15189 results for "cancer studies using rna-seq"

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• Reference epigenome ADMSC03 h3k9me3 ChIP-Seq data generated from KEP study

  ADMSC03 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007372

• Reference epigenome ADMSC03 input ChIP-Seq data generated from KEP study

  ADMSC03 input ChIP-Seq paired end data

  Dataset EGAD00001007373

• Reference epigenome ADMSC05 h3k27Ac ChIP-Seq data generated from KEP study

  ADMSC05 h3k27Ac ChIP-Seq paired end data

  Dataset EGAD00001007374

• Tumor-derived cell lines as pharmacogenomic models to predict therapeutic vulnerabilities in hepatocellular carcinoma

  Hepatocellular carcinoma (HCC) is a heterogeneous aggressive malignancy with low efficacy of current therapies at advanced stages. We integrated molecular and pharmacological profiling of a large panel of liver cancer cell lines (LCCL) to assess their clinical relevance as HCC preclinical models and identify new effective therapies and biomarkers of response. Here, we performed multi-omic analysis including whole-exome, RNA and microRNA sequencing in a series 34 LCCL. Molecular profiles of LCCL and primary HCC were compared and we searched for molecular features associated with drug response. Our panel of LCCL faithfully recapitulated the most aggressive molecular “proliferation class” of HCC.

  Dataset EGAD00001004938

• The National Heart, Lung, and Blood Institute (NHLBI)-funded Next Generation Genetic Association Studies (NextGen) Consortium: Phenotyping Lipid traits in iPS derived hepatocytes Study (PhLiPS Study)

  The goal of the PhLiPS study is to create a library of induced pluripotent stem cell (iPSC) lines and iPSC-derived hepatocytes of diverse genotypes for use in metabolic profiling and interrogating lipid phenotypes. These cell lines were created as a part of the Next Generation Genetic Association Studies (Next Gen) Program, which was a five-year, $80 million program to investigate functional genetic variation in humans by assessing cellular profiles that are surrogates for disease phenotypes. To achieve this, researchers from multiple institutions across the U.S. were awarded grants to derive iPSC lines from more than 1,500 individuals representing various conditions as well as healthy controls for use in functional genomic ("disease in a dish") research. This extensive panel includes a diverse set of age, gender, and ethnic backgrounds, and therefore will be an invaluable tool for evaluations across demographics. Further enhancing the utility of these cell lines are data sets such as phenotyping, GWAS, genome sequencing, gene expression and -omics analyses (e.g., lipidomic, proteomic, methylomic) that can be matched to the cell lines. The PhLiPS Study focuses on individuals free of cardiovascular disease or with lipoprotein metabolism disorders in the community served by the Hospital of the University of Pennsylvania.

  Study phs001341

• Shedding light over COVID-19 susceptibility and severity

  “One of the most remarkable features of SARS-CoV-2 infection is the variation in consequence ranging from asymptomatic to life-threatening” state the authors of the majestic Nature paper released in accelerated pre-view on July 2021. This variation in consequences represent not only an issue of burning medical relevance, but also a scientific and intellectual challenge for scientists around the globe. Researchers are known to be easily triggered by a challenge, and they recently demonstrated to be even better at double their efforts to focus on an urgent societal need. In this work led and coordinated by the COVID-19 Host Genetics Initiative (COVID-19 HGI), researchers gathered data from 49,562 cases and two million controls from 46 studies performed worldwide. The majority of the data has been deposited at the EGA, where 29 datasets have been collected under the study number EGAS00001005304. Data gathered from diverse sources conferred a huge robustness to the results. Genetic data from different ancestry origin were included in the analyses, even though data from patients with European origin was highly predominant (approx. 75%). Separated analysis of the different ancestries gave rise to relevant differences in the results, highlighting how important it is to take this aspect in consideration for future studies. Data from the individual studies was compiled into three categories according to COVID-19 severity for a meta-analysis: (a) critically ill cases (b) moderate or severe cases (c) all cases. They were paired with non-COVID-19-reported controls. All three analysis resulted in a total of 13 genetic loci associated with COVID-19 severity. Out of those, 9 loci associated specifically with critical illness, while 6 (a few overlapped) associated with moderated illness. Some genomic position matched previous findings. Interestingly, working with such an enormous amount of data allowed the depth to discriminate association with disease susceptibility vs severity, an issue of large medical relevance. Several loci mapped into biological relevant genes, like TYK2, PPP1R15A and FOXP4, previously implicated in immune response and/or lung functions. Researchers participating in the COVID-19 Host Genetics Initiative have worked in an unprecedented time-tight collaboration, prioritizing data sharing and collective results to tackle the pandemic affecting the world. The scientific community won’t miss any of the lessons to learn from this, and the EGA is ready to keep working to enable safe data sharing and fruitful collaborations.

  Blog covid-19-susceptibility-and-severity

• ICGC MMML-seq Data Freeze March 2013 transcriptome sequencing

  ICGC MMML-seq Data Freeze March 2013 transcriptome sequencing

  Dataset EGAD00001000356

• ATAC-Seq of human stimulated and cultured CD4+ Treg cells

  This dataset includes whole genome sequence data for ATAC-seq (42 samples) of human stimulated and cultured CD4+ Treg cells.

  Dataset EGAD00001004829

• Reference epigenome KNIH005 mRNA-seq data generated from KEP study

  A KNIH005 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002171

• Reference epigenome KNIH001 miRNA-seq data generated from KEP study

  A KNIH001 miRNA-seq single end data for islet cells

  Dataset EGAD00001002760