9337 results for "cancer rna-seq"

in 74.05 milliseconds.

• Reference epigenome IPS-NPC01 h3k9me3 ChIP-Seq data generated from KEP study

  IPS-NPC01 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007282

• Reference epigenome IPS-NPC01 input ChIP-Seq data generated from KEP study

  IPS-NPC01 input ChIP-Seq paired end data

  Dataset EGAD00001007283

• Reference epigenome IPS-NPC02 h3k27ac ChIP-Seq data generated from KEP study

  IPS-NPC02 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007284

• Reference epigenome IPS-NPC02 h3k27me3 ChIP-Seq data generated from KEP study

  IPS-NPC02 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007285

• Reference epigenome IPS-NPC02 h3k36me3 ChIP-Seq data generated from KEP study

  IPS-NPC02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007286

• Reference epigenome IPS-NPC02 h3k4me1 ChIP-Seq data generated from KEP study

  IPS-NPC02 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007287

• Reference epigenome IPS-NPC02 h3k9me3 ChIP-Seq data generated from KEP study

  IPS-NPC02 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007289

• Reference epigenome IPS-NPC02 input ChIP-Seq data generated from KEP study

  IPS-NPC02 input ChIP-Seq paired end data

  Dataset EGAD00001007290

• Reference epigenome IPS-ENeuron01 h3k27ac ChIP-Seq data generated from KEP study

  IPS-ENeuron01 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007291

• Reference epigenome IPS-ENeuron01 h3k27me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron01 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007292

• Reference epigenome IPS-ENeuron01 h3k36me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron01 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007293

• Reference epigenome IPS-ENeuron01 h3k4me1 ChIP-Seq data generated from KEP study

  IPS-ENeuron01 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007294

• Reference epigenome IPS-ENeuron01 h3k4me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron01 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007295

• Reference epigenome IPS-ENeuron01 h3k9me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron01 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007296

• Reference epigenome IPS-ENeuron01 input ChIP-Seq data generated from KEP study

  IPS-ENeuron01 input ChIP-Seq paired end data

  Dataset EGAD00001007297

• Reference epigenome IPS-ENeuron02 h3k27me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron02 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007299

• Reference epigenome IPS-ENeuron02 h3k36me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007300

• Reference epigenome IPS-ENeuron02 h3k4me1 ChIP-Seq data generated from KEP study

  IPS-ENeuron02 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007301

• Reference epigenome IPS-ENeuron02 h3k4me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron02 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007302

• Reference epigenome IPS-ENeuron02 h3k9me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron02 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007303

• Reference epigenome IPS-ENeuron02 input ChIP-Seq data generated from KEP study

  IPS-ENeuron02 input ChIP-Seq paired end data

  Dataset EGAD00001007304

• Reference epigenome Islet-derived-MSC03 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC03 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007424

• Reference epigenome Islet-derived-iPSC02 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC02 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007447

• Reference epigenome Pancreas-Islet06 h3k27ac ChIP-Seq data generated from KEP study

  Pancreas-Islet06 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007450

• Reference epigenome Pancreas-Islet06 h3k27me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet06 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007451

• Reference epigenome Pancreas-Islet06 h3k36me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet06 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007452

• Reference epigenome Pancreas-Islet06 h3k4me1 ChIP-Seq data generated from KEP study

  Pancreas-Islet06 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007453

• Reference epigenome Pancreas-Islet06 h3k4me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet06 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007454

• Reference epigenome Pancreas-Islet06 h3k9me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet06 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007455

• Reference epigenome Pancreas-Islet06 input ChIP-Seq data generated from KEP study

  Pancreas-Islet06 input ChIP-Seq paired end data

  Dataset EGAD00001007456

• Reference epigenome Pancreas-Islet07 h3k27ac ChIP-Seq data generated from KEP study

  Pancreas-Islet07 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007457

• Reference epigenome Pancreas-Islet07 h3k27me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet07 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007458

• Reference epigenome Pancreas-Islet07 h3k36me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet07 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007459

• Reference epigenome Pancreas-Islet07 h3k4me1 ChIP-Seq data generated from KEP study

  Pancreas-Islet07 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007460

• Reference epigenome Pancreas-Islet07 h3k4me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet07 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007461

• Reference epigenome Pancreas-Islet07 h3k9me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet07 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007462

• Reference epigenome Pancreas-Islet07 input ChIP-Seq data generated from KEP study

  Pancreas-Islet07 input ChIP-Seq paired end data

  Dataset EGAD00001007463

• Reference epigenome Pancreas-Islet08 h3k27ac ChIP-Seq data generated from KEP study

  Pancreas-Islet08 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007464

• Reference epigenome Pancreas-Islet08 h3k27me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet08 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007465

• Reference epigenome Pancreas-Islet08 h3k36me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet08 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007466

• Reference epigenome Pancreas-Islet08 h3k4me1 ChIP-Seq data generated from KEP study

  Pancreas-Islet08 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007467

• Reference epigenome Pancreas-Islet08 h3k4me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet08 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007468

• Reference epigenome Pancreas-Islet08 h3k9me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet08 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007469

• Reference epigenome Pancreas-Islet08 input ChIP-Seq data generated from KEP study

  Pancreas-Islet08 input ChIP-Seq paired end data

  Dataset EGAD00001007470

• Reference epigenome Fat-adipocyte03 h3k27ac ChIP-Seq data generated from KEP study

  Fat-adipocyte03 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007471

• Reference epigenome Fat-adipocyte03 h3k27me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte03 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007472

• Reference epigenome Fat-adipocyte03 h3k36me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte03 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007473

• Reference epigenome Fat-adipocyte03 h3k4me1 ChIP-Seq data generated from KEP study

  Fat-adipocyte03 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007474

• Reference epigenome Fat-adipocyte03 h3k4me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte03 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007475

• Reference epigenome Fat-adipocyte03 h3k9me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte03 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007476

• Reference epigenome Fat-adipocyte03 input ChIP-Seq data generated from KEP study

  Fat-adipocyte03 input ChIP-Seq paired end data

  Dataset EGAD00001007477

• Reference epigenome Fat-adipocyte04 h3k27ac ChIP-Seq data generated from KEP study

  Fat-adipocyte04 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007478

• Reference epigenome Fat-adipocyte04 h3k27me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte04 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007479

• Reference epigenome Fat-adipocyte04 h3k36me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte04 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007480

• Reference epigenome Fat-adipocyte04 h3k4me1 ChIP-Seq data generated from KEP study

  Fat-adipocyte04 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007481

• Reference epigenome Fat-adipocyte04 h3k4me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte04 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007482

• Reference epigenome Fat-adipocyte04 h3k9me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte04 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007483

• Reference epigenome Fat-adipocyte04 input ChIP-Seq data generated from KEP study

  Fat-adipocyte04 input ChIP-Seq paired end data

  Dataset EGAD00001007484

• Reference epigenome Fat-adipocyte05 h3k27ac ChIP-Seq data generated from KEP study

  Fat-adipocyte05 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007485

• Reference epigenome Fat-adipocyte05 h3k27me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte05 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007486

• Reference epigenome Fat-adipocyte05 h3k36me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte05 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007487

• Reference epigenome Fat-adipocyte05 h3k4me1 ChIP-Seq data generated from KEP study

  Fat-adipocyte05 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007488

• Reference epigenome Fat-adipocyte05 h3k4me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte05 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007489

• Reference epigenome Fat-adipocyte05 h3k9me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte05 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007490

• Reference epigenome Fat-adipocyte05 input ChIP-Seq data generated from KEP study

  Fat-adipocyte05 input ChIP-Seq paired end data

  Dataset EGAD00001007491

• release_2: ICGC PedBrain: ChIP-Seq

  release_2: ICGC PedBrain: ChIP-Seq

  Dataset EGAD00001001621

• McGill EMC Release 4 for assay "Bisulfite-seq"

  McGill EMC Release 4 for assay "Bisulfite-seq": Methylation profiling by high-throughput sequencing

  Dataset EGAD00001001289

• McGill EMC Release 4 for assay "mRNA-seq"

  McGill EMC Release 4 for assay "mRNA-seq": Transcriptome profiling by high-throughput sequencing

  Dataset EGAD00001001291

• McGill EMC Release 4 for assay "smRNA-seq"

  McGill EMC Release 4 for assay "smRNA-seq": Transcriptome profiling by high-throughput sequencing

  Dataset EGAD00001001292

• miRNA seq data of 43 cases out of dataset EGAD00001000650 (+ one read_me file)

  miRNA seq data of 43 cases out of dataset EGAD00001000650 (MMML)

  Dataset EGAD00001001619

• BLUEPRINT Bisulfite-seq and Whole Genome Sequencing of mantle cell lymphoma

  BLUEPRINT Bisulfite-seq and Whole Genome Sequencing of mantle cell lymphoma

  Dataset EGAD00001001892

• Paired-end ATAC-seq analysis of the 3D spatially mapped GBM samples.

  Dataset contains 70 paired-end ATAC-seq samples from 8 patients.

  Dataset EGAD00001010311

• Single-cell RNA-sequencing for peripheral blood mononuclear cells and plasma proteomics data from COVID-19 patients and healthy controls of Japanese

  We performed single-cell RNA-sequencing of peripheral blood mononuclear cells and measured plasma protein expression from COVID-19 patients and healthy controls.

  Study JGAS000783

• RNA sequencing of skin tissue and peripheral blood mononuclear cells from patients with atopic dermatitis and healthy controls.

  We performed RNA-sequencing of skin tissue and peripheral blood mononuclear cells obtained from patients with atopic dermatitis along with healthy controls in Japanese population.

  Study JGAS000628

• Bulk RNA-sequencing of AML blasts pre and post culture

  This dataset contains 18 Fastq files from 9 samples (pre-culture (n=3), post culture in standard (n=4) or niche-llike (n=2) conditions) from 4 patients.They correspond to bulk RNA sequencing on Illumina instrument of libraries prepared using SMARTer Universal Low Input RNA Kit.

  Dataset EGAD00001008773

• ChIP-seq data of Hodgkin lymphoma cell line L-428

  ChIP-seq data (H3K4Me3, H3K27Ac histone modifications) of Hodgkin lymphoma cell line L-428. Samples were processed as previously described (Sud et al., 2018). The files are in bam format, aligned to build 37.

  Study EGAS00001003033

• BLUEPRINT ATAC-seq data for cells in the haematopoietic lineages, from adult and cord blood samples

  BLLUEPRINT ATAC-seq data for cells in the haematopoietic lineages, from adult and cord blood samples.

  Study EGAS00001001596

• Combining transcription factor binding affinities with open chromatin data for accurate gene expression prediction

  The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid cost and labour intensive TF ChIP-seq assays.It is important to develop reliable and fast computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices.TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq, using either peaks or footprints as input.In addition to open-chromatin data, also Histone-Marks (HMs) can be used in TEPIC to identify candidate TF binding sites.TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength.Using machine learning techniques, we show that incorporating low affinity binding sites improves our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites.Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance.In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq datasets.Finally, we show that these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.

  Study EGAS00001002073

• Epithelioid sarcoma sequencing data

  This study shares DNA and RNA sequencing data from Epithelioid sarcoma patients

  Study EGAS00001005983

• Alveolar Rhabdomyosarcoma sequencing data

  This study shares DNA and RNA sequencing data from alveolar rhadbomyosarcoma patients

  Study EGAS00001005387

• Embryonal Rhabdomyosarcoma sequencing data

  This study shares DNA and RNA sequencing data from embryonal rhadbomyosarcoma patients

  Study EGAS00001005502

• Wilm's tumor sequencing data

  This study shares DNA and RNA sequencing data from Wilm's tumor patients

  Study EGAS00001005690

• Rhabdoid tumor sequencing data

  This study contains sequencing DNA and RNA sequencing data for rhabdoid tumors

  Study EGAS00001006351

• HipSci - Healthy Normals - RNA Sequencing - May 2014

  HipSci - Healthy Normals - RNA Sequencing - May 2014

  Dataset EGAD00001000897

• Dataset for "Genomic landscape of oral cancers" (Illumina RNA)

  Dataset for "Genomic landscape of oral cancers" (Illumina RNA)

  Dataset EGAD00001004366

• RNA sequencing of uveal melanoma metastases.

  RNA sequencing of uveal melanoma metastases.

  Dataset EGAD00001007912

• The Environmental Determinants of Diabetes in the Young Study (TEDDY)

  Study Overview The Environmental Determinants of Diabetes in the Young (TEDDY) Study is a longitudinal study that investigates genetic and genetic-environmental interactions, including gestational events, childhood infections, dietary exposures, and other environmental factors after birth, in relation to the development of islet autoimmunity and type 1 diabetes (T1D). A consortium of six clinical centers assembled to participate in the development and implementation of the study to identify environmental triggers for the development of islet autoimmunity and T1D in genetically susceptible individuals. Beginning in 2004, the TEDDY study screened over 400,000 newborns for high-risk HLA-DR, DQ genotypes from both the general population and families already affected by T1D. The TEDDY study enrolled around 8,676 participants across six clinical centers worldwide (Finland, Germany, Sweden and three in the United States) in the 15-year prospective follow-up. Participants are followed every three months for islet autoantibody (IA) measurements with blood sampling until four years of age and then at least every six months until the age of 15. After the age of four, autoantibody positive participants continue to be followed at three month intervals and autoantibody negative participants are followed at six-month intervals. In addition to the analysis of autoantibodies, additional data and sample collection are performed at each visit. Parents collect monthly stool samples in early childhood. The parents also fill out questionnaires at regular intervals in connection with study visits and record information about diet and health status in the child's TEDDY Book between visits. Continued long-term follow-up of the currently active TEDDY participants will provide important scientific information on early childhood diet, reported and measured infections, vaccinations, and psychosocial stressors that may contribute to the development of type 1 diabetes and islet autoimmunity. Additional information on the TEDDY study is available in the following articles: Rewers et al., 2008, PMID: 19120261 and Hagopian et al., 2006, PMID: 17130573. Details of the TEDDY protocol can be found in Hagopian et al., 2011, PMID: 21564455. TEDDY data currently available in dbGaP include: gene expression, SNPs, exome, microbiome (gut, nasal, and plasma), RNA sequencing, and whole genome sequencing. For more information on TEDDY Study version history please refer to TEDDY Study dbGaP README File. ImmunoChip SNP DNA from whole blood samples on study participants and their family members (mothers, fathers, and siblings) was obtained and used for SNP genotyping. Genotyping was performed by the Center for Public Health Genomics at the University of Virginia using the Illumina ImmunoChip SNP array, which contains around 196,000 SNPs from 186 regions associated with 12 autoimmune diseases (Hadley et al., 2015, PMID: 26010309). Data cleaning and validation included the removal of subjects with a low call rate (< 5% SNPs missing) and differences in reported sex and prior genotyping at the TEDDY HLA laboratory. Additionally, SNPs with a low call rate or Hardy-Weinberg equilibrium P value < 10-6, except for chromosome 6 due to HLA eligibility requirements, were removed from the final dataset (Törn et al., 2015, PMID: 25422107).TEDDY-T1DExome ArrayDNA from whole blood samples on study participants and their family members (mothers, fathers, and siblings) was obtained and used for genotyping. Genotyping was performed by the University of Virginia using the Illumina TEDDY-T1DExome array. The TEDDY-T1DExome array is a custom chip that contains 550,601 markers from the Infinium CoreExome-24 v1.1 BeadChip and an additional 90,214 tagSNPs specifically selected by the TEDDY investigators based on their associations with nutrients, vitamins, type 2 diabetes, autoimmune diseases, body-mass index, or other exposures and phenotypes measured by TEDDY study.The Illumina GenTrain2 algorithm was used for genotype calling. Sample quality control metrics included sample call rate, heterozygosity rate and concordance of gender between the information reported and genotyped. Gene Expression The TEDDY study collected peripheral blood for the extraction of total RNA from enrolled children starting at 3 months of age, and then at 3 month intervals up to 48 months and then biannually. Total RNA was extracted using a high throughput (96-well format) extraction protocol using magnetic (MagMax) beads technology at the TEDDY RNA Laboratory, Jinfiniti Biosciences in Augusta, GA. Purified RNA (200 ng) was further used for cRNA amplification and labeling with biotin using Target Amp cDNA synthesis kit (Epicenter catalog no. TAB1R6924). Labeled cRNA was hybridized to the Illumina HumanHT-12 Expression BeadChips based on the manufacturer's instructions. The HumanHT-12 Expression BeadChip provides coverage for more than 47,000 transcripts and known splice variants across the human transcriptome. Microbiome The TEDDY microbiome study aimed to characterize the longitudinal development of the microbiome, including bacteria, viruses and other microorganisms in the gut, plasma, and nasal cavity of prediabetic and diabetic subjects compared to autoantibody negative non-diabetic subjects. Stool samples used were collected monthly from 3 to 48 months, after which stool samples were collected every 3 months. Nasal swab samples were collected every 3 months starting at 9 months of age until 48 months, after which nasal swabs were collected every 6 months. Plasma samples were collected every 3 months starting at 3 months of age until 48 months, after which plasma samples were collected every 6 months. If the subject was autoantibody positive at 48 months then they remained on the 3 month collection interval for nasal swab and plasma samples. Samples underwent 16s rRNA gene sequencing, DNA and viral RNA metagenomics shotgun sequencing, and sequencing of the internal transcribed spacer (ITS) regions. Additional information on the TEDDY microbiome data is available in the following articles: Vatanen et al., 2018, PMID: 30356183, Stewart et al., 2018, PMID: 30356187, and Vehik et al., 2020, PMID: 31792456. RNA Sequencing The TEDDY study aimed to characterize the transcriptome in subjects with islet autoimmunity and type 1 diabetes compared to matched control subjects. Peripheral blood was collected to extract total RNA from enrolled children starting at 3 months of age, and then at 3 month intervals up to 48 months and then biannually. Total RNA was extracted using a high throughput (96-well format) extraction protocol using magnetic (MagMax) beads technology at the TEDDY RNA Laboratory, Jinfiniti Biosciences in Augusta, GA. Purified RNA was then sent to the Broad Institute for the generation of the TEDDY RNA sequencing (RNA-Seq) data. The RNA samples were prepped using Superscript III reverse transcriptase and Illumina's TruSeq Stranded mRNA Sample Prep Kit. The TruSeq libraries were run on the Illumina HiSeq2500 platform. Whole Genome Sequencing The TEDDY study aimed to conduct deep whole genome sequencing and examine the genomic variations in subjects with islet autoimmunity and type 1 diabetes compared to matched autoantibody negative and non-diabetic children. DNA from whole blood was obtained from TEDDY children for whole genome sequencing. The WGS data were generated on the Illumina HiSeq X Ten system.

  Study phs001442

• reChIP-seq reveals widespread bivalency of H3K4me3 and H3K27me3 in CD4+ memory T-Cells

  Identification of genomic loci exhibiting the physical co-localization of two distinct DNA-associated proteins remains a technical challenge. To tackle this challenge we have developed a novel reChIP-seq approach that enriches for two distinct DNA-associated proteins in an unbiased and genome-wide manner. We illustrate the utility of our approach by identifying nucleosomes bivalently modified by H3K4me3 and H3K27me3 in primary human CD4+ central memory T-cells (TCMs). We unravel widespread bivalency of many unmethylated CpG-rich regions in TCMs overlapping with transcriptional start sites of developmental regulators. Furthermore, reChIP-seq uncovers two yet unobserved classes of bivalency, where either H3K4me3 or H3K27me3 is mixed with low but detectable bivalency. We provide evidence that bivalency is established by an interplay between genome and epigenome and thus attains stability. Our described reChIP-seq approach augments conventional ChIP-seq and is broadly applicable in the study of co-localization of DNA-associated proteins to unravel the intricate possibilities of combinatorial action.

  Study EGAS00001001568

• 3D-GSC_expression_profiles

  In this study, we used RNA-sequencing gene expression profiling in order to characterize specific phenotypic traits in DIPG-derived glioma stem cell (GSC) models. Twenty-two primary GSC models derived from biopsies collected at diagnosis were cultured in an ECM-mimicking compound before for RNA extraction and subsequent rRNA-depletion and sequencing .

  Study EGAS00001007182

• small RNA-sequencing and RNA-sequencing of human brain tissue with temporal lobe epilepsy

  This data set contains the raw .fastq files from two RNA-sequencing experiments and two small RNA-sequencing experiments. Both control brain tissue and tissue from sufferers of mesial temporal lobe epilepsy were sequenced. Two different brain regions were sequenced; the cortex and the hippocampus. For more details please see: Mills, James D., et al. "Coding and non-coding transcriptome of mesial temporal lobe epilepsy: Critical role of small non-coding RNAs." Neurobiology of disease 134 (2020): 104612.

  Dataset EGAD00001005735

• Whole genome sequencing of adult glioblastoma nuclei

  Nuclei were extracted from three primary adult glioblastoma samples and processed for scATAC-seq, and residual nuclei left over from these same samples were used to extract DNA and processed and sequenced for WGS. This WGS data was then used to cross-validate assessment of large-scale copy number alterations with scATAC-seq using the Copy-scAT tool.

  Study EGAS00001005256

• Identification of Novel Therapeutic Targets for Calcific Aortic Valve Stenosis Using Integrative Genomics

  In order to identify novel therapeutic targets for calcific aortic valve stenosis, we have generated a large transcriptomic dataset from RNA sequencing of human aortic valve tissues from 500 individuals undergoing aortic valve replacement or heart transplant at the Institut universitaire de cardiologie et de pneumologie de Québec – Université Laval (QUEBEC-CAVS-RNA). This dataset represents a unique resource to understand the molecular mechanisms underlying aortic valve diseases.

  Study phs003541

• Enhancer Mapping in Chronic Lymphocytic Leukemia

  In this study we profile the epigenomic enhancer landscapes of CLL B cells (CD19+/CD5+) harvested from peripheral blood of patients from our Center. Included are results of ChIPseq profiling using chromatin immunoprecipitation of the enhancer histone mark H3K27ac (acetylated lysine 27 on histone H3), and open chromatin profiles using ATAC-seq (assay for transposase accessible chromatin). These profiles are used to define the global enhancer and super enhancer landscape of CLL B cells, and to derive active transcription factor networks associated with this disease. Also included are H3K27ac ChIP-seq and ATAC-seq datasets for non-CLL B cells obtained from the peripheral blood of normal adult donors.

  Study phs001704

• RNA sequencing of colon tumor/normal sample pairs

  RNA sequencing of colon tumor/normal sample pairs

  Dataset EGAD00001000215

• HipSci - Monogenic Diabetes - RNA Sequencing - July 2017

  HipSci - Monogenic Diabetes - RNA Sequencing - July 2017

  Dataset EGAD00001003530

• HipSci - Bleeding and Platelet Disorders - RNA Sequencing - July 2017

  HipSci - Bleeding and Platelet Disorders - RNA Sequencing - July 2017

  Dataset EGAD00001003539

• HipSci - Healthy Normals - RNA Sequencing - May 2015

  HipSci - Healthy Normals - RNA Sequencing - May 2015

  Dataset EGAD00001001438

• HipSci - Monogenic Diabetes - RNA Sequencing - October 2016

  HipSci - Monogenic Diabetes - RNA Sequencing - October 2016

  Dataset EGAD00001003180

• RNA data for MMML (EGAS00001002422)

  RNA sequencing data for MMML (3 tumor samples and 1 gcbcell)

  Dataset EGAD00001003285

• RNA-sequencing of SYSCOL colorectal adenoma-carcinoma samples

  RNA-sequencing alignment for SYSCOL colorectal adenoma-carcinoma samples

  Dataset EGAD00001003318