset EGAD00001010910

• Analysis_of_somatic_mutations_in_normal_blood__AML_and_MDS_samples

  Targeted pulldown of genes known to be recurrently mutated in AML & MDS from patient and normal samples using Agilent Sureselect and for some cases also using Illumina Truseq technology.

  Study EGAS00001000525

• HipSci - Congenital Hyperinsulinia - RNA Sequencing - July 2017

  HipSci - Congenital Hyperinsulinia - RNA Sequencing - July 2017

  Dataset EGAD00001003534

• HipSci - Embryonic Stem Cells - RNA Sequencing - April 2016

  HipSci - Embryonic Stem Cells - RNA Sequencing - April 2016

  Dataset EGAD00001002000

• NICHD Genomic and Proteomic Network for Preterm Birth Research (GPN)

  We performed genome-wide association studies (GWAS) to study the Spontaneous Preterm birth (SPTB). Maternal and neonatal DNA from 743 SPTB (20 to and less than 34 weeks), and 752 controls (39 to and less than 42 weeks) who fully consented for public research usage were collected from participants in several clinical centers in Alabama, Illinois, New York, North Carolina, Rhode Island, Texas, and Utah. Cases were 1:1-matched with controls according to race/ethnicity (White, Hispanics, African Americans, and Others), maternal age (<20, 20-29, 30-39, 40+), and parity (yes or no). To determine the case-control status for each participant, a Gestational Age (GA) was determined. DNA samples were taken from blood or saliva if blood was not available. Automated DNA extractions were performed on the Qiagen M48 robot, and samples were organized in 96-well plates with barcoding and sample tracking systems. Subsequent manipulations were performed on the Biomek FX liquid handling workstation with 96-channel pipetting. DNA quantitation was performed by dye-based assay on a Molecular Devices SpectraMax Plus plate reader. Samples were genotyped using Affymetrix Genome-Wide Human SNP Array 6.0. This work was conducted at the Microarray Facility at the University of Pennsylvania. Patient data were collected through chart reviews and patient interviews, which included Demographic information (parental age, race, ethnicity, etc.); Medical history (pre-pregnancy weight, height, medications, STD history, medical conditions, etc.); Social history (marital status, years of education, alcohol use and tobacco use); Obstetrical history including outcome of all prior pregnancies and dates of termination; History of preterm labor or premature rupture of membranes symptoms, evaluation or hospitalization for those symptoms in current pregnancy; Medications taken during current pregnancy; History of cervical evaluation (manually or ultrasonographically) or fetal fibronectin measurements; Pregnancy complications; Labor (type, indications, membrane status, and induction method); Delivery type and indications for cesarean section; Neonatal outcome (sex, weight, length, Apgar score, neonatal complications, admission to intensive care unit, etc.); and Maternal family history of SPTB.

  Study phs000714

• Northwestern NUgene Project: Type 2 Diabetes

  The ability to correlate genetic variation with disease susceptibility and response to drug therapy depends on genotype or sequence analysis of large numbers of richly characterized DNA samples. Eight years agoWe are a part of NHGRI's electronic Medical Records and Genomics (eMERGE) Network, whose goal is to conduct genome-wide association studies in thousands of individuals using EMR-derived phenotypes and DNA from linked biorepositories. For eMERGE, Northwestern University (NU) is studying type 2 diabetes as a phenotype. In addition, in order to explore race differences in the prevalence of type 2 diabetes, NU collaborated with Vanderbilt University to study a mix of both Caucasian and African-Americans. Northwestern University: In 2002, Northwestern committed to the development of a DNA repository to serve as a platform for the identification and validation of genotype-phenotype associations that will impact healthcare. The NUgene Project is a repository with longitudinal medical information from participating patients at affiliated hospitals and outpatient clinics from the Northwestern University Medical Center. Participants' DNA samples are coupled with data from a questionnaire (2 versions were used, 1 before and 1 after February 2006, both are included) and continuously updated data from our Electronic Medical Record (EMR) representing actual clinical care events. Northwestern has a state-of-the art, comprehensive inpatient and outpatient EMR system of over 2 million patients. NUgene has broad access to participant data for all outpatient visits as well as inpatient data via a consolidated data warehouse. NUgene participants consent to distribution and use of their coded DNA samples and data for a broad range of genetic research by third-party investigators. Vanderbilt University: BioVU, Vanderbilt's DNA databank, is an enabling resource for exploration of the relationships among genetic variation, disease susceptibility, and variable drug responses, and represents a key first step in moving the emerging sciences of genomics and pharmacogenomics from research tools to clinical practice. BioVU acquires DNA from discarded blood samples collected from routine patient care. The biobank is linked to de-identified clinical data extracted from Vanderbilt's EMR, which forms the basis for phenotype definitions used in genotype-phenotype correlations.

  Study phs000237

• NHLBI TOPMed: Lung Tissue Research Consortium (LTRC)

  Chronic obstructive pulmonary disease (COPD), a disease state characterized by airflow limitation that is not fully reversible, is the third leading cause of death in the U.S. COPD is a heterogeneous syndrome, with affected individuals demonstrating marked differences in lung structure (emphysema vs. airway disease); physiology (airflow obstruction); and other clinical features (e.g., exacerbations, co-morbid illnesses). Multiple genomic regions influencing COPD susceptibility have been identified by genome-wide association studies (GWAS), and rare coding variants can also influence risk for COPD. However, only a small percentage of the estimated heritability for COPD risk can be explained by known genetic loci. Like most complex diseases, COPD is influenced by multiple genetic determinants (each with modest individual effects). Emerging evidence supports the paradigm that complex disease genetic determinants are part of a network of interacting genes and proteins; perturbations of this network can increase disease risk. To identify this network, multiple Omics data will need to be analyzed with methods to account for nonlinear relationships and interactions between key genes and proteins. Our overall hypothesis is that integrated network analysis of genetic, transcriptomic, proteomic, and epigenetic data from biospecimens ranging from lung tissue to nasal epithelial cells to blood in highly phenotyped subjects will provide insights into COPD pathogenesis and heterogeneity. We will leverage the well-phenotyped, NHLBI-funded Lung Tissue Research Consortium (LTRC) to address these questions. We will perform multi-omics analysis in 1548 lung tissue and blood samples from the LTRC. With these multi-omics data, we will utilize a systems biology approach to understand relationships between multiple genetic determinants and multiple types of Omics data. We will begin by performing single Omics analyses in COPD vs. control lung, nasal, and blood samples. Next, we will integrate single Omics data with genetic variants identified by WGS to assist in fine mapping genetic determinants of COPD. We will then perform integrated network analysis of COPD with genetic and multiple Omics data using correlation-based, gene regulatory, and Bayesian networks. Subjects were recruited from Mayo Clinic, Universities of Colorado, Michigan, and Pittsburgh, and Temple University.

  Study phs001662