15013 results for "cancer studies using rna-seq"

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• BLUEPRINT EpiVar ChIP-seq for naive CD4+ T-cells

  BLUEPRINT EpiVar ChIP-seq for naive CD4+ T-cells

  Study EGAS00001000753

• The effects of bioinformatics preprocessing on cell-free DNA analysis

  The dataset contain 20 lung cancer and 20 healthy control cfDNA samples from plasma sequenced using 150bp PE sequencing.

  Dataset EGAD50000000213

• Dataset of Study assessing the efficacy and safety of durvalumab plus olaparib plus fulvestrant in selected metastatic or locally advanced ER-positive, HER2-negative breast cancer patients.

  This dataset is made of 166 samples extracted from 1) FFPE blocks of the most recent biopsy (metastasis when applicable or primary tumor) of the breast tumor 2) Plasma samples and blood samples at baseline, at Day 1 Cycle2, Day 1 Cycle 3 and at progression or treatment discontinuation. Target sequencing has been perfomed using GREAT_A_v3 panel

  Dataset EGAD50000001665

• 3D chromatin analysis of clear cell renal cell carcinoma using micro-C

  To investigate the 3D structure of chromatin in ccRCC, we performed micro-C on 3 ccRCC cell lines (786-O, A498, UMRC-2), and 1 normal kidney epithelium cell line (HK-2), cultured under normoxic (20% O2) and hypoxic (0.05% O2) conditions

  Study EGAS50000001323

• Colon Cancer Organoid Cultures and Tumors Whole Exome Sequencing Data

  Five hundred fifty nanograms of genomic DNA were input for library preparation after fragmentation by Covaris S2, following the KAPA Hyper Prep Kit (KR0961-V1.14) protocols, with selection for a library size range of 250-450 bp. Three hundred nanograms per library DNA each from 12 samples were normalized and combined into a single pool for exome capture using the xGen Lockdown Probes and Reagents based on their standard protocols

  Dataset EGAD00001005754

• RRBS analysis to characterize the epigenomic conservation between species in the context of Aging and Cancer.

  This dataset contains Raw Reduced Representation DNA bisulfite-sequencing data obtained from human brain samples corresponding to 3 Young and 3 Old individuals (aging context), and 3 normal and 3 Glioblastoma samples (tumor context). RRBS libraries were prepared at Diagenode SA and samples were sequenced using the Illumina Novaseq6000 sequencing platform.The accompanying samples from this study (mouse tissues) are located at the ENA database under the accession number PRJEB41460.

  Dataset EGAD00001006650

• Genome-wide 5-mC profiling in plasma of metastatic ALK-rearranged non-small cell lung cancer (NSCLC) patients receiving tyrosine kinase inhibitor therapy.

  Longitudinal plasma samples (n = 79) of 21 ALK-positive NSCLC patients and 13 healthy donors were collected alongside 15 ALK-positive tumor tissue and 10 healthy lung tissue specimens. All plasma and tissue samples were analyzed by cell-free DNA methylation immunoprecipitation sequencing to generate genome-wide 5-mC profiles. Paired cfMeDIP on NextSeq 550 using KAPA Hyper Prep Kit was done.

  Dataset EGAD00001009412

• Genetic vulnerability of knockout cancer lines (2019-04-01)

  Genome-wide CRISPR/Cas9 library screen was performed in isogenic cell lines. Three biological replicates were used. Cells were harvested at initiation of screen and at specific time points following cell culture. Using this approach we aim to identify genes specifically important in cell survival in engineered cell lines. Please perform 72, instead of 36, PCR reactions as the screen performed at 200x coverage. . This dataset contains all the data available for this study on 2019-04-01.

  Dataset EGAD00001004881

• Whole exome data from PMID27216186

  This dataset comprises complete exome data from from the study PMID27216186 (Harbst & Lauss et al, Cancer Research 2016). These data are from 49 samples (tumor and matched normal) from 8 patients representing multi-region sequencing of human melanoma. Files are in the BAM format and contain aligned and processed data used for e.g. somatic variant calling. The sequencing libraries were constructed using SureSelect target enrichment with Clinical Research Exome Panel (Agilent) and sequenced on a HiSeq2500 (Illumina).

  Dataset EGAD00001008149

• SF11964 snATAC Seq Low Grade Astrocytoma IDHR132H mutant

  Single Nuclei ATAC Seq IDHR132H mutant Astrocytoma . Male, 64. Single Nuclei ATAC seq data from low grade human glioma sample. NovaSeq6000 was used for ATAC seq. The files uploaded are bam files created with grch38 reference.

  Dataset EGAD00001005414

• Veterans Administration (VA) Million Veteran Program (MVP) Summary Results from Omics Studies

  MVP is an ongoing prospective cohort study and mega-biobank in the Department of Veterans Affairs Healthcare System designed to study genetic influences on health and disease among veterans.

  Study phs001672

• Exome sequencing of patients with rare neurological disorders

  Exome sequencing of patients and their families with diverse rare neurological disorders. Some families have prior linkage data identifying a specific chromosomal interval or interest, other families do not have linkage data available. Many of these families come from special populations whose demography or preference for consanguineous marriages make them particularly tractable for genetic studies.

  Dataset EGAD00001000346

• Reduced Representation Bisulfite Sequencing (RRBS) in various tissues to discover DNA methylation markers for the diagnosis of breast and ovarian cancer.

  Despite a myriad of attempts in the last three decades to diagnose ovarian cancer (OC) earlier, this clinical aim still remains a significant challenge. In addition, monitoring treatment and early detection of fatal breast cancer (BC) remains a major unmet need. Aberrant methylation patterns of linked CpGs analyzed in DNA fragments shed by cancers into the bloodstream (i.e. cell-free DNA) can provide highly specific signals indicating cancer presence.In order to discover the most relevant CpG regions, we used RRBS and analysed several tissue samples: 11 prospectively collected invasive epithelial ovarian cancer samples (high grade serous n=8, low grade serous n=1, endometrioid n=1, mucinous n=1, mean age = 54.7 years), 8 prospectively collected invasive ductal breast cancer samples (2/8 triple negative; mean age = 56.6 years), one benign tumor (papillary serous cystadenoma, age = 86 years), 18 non-neoplastic tissue samples (breast n=7 and adnexal n=11, mean age = 60.2 years), two non-neoplastic endometrial tissues (mean age = 68 years) and twenty three white blood cell samples (mean age = 57.8 years) were assessed by RRBS. Genome wide methylation analysis was performed by Reduced Representation Bisulfite Sequencing (RRBS) at GATC Biotech. DNA was digested with MspI followed by size selection of the library, providing enhanced coverage for the CpG-rich regions. The digested DNA was adapter ligated, bisulfite modified and PCR amplified. The libraries were sequenced on Illumina’s HiSeq 2500. Analysis of the first samples sequenced with 100bp paired-end mode showed that the library insert size was small. Therefore, the remaining samples were sequenced with 50 bp paired-end mode. Using Genedata Expressionist® for Genomic Profiling v9.1, we established a bioinformatics pipeline for the detection of cancer specific differentially methylated regions (DMRs). The most promising DMRs were taken forward for the development and validation of serum based clinical assays.

  Study EGAS00001002609

• Dataset of scRNAseq from 56 CRC, treatment-naive and post-chemotherapy

  The dataset includes FASTQ source files of scRNA-seq for 56 CRC patients, sequenced with Illumina 10x platform. All patients were diagnosed with colorectal cancer between 2014 and 2022 at the Gustave Roussy Institute. Cohort includes 32 samples from untreated patients and 24 samples from patients who received chemotherapy. Chemotherapy included FOLFOX or FOLFIRI as first line, in some cases in combination with anti-VEGF or anti-EGRF drugs (avastin, bevacizumab or cetuximab) as the second and third lines. The post-treatment cohort included 18 responders (partial response, stable disease and complete response) and 6 progressors (progressive disease).

  Dataset EGAD50000001218

• Reference epigenome SMC01_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC01_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003845

• Reference epigenome SMC07_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC07_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003851

• Reference epigenome SMC03_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC03_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003847

• Reference epigenome SMC05_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC05_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003849

• Reference epigenome SMC06_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC06_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003850

• Reference epigenome SMC08_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC08_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003852

• Reference epigenome SMC04_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC04_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003848

• Reference epigenome SMC02_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC02_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003846

• Reference epigenome SMC09_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC09_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003853

• STAG2-ChIP-Seq of STAG2-mutated and cohesin-wildtype adult AMLs

  ChIP-Seq targeting the cohesin subunit STAG2 in STAG2-mutant or cohesin wildtype adult AMLs.

  Dataset EGAD00001011205

• Impact of Respiratory Virus Infections and Bacterial Microbiome Shifts on Lymphocyte and Respiratory Function in Infants Born Prematurely or Full Term

  Public health importance: Babies born preterm, approximately 1 out of every 9 live births in the United States, have significant respiratory morbidity over the first two years of life, exacerbated by respiratory viral infections. Many (<50%) return to pediatricians, emergency rooms and pulmonologists with symptoms of respiratory dysfunction (SRD): intermittent or chronic wheezing, poor growth and an excess of upper and lower respiratory tract infections (LRTI). SRD correlate inversely with gestational age and weight at birth and is more common in those with chronic lung disease of prematurity, yet its incidence and severity varies widely among both the prematurely born and those born at term. There is evidence from clinical studies and animal models that risks of LRTI and recurrent wheezing is influenced by gut and respiratory flora and by T cell responses to infection. Information gained from this study will be used to identify characteristics, risk factors and potential mechanisms for early and persistent lung disease in children born at term and born preterm. This Clinical Research Study will investigate the relationships between sequential respiratory viral infections, patterns of intestinal and respiratory bacterial colonization, and adaptive cellular immune phenotypes which are associated with increased susceptibility to respiratory infections and long term respiratory morbidity in preterm and full term infants. We hypothesize that the timing and acquisition of specific viral infections and bacterial species are directly related to respiratory morbidity in the first year of life as defined by SRD and by measures of pulmonary function. We hypothesize that cellular and molecular immuno-maturity are altered due to factors presented by premature birth in such a way as to promote chronic inflammatory and cytotoxic damage to the lung, with subsequent enhanced, damaging responses to infectious agents and environmental irritants. Our preliminary studies demonstrate both feasibility and expertise in mutiparameter immunophenotyping of small volume peripheral blood samples obtained from premature infants including gene expression arrays of flow cytometry sorted cells. We will use new technologies for known viral identification, as well as high-throughput metagenome sequencing of RNA and DNA virus like particles (VLP) to be used for viral discovery in infant respiratory sample and use of high-throughput pyrosequencing (454T) of bacterial 16S rRNA to determine shifts in bacterial community structure, occurring in pre-term (PT) as compared to full term (FT) infants, over the first year of life. Finally, we present statistical approaches to stratify disease risk predictors using multivariate logistic regression modeling approaches. We propose to evaluate T cell phenotypic and functional profiles relative to viral and predominant bacterial exposures according to highly complementary, but independent, Specific Objectives. Objective 1: To determine if viral respiratory infections and patterns of respiratory and gut bacterial community structure (microbiome) in prematurely born babies predict the rate and degree of immunologic maturation, and pulmonary dysfunction, measured from birth to 36 weeks corrected gestational age (CGA). Objective 2: To determine the relationship between respiratory viral infections and disease severity up to one year CGA, and the lymphocyte (Lc) phenotypes documented at term gestation (birth for term infants and 36 wks/NICU discharge in preterm infants) and at one year CGA. Three secondary outcomes of this objective will be to a) relate the quantity, type and severity of viral infections with pulmonary function at one and three years of life, b) relate the viral community structure to severity of viral infections and c) to seek evidence of modulation of viral susceptibility by bacterial respiratory and gut community structure (microbiome). The relationship of colonization with known and non-identified bacterial species in both the respiratory tract and the gut will be evaluated. Flow cytometry data corresponding to this study can be found within Immport study SDY1302. Positive and negative controls for microbiome samples are uploaded under SRA bioproject PRJNA474485. Microbiome samples corresponding to PRISM2 are distinguished from PRISM1 via "_PRISM2" appended to the sample name. Within the positive and negative controls, PRISM1 controls are uploaded as bam files and PRISM2 controls are uploaded as paired fastq. Samples ending in -08 correspond to TLDA qPCR results for a given sample. There is a column for each pathogen tested and a column to indicate where that pathogen was bacteria or virus.

  Study phs001347