15040 results for "cancer studies using rna-seq"

in 36.80 milliseconds.

• Reference epigenome IPS04_X_Fibroblast_mRNA-Seq data generated from KEP study

  A IPS04_X_Fibroblast_mRNA-Seq paired end data for iPSC(Oct4)

  Dataset EGAD00001003491

• Reference epigenome SMC01 mRNA-Seq data generated from KEP study

  SMC01 mRNA-Seq paired end data

  Dataset EGAD00001007169

• Reference epigenome SMC04 mRNA-Seq data generated from KEP study

  SMC04 mRNA-Seq paired end data

  Dataset EGAD00001007172

• Reference epigenome SMC02 mRNA-Seq data generated from KEP study

  SMC02 mRNA-Seq paired end data

  Dataset EGAD00001007170

• Reference epigenome SMC03 mRNA-Seq data generated from KEP study

  SMC03 mRNA-Seq paired end data

  Dataset EGAD00001007171

• Reference epigenome ADMSC01 mRNA-Seq data generated from KEP study

  ADMSC01 mRNA-Seq paired end data

  Dataset EGAD00001007150

• Reference epigenome ADMSC03 mRNA-Seq data generated from KEP study

  ADMSC03 mRNA-Seq paired end data

  Dataset EGAD00001007152

• scRNA-seq of placental immune cells

  scRNA-seq analysis of 384 placental immune cells

  Dataset EGAD00001010040

• Reference epigenome IPS01_N_Fibroblast_mRNA-Seq data generated from KEP study

  A IPS01_N_Fibroblast_mRNA-Seq paired end data for iPSC(Oct4)

  Dataset EGAD00001003488

• McGill EMC Release 4 for assay "ChIP-Seq Input"

  McGill EMC Release 4 for assay "ChIP-Seq Input"

  Dataset EGAD00001001293

• STAG1-ChIP-Seq of STAG2-mutated and cohesin wildtype adult AMLs

  ChIPseq targeting the cohesin subunit STAG1 in STAG2-mutated AMLs or cohesin wildtype AMLs

  Dataset EGAD00001011206

• Reference epigenome IPS04_X_Fibroblast_smRNA-Seq data generated from KEP study

  A IPS04_X_Fibroblast_smRNA-Seq single end data for iPSC(Oct4)

  Dataset EGAD00001003504

• ATAC-Seq datasets in human islets

  13 ATAC-Seq datasets of human pancreatic islets from 13 donors

  Dataset EGAD00001005201

• scRNA/TCR-seq of CD8+ T cells from a melanoma patient

  Specific in vitro stimulation of patient-derived PBMC with the neoantigens SYTL4-S363F and KIF2C-P13L was followed by enriching for CD137+ activated T cells. Restimulated T cells as well as an unstimulated patient PBMC sample from the same time point were used for scRNA-Seq/scTCR-Seq.

  Dataset EGAD50000000854

• human placenta snRNA-seq (n=12)

  This dataset include the snRNA-seq raw sequencing files from the placentas of 4 normal weight control and 8 obese individuals. The files are paired-end fastq files. The snRNA-seq libraries were generated with Chromium Single Cell 3’ kit v3.1 (10X Genomics) and sequenced on Illumina NovaSeq 6000 at Novogene, UK.

  Dataset EGAD50000001222

• Human tumour ChIP-seq.

  We profiled 16 patient tumour samples by ChIP-seq. H3K27ac and Input are provided for 16 samples and H3K27me3 is provided for 14 samples. Among the 16 samples, 9 are G34WT and 7 are G34R/V. The raw fastq or bam files are provided.

  Dataset EGAD00001006100

• Genotyping microarray data of molecular tumorboard patients in the context of the HRD-manuscript published in IJC

  Intensity files (.idat) derived from the Infinium CytoSNP-850K BeadChip microarray (Illumina) were used to analzye a possible Homologous-Recombination-Repair Deficiency (Molecular Rationale for a PARP-Inhibitor therapy) of n= 39 tumor patients (each n= 39 .idat files of the green and red chanel respectively) presented in the Molecular Tumorboard at the University Hospital Erlangen. Using these files a Genomic Instability Score was bioinformatically deducted (R-based scripts, e.g. ASCAT, scarHRD) based upon the presence of three parameteres "Loss of Heterozygosity, LOH", "Large Scale Transitions, LST" and "Telomeric Allelic Imbalances, TAIs". The associated manuscript is published in International Journal of Cancer, IJC.

  Dataset EGAD00010002736

• ICR639 CPG NGS Validation series

  The dataset includes sequencing data generated using the TruSight Cancer Panel (TSCP) a targeted NGS assay for analysis of CPGs and orthogonally generated data supporting at least one pathogenic variant in a CPG for a total of 645 pathogenic CPG variants. The set of pathogenic CPG variants includes strong representation of some of the most challenging types of pathogenic variants, with 339 indels, including 16 complex indels and 24 insertions or deletions with length greater than 5bp, and 74 exon CNVs, including 23 single exon CNVs. There are 502 pathogenic variants in BRCA1 or BRCA2, making this an important first-line validation dataset for laboratories performing NGS testing of BRCA1 and BRCA2.

  Dataset EGAD00001004134

• Population-based analysis of POT1 variants in a cutaneous melanoma case-control cohort

  Pathogenic germline variants in the protection of telomeres 1 gene (POT1) have been associated with predisposition to a range of tumor types, including melanoma, glioma, leukemia and cardioangiosarcoma. We sequenced all coding exons of the POT1 gene in 2,929 European-descent melanoma cases and 3,298 controls, identifying 43 protein-changing genetic variants. We performed functional studies on each of these variants and explored their possible contribution to disease risk.

  Dataset EGAD00001009848

• 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 obtained from patients with atopic dermatitis, psoriasis and controls in Japanese population.

  Study JGAS000780

• Pediatric B-cell precursor acute lymphoblastic leukemia RNA sequencing

  This dataset included 33 childhood B-cell precursor acute lymphoblastic leukemia RNA sequencing samples. All samples were subjected to Illumina pair-end sequencing.

  Study EGAS50000000763

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

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

  Study JGAS000593

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

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

  Study JGAS000543

• Single cell RNA-sequencing of GSCs and GBM tumours

  Characterization of glioblastoma tumors and glioblastoma stem cell (GSCs) lines with single cell or single nuclei RNA-sequencing.

  Study EGAS00001004656

• RNA sequencing of AD, MCI and control OM cells

  Total RNA sequencing of cultured OM cells derived from patients with Alzheimer's disease (AD), individuals with mild cognitive impairment (MCI) and cognitively healthy controls.

  Study EGAS00001006195

• Plasma MicroRNA Signatures of Aging

  Background. MicroRNAs are small non-coding RNAs that regulate gene expression post-transcriptionally and show differential expression in various tissues with aging phenotypes. Detectable in circulation, extracellular microRNAs reflect (patho)physiological processes and hold promise as biomarkers for healthy aging and age-related diseases. This study aimed to explore plasma extracellular microRNAs as biological aging indicator and their associations with health outcomes using population-level data. Methods. We quantified plasma expression levels of 2,083 extracellular microRNAs using targeted RNA-sequencing in 2,684 participants from the population-based Rotterdam Study cohort. The training and test sets included 1,930 participants from the advanced-aged initial and second subcohort (RS-I/RS-II; median age: 70.6), while the validation set comprised 754 participants from the middle-aged fourth subcohort (RS-IV; median age: 53.5). Based on 591 microRNAs well-expressed in plasma, we examined differential expression of microRNAs with chronological age, PhenoAge –a composite score of age and nine multi-system blood biomarkers–, the frailty index, and mortality. Next, elastic net models were employed to construct composite microRNA-based aging biomarkers predicting chronological age (mirAge), PhenoAge (mirPA), frailty index (mirFI), and mortality (mirMort). The association of these aging biomarkers with different age-related health outcomes was assessed using Cox Proportional Hazard, linear regression, and logistic regression models in the test and validation sets. Results. We identified 188 microRNAs differentially expressed with chronological age within the RS-I/RS-II advanced-aged population (ntraining=1158, ntest=772), of which 177 microRNAs (94.1%) were replicated in the middle-aged RS-IV subcohort (nvalidation=754). Moreover, 227 miRNAs showed robust associations with PhenoAge, 61 with FI, and 16 with ten-year mortality independent of chronological age. Subsequently, we constructed four plasma microRNA-based aging biomarkers: mirAge with 108, mirPA with 153, mirFI with 81, and mirMort with 50 miRNAs. Elevated scores on these microRNA-based aging biomarkers were associated with unfavorable health outcomes, including lower subjective physical functioning and self-reported health and increased mortality and frailty risk, but not with first- or multi-morbidity. Overall, larger effect estimates were observed for mirPA, mirFI, and mirMort compared to mirAge. Conclusions. This study describes distinct plasma microRNA-aging signatures and introduces four microRNA-based aging biomarkers with potential to identify accelerated aging and age-related decline, providing insights into the intricate process of human aging.

  Study EGAS00001008117

• Genetic Factors associated with Conversion from Active Surveillance to Treatment for Prostate Cancer

  This is a genome-wide association study (GWAS) of conversion from active surveillance (AS) for prostate cancer (PC) to treatment. AS is the monitoring with screening tests and/or biopsies over time of men usually with low-risk, early-stage PC to avoid harmful side effects of PC treatment. "Conversion" refers to patients who withdraw from AS (switch to active treatment) due to changes in their cancer development, anxiety about delayed treatment, or other reasons. Patients who converted because of anxiety alone were censored in the GWAS analysis. The overall goal of this study is to develop an easily measurable, germline-based panel of genetic variants to aid in selecting PC patients appropriate for AS and/or the intensity of monitoring. In total, 6,324 PC patients from 28 collaborating institutions are included in this study. Patients are those with PC who are either on AS or who have previously converted from AS to treatment. In this study, 70% of all subjects were diagnosed with very low-risk, low-risk, or low-volume intermediate-risk PC (Kryvenko and Epstein, 2016; PMID: 26709152) and have been enrolled in an AS program. DNA was extracted from blood, saliva, or formalin-fixed, paraffin-embedded (FFPE) tissue collected from the study subjects at the participating sites. Genotyping, quality control processes, and imputation were performed at the Center for Inherited Disease Research (CIDR) at Johns Hopkins University. CIDR used the Illumina Infinium Multi-Ethnic Global Array (MEGA) version 1, which included genome-wide tag SNPs and 10,000 SNPs selected with custom content. Quality control and assurance (QC/QA) checks on genotyped data involved four domains: sample-level checks, batch effects, SNP-level checks, and preliminary association tests. Samples and SNPs passing the QC/QA checks were imputed to appropriate reference panels (HRC r1.1 for patients of European ancestry) using the Michigan Imputation Server (PMID: 27571263). Unmeasured genetic variants were imputed using the Trans-Omics for Precision Medicine (TOPMed) Imputation Server, with 97,256 reference samples and 308,107,085 SNPs. Variants with imputation quality (INFO) score < 0.3 were excluded, leaving a total of 22,691,641 SNPs successfully imputed. After QC steps, a total of 5,936 samples genotyped at CIDR remained for inclusion in the GWAS.

  Study phs002056

• Bulk RNA Sequencing of 86 Human Donor Lungs

  The goal of this study was to characterize the effect of age on gene expression in normal human lung. We collected distal lung samples from 86 human donors distributed in age between 16 and 76 years and extracted RNA for Illumina-based sequencing.

  Study phs002484

• RNA004 Nanopore DRS of peripheral blood

  Direct RNA Sequencing offers the capability of standard transcriptomic analyses + RNA modification analysis, which holds promising applications for future clinical workflows. To this end, we tested the newly available RNA004 kit on human peripheral blood and provide orthogonal GLORI measurement for benchmarking of m6A-detection.

  Study EGAS50000001201

• Discrepancies in Tumour Mutation Burden (TMB) reporting from sequential Endobronchial ultrasound trans bronchial needle aspiration (EBUS TBNA) samples within single lymph node stations

  Introduction: Tumour Mutation Burden (TMB) is a potential biomarker for immune therapies. Identifying factors that may affect TMB prediction is key to its utility as biomarker for treatment options. Here we investigated parameters that might affect TMB using cytology smears obtained from endobronchial ultrasound transbronchial needle aspiration (EBUS TBNA)-sampled malignant lymph nodes, which are often the only diagnostic samples collected in patients with advanced lung cancer. Methods: Individual Diff-Quik cytology smears were prepared for each needle pass from the same enlarged malignant mediastinal lymph nodes. DNA extracted from smears of each pass underwent sequencing using a large gene panel sequencing (TSO500, Illumina). TMB was estimated for each individual needle pass using the TSO500 Local App v. 2.0 (Illumina). Results: Twenty patients had two or more Diff-Quik smears (total 45 smears) which passed sequencing quality control. Average TMB for all smears was 8.7 ± 5.0 mutations per megabase (Mb). Sixteen of the 20 patients had paired samples with minimal differences in TMB score (average difference 1.3 ± 0.85). Paired samples from 13 patients had concordant TMB in terms of scores being below or above a threshold of 10 mutations/Mb. Samples from seven patients had discrepant TMB, with four cases in particular exhibiting an average difference of 11.3 ± 2.7 mutations/Mb. Factors affecting TMB calling involved tumour content of the samples, the amount of DNA used in sequencing as well as bone fide intra tumour heterogeneity between paired samples. Conclusions: TMB assessment is feasible from EBUS-TBNA derived Diff-Quik slides from a single needle pass. Repeated samples of one or more lymph nodes have minimal variation in TMB in most cases (80%), suggesting accuracy of the test overall. To enhance accuracy of the result, care should be taken in relation to the tumour content of the cytology smear. Further, clinicians should be aware that even slight differences in lymph node site selection could give rise to variable genomics and discrepant TMB due to tumour heterogeneity. Therefore, it would be reasonable for multi-lymph node site analysis of TMB to be performed using EBUS TBNA aspirates from different easily accessible lymph nodes. Further investigations need to determine whether these samples should be combined as one sample or tested separately.

  Study EGAS00001007484

• ctDNA monitoring using patient-specific sequencing and integration of variant reads - Breast cohort

  Circulating tumor-derived DNA (ctDNA) can be used to monitor cancer dynamics noninvasively. Detection of ctDNA can be challenging in patients with low-volume or residual disease, where plasma contains very few tumor-derived DNA fragments. We show that sensitivity for ctDNA detection in plasma can be improved by analyzing hundreds to thousands of mutations that are first identified by tumor genotyping. We describe the INtegration of VAriant Reads (INVAR) pipeline, which combines custom error-suppression methods and signal-enrichment approaches based on biological features of ctDNA. With this approach, the detection limit in each sample can be estimated independently based on the number of informative reads sequenced across multiple patient-specific loci. We applied INVAR to custom hybrid-capture sequencing data from 176 plasma samples from 105 patients with melanoma, lung, renal, glioma, and breast cancer across both early and advanced disease. By integrating signal across a median of >105 informative reads, ctDNA was routinely quantified to 1 mutant molecule per 100,000, and in some cases with high tumor mutation burden and/or plasma input material, to individual parts per million. This resulted in median Area Under the Curve (AUC) values of 0.98 in advanced cancers, and 0.80 in early stage and challenging settings for ctDNA detection. We generalized this method to whole-exome and whole-genome sequencing, showing that the INVAR may be applied without requiring personalized sequencing panels, so long as a tumor mutation list is available. As tumor sequencing becomes increasingly performed, such methods for personalized cancer monitoring may enhance the sensitivity of cancer liquid biopsies.

  Dataset EGAD00001006293

• 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

• CITE-seq raw count matrix

  Raw count matrix for CITE-seq data of PBMC collected from 113 patients.

  Dataset EGAD50000000141

• Raw count matrix scRNA-seq

  Raw count matrix for scRNA-seq data of PBMC collected from 113 patients.

  Dataset EGAD50000000142

• Targeted PacBio long-read scRNA-seq

  Twist custom capture–based targeted PacBio long-read scRNA-seq datasets for human adrenal sampleA and sampleB

  Dataset EGAD50000002212

• Reference epigenome KNIH010 miRNA-seq data generated from KEP study

  A KNIH010 miRNA-seq single end data for podocytes

  Dataset EGAD00001002769

• Reference epigenome CKD23_C_Mesan_smRNA-Seq data generated from KEP study

  A CKD23_C_Mesan_smRNA-Seq single end data for Mesangial cells(kidney)

  Dataset EGAD00001003497

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

  Fat-adipocyte03 miRNA-Seq single end data

  Dataset EGAD00001007190

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

  Pancreas-Islet06 miRNA-Seq single end data

  Dataset EGAD00001007187

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

  Fat-adipocyte04 miRNA-Seq single end data

  Dataset EGAD00001007191

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

  Fat-adipocyte05 miRNA-Seq single end data

  Dataset EGAD00001007192

• Reference epigenome CKD27_C_Mesan_smRNA-Seq data generated from KEP study

  A CKD27_C_Mesan_smRNA-Seq single end data for Mesangial cells(kidney)

  Dataset EGAD00001003500

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

  Pancreas-Islet07 miRNA-Seq single end data

  Dataset EGAD00001007188

• Reference epigenome Islet-derived_iPSC04 miRNA-Seq data generated from KEP study

  Islet-derived_iPSC04 miRNA-Seq single end data

  Dataset EGAD00001007883

• Single-cell DNA-seq

  Single-cell DNA sequencing (scDNA-seq) data.

  Dataset EGAD00001008779

• Reference epigenome Islet-derived_MSC08 miRNA-Seq data generated from KEP study

  Islet-derived_MSC08 miRNA-Seq single end data

  Dataset EGAD00001007882

• oxBS-seq for APL

  Oxidative bisulfite sequencing (oxBS-Seq) for APL

  Dataset EGAD00001008135

• Dataset for iAMP21 scRNA-seq

  10x Genomics 5' library scRNA-seq data for 4 iAMP21 patients

  Dataset EGAD00001009504

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

  Pancreas-Islet08 miRNA-Seq single end data

  Dataset EGAD00001007189

• Reference epigenome CKD23_C_Mesan_mRNA-Seq data generated from KEP study

  A CKD23_C_Mesan_mRNA-Seq paired end data for Mesangial cells(kidney)

  Dataset EGAD00001003481

• Reference epigenome Islet-derived_MSC08 mRNA-Seq data generated from KEP study

  Islet-derived_MSC08 mRNA-Seq paired end data

  Dataset EGAD00001007879

• Reference epigenome Islet-derived_iPSC04 mRNA-Seq data generated from KEP study

  Islet-derived_iPSC04 mRNA-Seq paired end data

  Dataset EGAD00001007880

• Reference epigenome Islet-derived_MSC06 miRNA-Seq data generated from KEP study

  Islet-derived_MSC06 miRNA-Seq single end data

  Dataset EGAD00001007881

• Reference epigenome KNIH011 miRNA-seq data generated from KEP study

  A KNIH011 miRNA-seq single end data for podocytes

  Dataset EGAD00001002770

• Reference epigenome KNIH010 mRNA-seq data generated from KEP study

  A KNIH010 mRNA-seq paired end data for podocytes

  Dataset EGAD00001002176

• Reference epigenome KNIH011 mRNA-seq data generated from KEP study

  A KNIH011 mRNA-seq paired end data for podocytes

  Dataset EGAD00001002177

• Reference epigenome CKD27_C_Mesan_mRNA-Seq data generated from KEP study

  A CKD27_C_Mesan_mRNA-Seq paired end data for Mesangial cells(kidney)

  Dataset EGAD00001003484

• Histone marks

  EZH2, H3K4me3, H3K27ac and H3K27me3 ChIP-seq data consisting of fastq single-end reads from peripheral blood CLL cells

  Dataset EGAD00001004063

• T cell receptor repertoire sequencing reveals chemotherapy-driven clonal expansion in colorectal liver metastases

  Colorectal liver metastasis (CLM) is a leading cause of colorectal cancer mortality, and the response to immune checkpoint inhibition (ICI) in microsatellite stable CRC has been disappointing. Administration of cytotoxic chemotherapy may cause increased density of tumour infiltrating T cells, which has been associated with improved response to ICI. This study aimed to quantify and characterize T cell infiltration in CLM using T cell receptor (TCR) repertoire sequencing. Eighty-five resected CLM from patients included in the Oslo CoMet study were subjected to TCR repertoire sequencing. Thirty-five and 15 patients had received neoadjuvant chemotherapy (NACT) within a short or long interval, respectively, prior to resection, while 35 patients had not been exposed to NACT. T cell fractions were calculated, repertoire clonality was analysed based on Hill evenness curves, and TCR sequential convergence was assessed using network analysis. Increased T cell fractions (10.6% vs 6.3%) were detected in CLM exposed to NACT within a short interval prior to resection, while modestly increased clonality was observed in NACT exposed tumours independently of the timing of NACT administration and surgery. While private clones made up >90% of detected clones, network connectivity analysis revealed that public clones contributed the majority of TCR sequence convergence. TCR repertoire sequencing can be used to quantify T cell infiltration and clonality in clinical samples. This study provides evidence to support chemotherapy-driven T cell clonal expansion in CLM in a clinical context.

  Study EGAS00001007136

• scRNA-seq data of human nuclei collected from the temporal cortex of 17 individuals.

  Transcriptome analysis of single cells has tremendously increased our understanding of cellular heterogeneity in complex tissues. However, whole-cell transcriptomics cannot be applied to frozen tissue due to poor cell recovery after freezing. Human tissue is usually limited to tissue banks containing frozen material, and since single nuclei can be readily isolated from frozen tissues, human cell nuclei can substitute whole-cell analysis to extract single cell transcriptomes from human cells. Despite the importance of human tissue analysis, single-nucleus RNA-sequencing protocols are less developed than those for whole cells. SMARTSeq, either in form of custom-made SMARTSeq2 or as part of the Fluidigm C1 platform, provides a high resolution view of the transcriptome, has proven effective for whole-cell analysis and has recently been adapted to nuclei. Here, we show that the standard SMARTSeq2 protocol applied to single nuclei biases the amplification of single-nucleus transcriptomes due to unspecific capturing of transcripts, which can comprise the majority of reads in single-nucleus RNA-sequencing libraries. We demonstrate that modification of the SMARTSeq2 protocol with respect to template switching and cDNA amplification conditions generates sequencing libraries of higher complexity, allows for detection of more genes and reduces biases of the amplified transcriptome. In addition, modification of SMARTSeq2 chemistry allows for gene detection at shallower sequencing depth, thus increasing efficiency and decreasing cost.

  Study EGAS00001002882

• Kids First and INCLUDE: Down Syndrome, Heart Defects, and Acute Lymphoblastic Leukemia

  This project is a collaboration with the trans-NIH INvestigation of Co-occurring conditions across the Lifespan to Understand Down syndromE (INCLUDE) Project, which seeks to improve health and quality-of-life for individuals with Down syndrome, and NHLBI's TransOmics for Precision Medicine (TOPMed) program, which seeks to apply omics technologies to improve scientific understanding of the fundamental biological processes that underlie heart, lung, blood, and sleep (HLBS) disorders. The data is also available at the INCLUDE Data Hub. Down syndrome (DS), which occurs due to trisomy 21, is one of the strongest risk factors for both congenital heart disease (CHD) and acute leukemia. For instance, children with DS have a 2000-fold increased risk of atrioventricular septal defects (AVSD) and a 20-fold increased risk of acute lymphoblastic leukemia (ALL). An important and innovative aspect of the Kids First program is understanding the overlap between structural birth defects and childhood cancer. Notably, the background of DS predisposes children to both of these phenotypes, however, the genomic architecture of risk remains largely undiscovered. In this joint Kids First/TOPMed/INCLUDE project, we are currently including >2,000 samples for whole-genome sequencing, including: 1) paired germline-tumor samples from children with DS-ALL; and 2) samples from families with DS-CHD. This work will advance our understanding of the developmental pathways that may lead to both structural birth defects and childhood cancer. The objectives of this study are to determine the genetic variants underlying AVSD and ALL risk in children with DS. Therefore, the aims of our study are: 1) compare whole-genome sequencing (WGS) data between children with documented DS-AVSD and children with DS who have structurally normal hearts to identify genetic variants that perturb heart development; and 2) compare WGS data between children with documented DS-ALL and children (from Aim 1) with DS who do not have a known history of ALL. We will also examine associations between germline mutations and somatic genomic features. This study will address the fundamental question of why children with DS have an elevated risk of AVSD and ALL. Insights into the genes that drive DS-AVSD and DS-ALL may have implications for improved genetic counseling, surveillance, clinical management, and treatment strategies for these children. Additionally, our findings may inform targeted therapies or interventions for children without DS who are at risk for structural birth defects and cancer. Additional Pediatric Cardiac Genetics Consortium (PCGC) data from children affected with Down syndrome and congenital heart disease are accessible through two separate dbGaP studies: phs001138 (Kids First) and phs001194 (TOPMed).

  Study phs002330

• scRNA-seq and scATAC-seq data of bone marrow

  17 scRNA-seq and 16 scATAC-seq datasets on bone marrows derived from 16 patients. The sequencing dataset consists of 5 samples without bone marrow infiltration, defined as controls (C1, C2, C3, C4, and C5) and 11 neuroblastoma infiltrated bone marrow samples from patients with MYCN amplification (M1, M2, M3, M4), ATRX mutations (A1, A2), and cases lacking either alteration (S1, S2, S3, S4, S5).

  Dataset EGAD00001008635

• CCA ChIP-seq data (63 CCA, 8 normal, 19 cell-line)

  Fastq files for H3K27ac ChIP-seq (with matched input-DNA control) for 63 CCAs, 8 normal bile duct tissues, 16 CCA cell-lines (including replicates), and 3 normal cholangiocyte cell-lines (including replicates). Library prep was performed with the NEBNext ChIP-seq library preparation kit. Each library (including matching input DNA) was sequenced to an average depth of 20 to 30 million raw reads on Illumina HiSeq4000 sequencer, with paired-end sequencing (except for 3 normal bile tissues done with single-end sequencing).

  Dataset EGAD00001012103

• Genomic Studies of Gilles de la Tourette Syndrome

  This study consists of three components. The first component includes genome-wide association study (GWAS) data on 695 TS cases and 198 ancestry matched controls from the first TS GWAS of 1285 TS cases and 4964 ancestry matched controls. The second component includes genome-wide association study (GWAS) data on 2106 TS cases from the second TS GWAS of 2716 TS cases and 3762 ancestry matched controls. The third component consists of 438 individuals representing 146 probands with DSM-IV-TR diagnosed Tourette Syndrome and their parents (146 complete parent-offspring trios). These individuals are part of the whole exome sequencing study, aiming to use whole exome sequencing of TS parent-offspring to identify de novo protein-truncating variants (PTVs) that are present in the child with TS but not in either parent. All subjects were collected by the Tourette Association of America International Consortium for Genetics (TAAICG) at seven sites in the United States and Canada. Both affected individuals and unaffected relatives were assessed for the presence of Tourette Syndrome and Chronic (Persistent) Tic Disorder (CTD) using a standardized, semi-structured interview, which has high clinical validity and reliability for the diagnoses of TS and CTD (TSAICG, Am J Hum Genet, 2007 (PMID: 17304708)); Darrow et al., Psychiatric Research, 2015 (PMID: 26054936)).

  Study phs001380

• ChIP-Seq files for RMS.

  ChIP-Seq files accompanying the paper titled "Identification of Therapeutic Targets in Rhabdomyosarcoma Through Integrated Genomic, Epigenomic, and Proteomic Analyses".

  Dataset EGAD00001006398

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

  Fat-adipocyte03 mRNA-Seq paired end data

  Dataset EGAD00001007157

• Reference epigenome OB56_N_PreA_smRNA-Seq data generated from KEP study

  A OB56_N_PreA_smRNA-Seq single end data for Preadipocytes(fat)

  Dataset EGAD00001003495

• Dataset for B-ALL scRNA-seq

  10x Genomics 5' library scRNA-seq data for 85 B-ALL patients with different subtypes

  Dataset EGAD00001011327

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

  Fat-adipocyte04 mRNA-Seq paired end data

  Dataset EGAD00001007158

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

  Pancreas-Islet08 mRNA-Seq paired end data

  Dataset EGAD00001007168

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

  Fat-adipocyte05 mRNA-Seq paired end data

  Dataset EGAD00001007159

• Reference epigenome OB57_D_PreA_mRNA-Seq data generated from KEP study

  A OB57_D_PreA_mRNA-Seq paired end data for Preadipocyte(fat)

  Dataset EGAD00001003487

• Reference epigenome CKD25_C_Podo_smRNA-Seq data generated from KEP study

  A CKD25_C_Podo_smRNA-Seq single end data for Podocytes(CD90(-) Podocalyxin(+), kidney)

  Dataset EGAD00001003499

• Reference epigenome Islet-derived_MSC06 mRNA-Seq data generated from KEP study

  Islet-derived_MSC06 mRNA-Seq paired end data

  Dataset EGAD00001007878

• Reference epigenome CKD25_C_Podo_mRNA-Seq data generated from KEP study

  A CKD25_C_Podo_mRNA-Seq paired end data for Podocytes(CD90(-) Podocalyxin(+), kidney)

  Dataset EGAD00001003483

• ChIP-Seq files for PCGP ATRX study

  ChIP-Seq files for PCGP ATRX study paper titled "MYCN Amplification and ATRX Mutations are Incompatible in Neuroblastoma"

  Dataset EGAD00001004429

• Reference epigenome SMC05 h3k9me3 ChIP-Seq data generated from KEP study

  SMC05 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007336

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

  Pancreas-Islet07 mRNA-Seq paired end data

  Dataset EGAD00001007167

• Reference epigenome CKD24_C_Podo_mRNA-Seq data generated from KEP study

  A CKD24_C_Podo_mRNA-Seq paired end data for Podocytes(CD90(-) Podocalyxin(+), kidney)

  Dataset EGAD00001003482

• Reference epigenome OB57_D_PreA_smRNA-Seq data generated from KEP study

  A OB57_D_PreA_smRNA-Seq single end data for Preadipocyte(fat)

  Dataset EGAD00001003496

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

  Pancreas-Islet06 mRNA-Seq paired end data

  Dataset EGAD00001007166

• Reference epigenome SMC01 h3k4me3 ChIP-Seq data generated from KEP study

  SMC01 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007311

• Reference epigenome CKD24_C_Podo_smRNA-Seq data generated from KEP study

  A CKD24_C_Podo_smRNA-Seq single end data for Podocytes(CD90(-) Podocalyxin(+), kidney)

  Dataset EGAD00001003498

• ChIP-Seq files for RMS.

  ChIP-Seq files accompanying the paper titled "Identification of Therapeutic Targets in Rhabdomyosarcoma Through Integrated Genomic, Epigenomic, and Proteomic Analyses".

  Dataset EGAD00001004312

• Reference epigenome ADMSC05 h3k27me3 ChIP-Seq data generated from KEP study

  ADMSC05 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007375

• Non-small cell lung cancer molecular subtypes and vulnerability to immunotherapy treatment combinations

  The phase 3 IMpower150 trial in treatment-naïve patients with metastatic non-small-cell lung cancer (NSCLC) demonstrated significantly longer progression-free (PFS) and overall survival (OS) with first-line atezolizumab (anti-PD-L1)-bevacizumab (anti-VEGF)-carboplatin-paclitaxel (ABCP) than with bevacizumab-carboplatin-paclitaxel (BCP). We characterised four molecular NSCLC subtypes identified by unsupervised clustering of transcriptomes of 564 pre-treatment primary tumour samples from IMpower150 using non-negative matrix factorization (NMF1-4). Each subtype had distinct tumour PD-L1 expression levels, epithelial characteristics, immune composition, and treatment outcomes. Both NMF2 (enriched in tumour proliferation signal, macrophages, and monocytes) and NMF4 (enriched in B-cells and T cells) had elevated tumour PD-L1 expression. Of these two, only NMF4 demonstrated PFS and OS benefits with ABCP versus either BCP or atezolizumab-carboplatin-paclitaxel (ACP). Patients with NMF1 (enriched in basal and squamous-like cells) had improved outcomes on ABCP compared with ACP or BCP; those with NMF3 (enriched in adenocarcinoma signatures) showed similar outcomes among treatments. These insights could help inform individualised first-line treatment for metastatic NSCLC.

  Study EGAS50000001272

• Cell-free DNA analysis reveals POLR1D-mediated resistance to bevacizumab in colorectal cancer

  Bevacizumab is an approved anti-angiogenic drug for patients with metastasized colorectal cancer (mCRC) targeting VEGF. The survival benefit of anti-VEGF therapy in mCRC patients is limited to a few months and acquired resistance mechanisms are greatly unknown. Using plasma DNA, we studied the evolution of tumor genomes in a cohort of patients with mCRC (n=150) and observed a recurrent focal amplification (8.7% of cases) on chromosome 13q12.2. Analysis of TCGA data (n=619) suggested an association with later stages, which we confirmed by longitudinal plasma analyses. We defined the minimally amplified region and studied the mechanistic consequences of copy number gain of the involved genes. The amplification of one gene, POLR1D, impacted cell proliferation, resulting in upregulation of VEGFA, an important regulator of angiogenesis which has been implicated in the resistance to bevacizumab. In several patients, we observed the emergence of this 13q12.2 amplicon under bevacizumab treatment, which was invariably associated with evolution of therapy resistance. Hence, we describe a novel resistance mechanism against a widely applied treatment in mCRC patients which will impact clinical management

  Study EGAS00001003791

• Whole Genome Profiling to Detect Schizophrenia Methylation Markers

  Schizophrenia (SCZ) is a devastating psychiatric disorder. The aim of this study was to detect DNA methylation (5meC) profiles in whole blood associated with SCZ. The sample consisted of schizophrenia cases and controls selected from national population registers in Sweden. We employed MBD protein-based enrichment of the methylated DNA fraction, followed by single end sequencing (50 bp reads) on the SOLiD platform. To improve methylome-wide coverage we used an existing protocol variant, which increases the relative number of fragments from CpG poor regions. To analyze the data, we used an analysis pipeline described elsewhere7, that was designed specifically for MBD-seq and included alignment, quality control (QC) of reads and samples, coverage calculations for the 26,752,702 autosomal CpGs in the reference genome using a non-parametric estimate of the fragment size distribution, an in silico experiment to identify CpGs in loci with alignment problems, a 2-stage adaptive algorithm to combine data from correlated adjacent CpG sites in "blocks", principal component analysis (PCA), association testing, bioinformatics annotation and network analysis. After QC, 1459 subjects with on average 32.4 million high quality reads remained. Association testing was performed on 4.3 million high quality blocks after regressing out possible assay related technical artifacts, sex/age, and unmeasured confounders through the PCA components.

  Study phs000608

• Identification of driver oncogenes in scirrhous-type gastric cancer cell lines

  Scirrhous type gastric cancer (SGC) is one of the most intractable cancer subtypes in human, and its therapeutic targets have been rarely identified to date. Exploration of somatic mutations in the SGC genome with the next generation sequencers has been hampered by low tumor contents by the markedly increased fibrous tissues. Thus, SGC cell lines may be useful resources to search for novel oncogenes in. Here we have conducted whole exome sequencing and RNA sequencing on two SGC cell lines, OCUM-8 and OCUM-9. Interestingly, most of the mutations thus identified have not been reported. In OCUM-8 cells, a novel CD44-IGF1R fusion gene is discovered, the protein product of which ligates the amino-terminus of CD44 to the transmembrane and the tyrosine-kinase domains of IGF1R. Further, both of CD44 and IGF1R are markedly amplified in the OCUM-8 genome and abundantly expressed. CD44-IGF1R has a transforming ability, and the suppression of its kinase activity leads to rapid cell death of OCUM-8. On the other hand, OCUM-9 seems to possess multiple oncogenic events in its genome. The MET gene is highly amplified, and the treatment of OCUM-9 with crizotinib, a MET inhibitor, results in cell death. Further, a novel BORCS5-ETV6 fusion gene is identified in the OCUM-9 genome. BORCS5-ETV6 possesses oncogenic activity, and suppression of its message partially inhibits cell growth. Prevalence of these novel fusion genes among SGC awaits further investigation, but we validate the significance of cell lines as appropriate reagents for detailed genomic analyses of SGC.

  Study JGAS000179

• Biallelic tumor suppressor loss and DNA repair defects in de novo small cell prostate cancer

  Small cell prostatic carcinoma (SCPC) is an aggressive pathology that is managed similar to small cell lung cancer. SCPC can evolve from prostatic adenocarcinoma in response to androgen deprivation therapy but in rare cases is present at initial cancer diagnosis. The molecular etiology of de novo SCPC is incompletely understood due to the scarcity of tumor tissue and the brief life expectancy of patients. Through a retrospective search of our regional oncology pharmacy database, we identified 18 patients diagnosed with de novo SCPC between 2004 and 2017. Ten patients had pure SCPC pathology and the remainder had some admixed adenocarcinoma foci, but all were treated with first line platinum-based chemotherapy. Median overall survival was 28 months. We performed targeted DNA sequencing, whole exome sequencing and mRNA profiling on formalin-fixed paraffin embedded archival tumor tissue. We observed frequent biallelic deletion and/or mutation of tumor suppressors TP53, RB1 and PTEN, similar to treatment related SCPC. Indeed, at the RNA level pure de novo SCPC closely resembled treatment-related SCPC. However, five patients had biallelic loss of DNA repair genes including BRCA1, BRCA2, ATM and MSH2/6: potentially underlying the high genomic instability of this rare disease variant. Two patients with pure de novo SCPC harbored ETS gene rearrangements to androgen-driven promoters, consistent with evolution of de novo SCPC from an androgen-driven ancestor. Overall, our results reveal a highly aggressive molecular landscape underlies this unusual pathologic variant, and propose opportunities for targeted therapy strategies in a disease with few treatment options.

  Study EGAS00001003007

• Colorectal cancer organoids expressing BRAF (fusion) genes

  Fusion genes may be oncogenic drivers and potential targets for personalized therapies in a variety of cancer types. The BRAF gene is frequently involved in oncogenic fusions, with fusion frequencies of 0.2-3% throughout different cancers. BRAF can be fused to a wide variety of genes, however, BRAF fusions rarely occur in the exact same gene configuration, making assessment of the fusion relevance and decision for drug treatment challenging. Here, we devised a colorectal cancer organoid-based platform to functionally characterize diverse BRAF fusions, containing various partner genes (AGAP3, DLG1 and TRIM24). We compared these BRAF fusions with respect to cellular behaviour, downstream signaling activation and response to targeted therapies. We found that 5’ partner choice of BRAF affects cellular localization and intracellular signaling capacities of the fusion genes. The DLG1-BRAF fusion gene showed distinct localization to the plasma membrane and exhibited increased levels of MAPK pathway activation under unperturbed conditions. Furthermore, the different BRAF fusions showed varying sensitivities to the targeted inhibition of ERK and BRAF, with the DLG1-BRAF fusion being the most sensitive. Additionally, RNA-sequencing identified distinct subsets of genes affected by the DLG1-BRAF fusion gene. Importantly, all fusion genes conveyed resistance to the clinically relevant EGFR/HER2/HER4-inhibitor afatinib, suggesting that BRAF fusions should be screened alongside other MAPK pathway alterations to identify mCRC patients amenable to cetuximab treatment. In summary, we developed a platform to efficiently assess molecular and cellular effects of fusion genes, and revealed that differential drug responses and distinct gene expression profiles can be induced by different BRAF fusions .

  Study EGAS00001003558

• Human embryo ATAC+RNA single cell sequencing samples DAC (Linnarsson)

  DAC for human embryo ATAC+RNA single cell sequencing samples

  Dac EGAC50000000657

• Linked-Read Whole Genome Sequencing Profiles Structural Variant Landscape of Gliomas with Barcode-Level Evidence

  Structural variants (SVs) play essential roles in glioma oncogenesis and offer prognostic insights, yet the SV landscape of gliomas has not been completely characterized due to previous technological limitations. To comprehensively profile the SV landscape of gliomas, we performed linked-read whole genome sequencing (LR-WGS) on 31 tumour samples from 20 glioma patients. Using barcode-level evidence, we validated known SVs and identified the presence of extrachromosomal DNA containing the EGFR sequence, which correlates with elevated EGFR expression. Additionally, we uncovered rare SVs affecting genes typically mutated by single nucleotide variants (SNVs), including those in the SWI/SNF chromatin remodeling complex. Our analysis also reveals selective chromosomal vulnerabilities to SVs, highlighting certain chromosomes as hotspots for SV accumulation. Collectively, our results demonstrate the utility of LR-WGS in accurately detecting SVs and thoroughly profiling SV landscapes using barcode-level evidence. The insights gained into the synergistic effects of SNVs and SVs on oncogenes further underscore the importance of integrating SV analysis when examining mutations in gliomas.

  Study EGAS50000000947

• Providing access to COVID-19 data: one year later

  response efforts On 11 March 2020 the World Health Organization declared the novel coronavirus outbreak a global pandemic. Four months later, the European Genome-phenome Archive (EGA) released its first COVID-19 dataset. This dataset – single cell RNA and VDJ sequencing of B cells from 60 COVID-19 patients – showed that neutralizing antibodies could be identified by high-throughput sequencing in response to SARS-CoV-2 infection. That was one year ago. Today, the EGA provides access to fifteen COVID-19 datasets from researchers across seven countries in Asia, Europe, and North America. These studies represent almost 17,000 individuals and have resulted in at least sixteen publications and preprints. Researchers deposit controlled access COVID-19 data at EGA The global research community has come together rapidly to investigate the SARS-CoV-2 coronavirus and better understand the related disease, COVID-19. These research efforts generate valuable genetic and phenotypic data from patients and research participants that can be shared with approved researchers. The EGA enables sharing of this research data by providing a service for archiving and controlled distribution of sensitive data. Over the past year, the EGA has worked with researchers to archive and distribute COVID-19 data from high-throughput sequencing experiments, genotyping studies, and phenotypic information. These datasets investigate the immune system, blood, and cells and tissues of the lung, which are relevant for studying a contagious respiratory illness caused by a viral infection. *Study Spotlight. In January 2021, Ancestry.com demonstrated the utility of deep phenotyping based on self-reported outcomes from a large population of mild and asymptomatic COVID-19 cases. They identified genetic associations with eight COVID-19 phenotypes and showed distinct patterns of association, most notably related to the chr3/SLC6A20/LZTFL1 and chr9/ABO regions. The supporting data is available at the EGA to approved researchers and includes both genotype and phenotype data for 15,000 individuals. EGA collaborates with global COVID-19 community Since the coronavirus outbreak, the EGA has collaborated with other life science resources to support discovery and access to COVID-19 datasets. COVID-19 Host Genetics Initiative. With the NHGRI’s Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) platform, the EGA enables sharing of individual-level genetic and phenotypic data from the COVID-19 Host Genetics Initiative (HGI). This initiative aims to generate, share, and analyze data from COVID-19 host genetics research projects to better understand the genetic determinants of COVID-19 susceptibility, severity, and outcomes. In response to COVID-19, the EGA actively supports COVID-19 data submissions and integration of data access and flow into the COVID-19-HGI analysis platform. *Study Spotlight. The COVID-19 HGI has combined individual-level data for 13,868 COVID-19 positive patients (N=7,167 hospitalized) from 17 cohorts in nine countries. The data were used to assess the association of the major common COVID-19 genetic risk factor (chromosome 3 locus tagged by rs10490770) with mortality, COVID-19-related complications, and laboratory values. The genotype and phenotype data for 10 of these cohorts is available at the EGA to approved researchers under accession EGAS00001005304. Fig 1: EGA COVID-19 studies are searchable in the European COVID-19 Data Portal alongside other COVID-19 and SARS-CoV-2 public datasets. European COVID-19 Data Portal. EGA-archived COVID-19 data are discoverable via the European COVID-19 Data Portal (Fig. 1), which brings together public and controlled access data to accelerate coronavirus research for the international community. By indexing all COVID-19-related data in one place, researchers can more easily discover relevant datasets of interest, thus increasing the “FAIRness” (Findability, Accessibility, Interoperability, Reusability) of this valuable data. Fig 2: SARS-CoV-2 viral sequences are imported from ENA and analysed in Galaxy to detect variants. Results are accessible to researchers through the COVID-19 Viral Beacon. COVID-19 Viral Beacon. The COVID-19 Viral Beacon tool was developed in collaboration with the European Nucleotide Archive and Galaxy to enable near real-time browsing of SARS-CoV-2 variability at genomic, amino acid, and motif levels (Fig. 2). The COVID-19 Viral Beacon allows researchers to (i) perform detailed searches about genomic variants, (ii) filter queries and find unique cases, (iii) filter data based on strain-specific variants, and (iv) explore associated metadata. It uses the Global Alliance for Genomics and Health (GA4GH) Beacon standard including new Beacon v2 features. With this tool, researchers can study intra-host mutations on genomic regions of interest, or trace any variant frequency over time using raw read data. More than 200,000 SARS-CoV-2 analysed genomic data files are now available to researchers for further exploration. Ongoing COVID-19 efforts at EGA Addressing the COVID-19 pandemic is a global effort. Federated resources are necessary to support transnational deposition, access, and analysis of sensitive COVID-19 host genetics and other related data. At the same time, many countries now have emerging personalized medicine programmes which are generating data from national or regional healthcare initiatives. These data are subject to more stringent information governance than research data and often must comply with national data protection legislation. To address this need, the Federated EGA was established to serve as the primary global resource for discovery and access of sensitive human omics and associated data consented for secondary use. The Federated EGA will comprise a network of national human data repositories and will implement community standards and common interfaces. Launching Federated EGA promises to accelerate not only global research efforts to understand, diagnose, and treat COVID-19, but also to foster data reuse, enable reproducibility, and accelerate biomedical and disease research to ultimately improve human health.

  Blog providing-access-to-covid-19

• ICGC MMML-seq Data Freeze November 2012 transcriptome sequencing

  ICGC MMML-seq Data Freeze November 2012 transcriptome sequencing

  Dataset EGAD00001000281

• Genetic Mechanisms of Disease Lab DAC

  Data access for raw and processed H3K27ac ChIP-seq and ATAC-seq data from "Effect of ETS2 modulation on chromatin accessibility and enhancer activity in human macrophages".

  Dac EGAC50000000087

• Reference epigenome KNIH007 mRNA-seq data generated from KEP study

  A KNIH007 mRNA-seq paired end data for adipocytes

  Dataset EGAD00001002173

• Reference epigenome SMC03_smRNA-Seq data generated from KEP study

  A SMC03_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003860

• Reference epigenome SMC08_smRNA-Seq data generated from KEP study

  A SMC08_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003865

• Reference epigenome SMC01_smRNA-Seq data generated from KEP study

  A SMC01_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003858

• Reference epigenome KNIH009 mRNA-seq data generated from KEP study

  A KNIH009 mRNA-seq paired end data for preadipocytes

  Dataset EGAD00001002175

• Reference epigenome SMC04 h3k9me3 ChIP-Seq data generated from KEP study

  SMC04 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007330

• Reference epigenome SMC07_smRNA-Seq data generated from KEP study

  A SMC07_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003864

• Reference epigenome ADMSC01 input ChIP-Seq data generated from KEP study

  ADMSC01 input ChIP-Seq paired end data

  Dataset EGAD00001007361

• Reference epigenome SMC09_smRNA-Seq data generated from KEP study

  A SMC09_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003866

• Reference epigenome KNIH009 miRNA-seq data generated from KEP study

  A KNIH009 miRNA-seq single end data for preadipocytes

  Dataset EGAD00001002768

• Reference epigenome SMC09 h3k9me3 ChIP-Seq data generated from KEP study

  SMC09 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007354

• Reference epigenome SMC01 h3k9me3 ChIP-Seq data generated from KEP study

  SMC01 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007312

• Reference epigenome SMC02 h3k36me3 ChIP-Seq data generated from KEP study

  SMC02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007315

• Reference epigenome ADMSC06 h3k9me3 ChIP-Seq data generated from KEP study

  ADMSC06 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007386

• Reference epigenome ADMSC06 input ChIP-Seq data generated from KEP study

  ADMSC06 input ChIP-Seq paired end data

  Dataset EGAD00001007387

• Reference epigenome SMC08 h3k27ac ChIP-Seq data generated from KEP study

  SMC08 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007344

• Reference epigenome ADMSC01 h3k27ac ChIP-Seq data generated from KEP study

  ADMSC01 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007356

• Reference epigenome ADMSC02 h3k36me3 ChIP-Seq data generated from KEP study

  ADMSC02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007363

• Reference epigenome ADMSC06 h3k4me3 ChIP-Seq data generated from KEP study

  ADMSC06 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007385

• Reference epigenome ADMSC08 h3k4me1 ChIP-Seq data generated from KEP study

  ADMSC08 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007398

• Reference epigenome SMC04 h3k4me1 ChIP-Seq data generated from KEP study

  SMC04 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007328

• Reference epigenome ADMSC06 h3k27me3 ChIP-Seq data generated from KEP study

  ADMSC06 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007382

• Reference epigenome SMC03 h3k9me3 ChIP-Seq data generated from KEP study

  SMC03 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007324

• Reference epigenome SMC04 h3k36me3 ChIP-Seq data generated from KEP study

  SMC04 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007327

• Reference epigenome SMC04 h3k4me3 ChIP-Seq data generated from KEP study

  SMC04 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007329

• Reference epigenome SMC05 h3k4me1 ChIP-Seq data generated from KEP study

  SMC05 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007334

• Reference epigenome ADMSC02 h3k27ac ChIP-Seq data generated from KEP study

  ADMSC02 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007362

• Reference epigenome ADMSC02 h3k4me1 ChIP-Seq data generated from KEP study

  ADMSC02 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007364