11936 results for "fc 26 switch coins Visit Buyfc26coins.com for latest FC 26 coins news..NhK9"

in 30.17 milliseconds.

• UK10K COHORT TWINSUK

  The UK10K project proposes a series of complementary genetic approaches to find new low-frequency/rare variants contributing to disease phenotypes. These will be based on obtaining the genome-wide sequence of 4000 samples from the TwinsUK and ALSPAC cohorts (at 6x sequence coverage), and the exome sequence (protein-coding regions and related conserved sequence) of 6000 samples selected for extreme phenotypes. Our studies will focus primarily on cardiovascular-related quantitative traits, obesity and related metabolic traits, neurodevelopmental disorders and a limited number of extreme clinical phenotypes that will provide proof-of-concept for future familial trait sequencing. We will directly analyse quantitative traits in the cohorts and the selected traits in the extreme samples, and also use imputation down to 0.1% allele frequency to extend the analyses to further sample sets with genome wide genotype data. In each case we will investigate indels and larger structural variants as well as SNPs, and use statistical methods that combine rare variants in a locus or pathway as well as single-variant approaches. The TwinsUK samples will be part of the cohort study and will undergo whole genome sequencing. For further information with regard to this cohort please contact Brent Richards (brent.richards@kcl.ac.uk) or Nicole Soranzo (ns6@sanger.ac.uk).

  Study EGAS00001000108

• Performance comparison of three DNA extraction kits on human whole-exome data from formalin-fixed paraffin-embedded normal and tumor samples

  We generated 42 human whole-exome sequencing data sets from fresh-frozen (FF) and FFPE samples. These samples include normal and tumor tissues from two different organs (liver and colon), that we extracted with three different FFPE extraction kits (QIAamp DNA FFPE Tissue kit and GeneRead DNA FFPE kit from Qiagen, Maxwell\textsuperscript{TM} RSC DNA FFPE Kit from Promega). Variant calling analysis shows a very high rate of concordance between matched FF / FFPE pairs and equivalent performance for the three kits we analyzed. We find a significant variation in the difference of total number of variants called between FF and FFPE samples for the three different FFPE DNA extraction kits. Coverage analysis shows that FFPE samples have less good indicators than FF samples, yet the coverage quality remains above accepted thresholds. We detect limited but significant variations in coverage indicator values between the three FFPE extraction kits. Globally, the GeneRead and QIAamp kits have better variant calling and coverage indicators than the Maxwell kit on the samples used in this study, although this kit performs better on some indicators and has advantages in terms of practical usage. Taken together, our results confirm the potential of FFPE samples analysis for clinical genomic studies, but also indicate that the choice of a FFPE DNA extraction kit should be done with careful testing and analysis beforehand in order to maximize the accuracy of the results.

  Study EGAS00001002631

• Long-read trio sequencing of unsolved patients with intellectual disability

  Long-read sequencing (LRS) has the potential to comprehensively identify all medically-relevant genome variation, including variation commonly missed by short read sequencing (SRS) approaches. To determine this potential, we performed LRS up to 40x coverage using the Pacific Biosciences Sequel instrument for 5 patient-parent trios with intellectual disability, whose aetiology remained unresolved after SRS exomes and genomes. A first systematic assessment of LRS coverage showed that ~35Mb of the genome was only accessible by LRS and not SRS. Structural variant (SV) calling yielded on average 28,292 SV calls per individual, totalling 12.9 MB of sequence. Trio-based analyses showed concordance for up to 95% of these SV calls across the genome, and 80% of the LRS SV calls were not identified by SRS. De novo mutation analysis did not identify any de novo SVs, confirming these are rare events. Because of high sequence coverage, we were also able to call single nucleotide substitutions. On average, we identified 3 million substitutions per genome, with a Mendelian inheritance concordance of up to 97%. Of these, approximately 100,000 were located in the ~35Mb of the genome that was only captured by LRS. Moreover, these variants affected the coding sequence of 64 genes, including 32 known to cause Mendelian disorders. Although no clinical diagnoses were made in these 5 patients, our data show the potential added value of LRS compared to SRS for identifying medically relevant genome variation.

  Study EGAS00001004319

• The Southern African Human Genome Programme

  The Southern African Human Genome Programme (SAHGP) is a national and regional initiative that aims to unlock the unique genetic character of southern African populations. Its vision is to improve quality of life through understanding human genetic diversity. It is timely to advance the SAHGP initiative since global genomic initiatives are increasingly pointing to populations in Africa, more specifically the San and Khoe from southern Africa, as harbouring the most ancient genetic signatures among the living populations of the world. Studies on African populations have already generated a wealth of information upon which the SAHGP can build. This information will be used to promote and support genomic research programmes both nationally and regionally to address critical questions that would benefit the people of our region. The SAHGP aims to make a significant contribution to the understanding of DNA variation among southern Africans and how this impacts on the health of the people of this region. Aims of the SAHGP: To develop capacity for genomic research in southern Africa; To establish sustainable resources for genomic research in the region; and To translate knowledge and information into improvements in human health in the region. The objective is to derive the maximum benefit from the SAHGP, and that the data should be widely shared with the scientific community for the benefit of the people of the sub-continent and beyond.

  Study EGAS00001002639

• Disease and phenotype relevant genetic variants identified from histone acetylomes in human hearts

  Identifying genetic markers for heterogeneous complex diseases such as heart failure has been challenging, and may require prohibitively large cohort sizes in genome-wide association studies (GWAS) in order to demonstrate statistical significance1. On the other hand, chromatin quantitative trait loci (QTL), elucidated by direct epigenetic profiling of specific human tissues, may contribute towards prioritising variants for disease-association. Here, we captured non-coding genetic variants by performing enhancer H3K27ac ChIP-seq in 70 human control and end-stage failing hearts, mapping out a comprehensive catalogue of 47,321 putative human heart enhancers. 3,897 differential acetylation peaks (FDR < 0.05) pointed to recognizable pathways altered in heart failure (HF). To identify cardiac histone acetylation QTLs (haQTLs), we regressed out confounding factors including HF disease status, and employed the G-SCI test2 to call out 1,680 haQTLs (FDR < 0.1). A subset of these showed significant association to gene expression, either in cis (180), or through long range interactions (81), identified by Hi-C and Hi-ChIP performed on a subset of hearts. Furthermore, a concordant relationship was found between the gain or disruption of specific transcription factor (TF) binding motifs, inferred from alternative alleles at the haQTLs, associated with altered H3K27ac peak heights. Finally, colocalisation of our haQTLs with heart-related GWAS datasets allowed us to identify 62 unique loci. Disease-association for these new loci may indeed be mediated through modification of H3K27-acetylation enrichment and their corresponding gene expression differences.

  Study EGAS00001003586

• Spatio-temporal Profiling of a Rhabdoid Tumor Case Study

  Background Immune checkpoint inhibition (ICI) has revolutionized oncology, offering extended survival and long-term remission in previously incurable cancers. While highly effective in tumors with high mutational burden, lowly mutated cancers, including pediatric malignancies, present low response rate and limited predictive biomarkers. Patients and methods We present a framework for the identification and validation of tumor-reactive T cells as a biomarker to quantify ICI efficacy and as candidates for a personalized TCR-T cell therapy. Therefore, we profiled a pediatric malignant rhabdoid tumor patient with complete remission after ICI therapy using deep single-cell T cell receptor (TCR) repertoire sequencing of the tumor microenvironment (TME) and the peripheral blood. Results Tracking T cell dynamics longitudinally from the tumor to cells in circulation over a time course of 12 months revealed a systemic response and durable clonal expansion of tumor-resident and ICI-induced TCR clonotypes. We functionally validated tumor reactivity of TCRs identified from the TME and the blood by co-culturing patient-derived tumor cells with TCR-engineered autologous T cells. Here, we observed unexpectedly high frequencies of tumor-reactive TCR clonotypes in the TME and confirmed T cell dynamics in the blood post-ICI to predict tumor-reactivity. Conclusion These findings strongly support spatio-temporal tracking of T cell activity in response to ICI to inform therapy efficacy and to serve as a source of tumor-reactive TCRs for personalized TCR-T designs.

  Study EGAS00001008174

• Genomic Diagnosis and Individualized Therapy of Highly Penetrant Genetic Diabetes

  The most common and well-known causes of diabetes are type 1 and type 2 diabetes; however, there are many other etiologies of diabetes. At least 1-2% of diabetes cases result from high penetrance single gene defects, most commonly in a gene encoding either a transcription factor involved in beta cell development and function or the enzyme glucokinase (GCK). Others have mutations in transcription factors necessary for beta cell development and function. This subset of monogenic diabetes cases is autosomal dominant and is known as "Maturity-Onset Diabetes of the Young" (MODY). Patients with type 1 diabetes are insulin dependent, meaning they lack insulin production and therefore require multiple daily injections of insulin, as well as multiple times a day blood glucose monitoring. This can be quite burdensome. Type 2 diabetes is predominantly an insulin resistance problem and can often be managed with pills initially, but there are many who also require insulin either at diagnosis or later on in their disease course. Usually, monogenic diabetes is misdiagnosed as type 1 or type 2 diabetes and thus treated suboptimally. Those with monogenic diabetes, due to a defect in a gene encoding a transcription factor, respond extremely well to a class of oral diabetes medications called sulfonylureas. Those with GCK-MODY often have a mild, stable, fasting, nonprogressive hyperglycemia for which pharmacological treatment is often not necessary. It is important to accurately diagnose our patients with the correct etiology to their diabetes for a multitude of reasons. Accurately diagnosing a patient with MODY, for example, can predict their clinical course and we can tailor treatment accordingly. If diagnosed with a transcription factor MODY, we can try switching the patient from insulin to an oral sulfonylurea, and if diagnosed with a GCK-MODY, we can consider discontinuing pharmacological treatment altogether. With specific tailoring of treatment, we would hope to improve quality of life by discontinuing perhaps multiple daily injections of insulin and glucose checking as these may not be necessary. In addition, we would hope to improve glycemic control with an eventual decreased cost to society by decreasing the complication rate of diabetes, resulting in less specialty referrals, lab tests, supplies and procedures. Further, given MODY is an autosomal dominant condition with a 50% chance of inheritance to first-degree family members, we could offer testing to asymptomatic family members which could lead to prevention and/or early diagnosis and treatment. Overall, testing for monogenic diabetes is underutilized clinically. Testing is being done in the U.K., but in the U.S. we are lagging behind. This is most predominantly due to lack of knowledge and concern over cost. In addition, it is difficult to differentiate the relatively small number of monogenic diabetes cases from the larger number with type 1 and type 2 diabetes as each of these etiologies have overlapping characteristics. In fact, many patients with monogenic diabetes are being misdiagnosed with type 1 and type 2 diabetes. There are recommendations set forth by different societies on who to consider screening for monogenic diabetes. However, these recommendations are difficult to employ clinically as they are general recommendations, there is no specific algorithm, and they are based on varying levels of evidence. The purpose of this study is to implement a personalized diabetes medicine program to enhance the identification of individuals and families affected by highly penetrant diabetes gene mutations through a combination of systematic screening and genetic testing at the University of Maryland Center for Diabetes and Endocrinology, the Baltimore VA Medical Center and additional partner centers at Geisinger Health System and Bay West Endocrinology Associates, and other UM outpatient clinics. Our hope is to develop an approach that can be eventually implemented across the United States so that we can more often uncover the correct etiology to diabetes, tailor treatment accordingly, and test asymptomatic family members.

  Study phs001771

• Sequencing data for oesophageal and related samples - BOs release 2 (RNA)

  Dataset EGAD00001003840

• DCC_R26.2.PRAD-CNSM-Array

  Raw Array data from the PRAD-CA for ICGC DCC Release27

  Dataset EGAD00010001519

• gz_bjREP

  DNA for 2979 individuals from Guangzhou was extract from peripheral blood and genotyped by Sequenom, with digit-number working memory, visuospatial working memory, recent long-term memory measured.

  Dataset EGAD00010001528

• Sequencing data for oesophageal and related samples - BOs release 4 (RNA)

  Dataset EGAD00001004023

• Genentech_cell_line_SNP

  SNP array data for 668 cancer cell lines

  Dataset EGAD00010000951

• Sequencing data for oesophageal and related samples - BOs release 5 (RNA)

  Dataset EGAD00001005385

• 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

• Sequencing data for oesophageal and related samples - BOs release 3 (RNA)

  Dataset EGAD00001003901

• Effects of interferon-gamma treatment on human small intestinal organoids.

  Intestinal organoids treated with interferon-gamma 1 ng/mL either for 6h or 18h.

  Dataset EGAD50000000115

• Small RNA sequencing dataset from human prefrontal cortex tissue - ALS vs CTR

  For mature miRNAs and hairpins, BAM and FASTq files

  Dataset EGAD50000000468

• TOAPR-B patient cell-free DNA WGS and WES

  Sequencing files for TOPARP-B patients as described in manuscript

  Dataset EGAD50000000407

• RNAseq RPKM

  RNA-seq data from ILC samples. This dataset includes counts normalized for sequencing depth and gene length.

  Dataset EGAD50000000828

• Centre Leon Berard - DAC - seq data Neuroendocrine tumors - B Gibert

  Review committee for the neuroendocrine tumor genetic data set uploaded by Benjamin GIBERT (CRCL), Anthony FERRARI (CLB) and Thomas WALTER (HCL)

  Dac EGAC50000000218

• DAC for "Capturing disease severity in LIS1-lissencephaly reveals proteostasis dysregulation in patient-derived forebrain organoids" with Julia Ladewig(Julia.ladewig@zi-mannheim.de), Moritz Mall(m.mall@dkfz-heidelberg.de), and Matteo Gasparotto(matteo.gasparotto@zi-mannheim.de)

  Dac EGAC00001003588

• Walter and Eliza Hall - Aix-Marseille Université

  Data Access Committee for the Liao et al. 2025 study, D4Z4End2End: complete genetic and epigenetic architecture of D4Z4 macrosatellites in FSHD, BAMS and reference cohorts.

  Dac EGAC50000000655

• Division of Cancer Evolution National Cancer Center Research Institute Data Access Committee

  Data Access Committee for data from Division of Cancer Evolution, National Cancer Center Research Institute

  Dac EGAC50000000737

• MD Anderson OPMD DAC

  Datasets and agreement used for the OPMD dataset access of the publication Reconstructing oral cavity tumor evolution through brush biopsy, Springer Nature, DOI: 10.1038/s41598-024-72946-3

  Dac EGAC50000000384

• scWGS PDX

  Single cell WGS (low pass) for PT1-derived PDX samples

  Dataset EGAD00001006137

• 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

• Uncovering tumor intrinsic and extrinsic factors that regulate hepatocellular carcinoma growth using patient derived xenograft assays

  Exome Sequencing and RNA Sequencing Data for PDX Samples

  Dataset EGAD00001005734

• Reference epigenome IPS02_N_NPC_WGBS data generated from KEP study

  A IPS02_N_NPC_WGBS paired end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003474

• Reference epigenome IPS03_N_ENeuron_WGBS data generated from KEP study

  A IPS03_N_ENeuron_WGBS paired end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003475

• 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 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

• RNA sequencing data to study "A biobank of patient-derived pediatric braintumor models"

  RNA sequencing data for the PDOX model EPD-613FH

  Dataset EGAD00001003573

• Fecal 16S HV metadata

  Metadata for fecal 16S-V4 rRNA gene sequence data from NCT02749630 healthy volunteers.

  Dataset EGAD00001008821

• scRNA primaries

  Single cell RNA-seq for primary samples

  Dataset EGAD00001006135

• 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

• Illumina HiSeq 2000

  Three files of patients 20, 23 and 25 with WGS done on Illumina HiSeq 2000. For research purpose and authorised user only.

  Dataset EGAD00001003438

• 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

• RNA-Seq Pair End

  RNA analysis of two patients 11 and 15 with WGS done on Illumina HiSeq2000. For research purpose and authorised user only.

  Dataset EGAD00001003429

• 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

• Treatment arm information and sample sheet

  Table of treatment arm information for the 418 RNAseq evaluable population.

  Dataset EGAD00001009502

• H3Africa SIREN Phenotype

  Phenotype data for 3421 Samples from Nigeria and Ghana, sequenced with the Illumina NestSeq 500.

  Dataset EGAD00001011075

• V2 Colorectal panel test

  Targeted gene screen of cell line tumour samples for testing the new V2 Colorectal gene panel.

  Dataset EGAD00001003253

• 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

• ATAC-seq for placental and buffycoat cells.

  We performed ATAC-seq experiments using 2 placental samples and 2 buffycoat samples.

  Dataset EGAD00001004291

• Res1_PC9_exp2_MC_01.04.22

  8 cell pellet samples for genomic DNA extraction. CRISPR PCR1 and PCR2 indexing - Please use standard Kosuke primers.

  Dataset EGAD00001012233

• Res1_PC9_exp1_MC_29.03.22

  8 cell pellet samples for genomic DNA extraction. CRISPR PCR1 and PCR2 indexing - Please use standard Kosuke primers.

  Dataset EGAD00001012232

• GR@ACE_StageI.v01

  Binary Plink files for pre-GWAS quality control in 7409 samples genotyped using Axiom 815K Spanish Biobank array (Thermo Fisher)

  Dataset EGAD00010001654

• GR@ACE_StageI.CEL_files

  Intensity calculation on the pixel values of the DAT file for 7409 samples genotyped using Axiom 815K Spanish Biobank array (Thermo Fisher)

  Dataset EGAD00010001655

• CML_CP_MBC_LBC_Illumina_ Beadchip_HT12v4_All_Samples_Gene_Expression.xlsx

  Expression dataset for CD34 sorted primary CML bone marrow samples

  Dataset EGAD00010002209

• The ERASMUSMC-HEMA Data Access Committee controlling appeals for 3q26-rearranged acute myeloid leukemia datasets from the department of Hematology at Erasmus MC

  Dac EGAC00001000162

• PBMC all cell metadata

  Cell annotation for single cells of PBMC collected from 113 patients, including UMAP coordinates and annotation.

  Dataset EGAD50000000145

• TUM Experimental Neuroimmunology

  This DAC will decide about all data requests to human data deposited at EGA that was primarily produced at the Institute for Experimental Neuroimmunology at the Technical University Munich, Germany.

  Dac EGAC50000000093

• 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

• BROCA cancer panel sequencing for 15 samples

  BROCA panel sequencing using the BROCA-HR v8 and BROCA-GO v1 versions of the gene panel

  Dataset EGAD50000000035

• bfast CohortD clinical data

  Clinical data for BFAST Cohort D, including time-to-event, tumor response, and baseline prognostics data.

  Dataset EGAD50000000149

• Amsterdam UMC Head and Neck Cancer Biology and Immunology

  This data access committee oversees data and assesses requests for the Head and Neck Cancer Biology and Immunology lab of Amsterdam UMC, location VUmc.

  Dac EGAC50000000350

• RCC trial WES dataset

  This dataset includes WES data for 116 runs, corresponding to 58 pairs of normal(PBMC)/tumor samples from 58 patients (116 BAM files).

  Dataset EGAD50000000953

• H3Africa KDRN Phenotype

  The phenotypic data for 6431 samples of the KDRN Study from Ghana and Nigeria.

  Dataset EGAD00001009333

• Guardant ctDNA variant analysis

  This table describes each variant detected using Guardant 360 for each sample included in this analysis.

  Dataset EGAD50000001340

• DAC "Genotyping based on SNPs covered by targeted NGS"

  DAC for the dataset from the article "Genotyping from targeted NGS data based on a small set of SNPs correctly matches patient samples"

  Dac EGAC50000000358

• DAC for "High-Resolution Spatial Transcriptomics Uncover Epidermal-Dermal Divergences in Merkel Cell Carcinoma: Spatial Context Reshapes the Gene Expression Landscape" with Jürgen C. Becker(j.becker@dkfz-heidelberg.de) and Nalini Srinivas(n.srinivas@dkfz-heidelberg.de )

  Dac EGAC00001003599

• Data of the Institute of Pathology, University Medical Center Freiburg, Germany

  DAC for proteomics data deposited in EGA by the proteomics laboratory of the Institute of Pathology, University Medical Center Freiburg, Germany

  Dac EGAC50000000048

• Dataset for desmoplastic small round cell tumor array data

  This dataset contains 24 methylation array samples of patients with desmoplastic small round cell tumor measured on Illumina 850k.

  Dataset EGAD00010002689

• Nouscom Data Access Committee

  The Data Access Committee reviews and approves data access requests for "WES Analysis of precancerous lesions in Lynch Syndrome" study, ensuring compliance with participants’ consent and institutional ethical regulations.

  Dac EGAC50000000784

• Plasma cell‐free transcriptome profiling in chronic liver disease

  RNAseq data from plasma samples corresponding to a Grand Challenge Programme project to find biomarkers for liver disease, including NAFLD, NASH, Cirrhosis and Hepatocelullarcarcinoma.

  Dac EGAC50000000360

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

  A KNIH009 mRNA-seq paired end data for preadipocytes

  Dataset EGAD00001002175

• 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 ADMSC02_ChIP-Seq(H3K27me3) data generated from KEP study

  A ADMSC02_ChIP-Seq(H3K27me3) paired end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003855

• 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 SMC09_smRNA-Seq data generated from KEP study

  A SMC09_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003866

• Reference epigenome SMC08_smRNA-Seq data generated from KEP study

  A SMC08_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003865

• bulkRNA

  Bulk RNA-seq for cALL patient-derived PDX samples

  Dataset EGAD00001006133

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

  A KNIH007 mRNA-seq paired end data for adipocytes

  Dataset EGAD00001002173

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

  A KNIH009 miRNA-seq single end data for preadipocytes

  Dataset EGAD00001002768

• Reference epigenome SMC01_smRNA-Seq data generated from KEP study

  A SMC01_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003858

• Whole Exome Sequencing

  Whole Exome Sequencing for a cohort of 20 B-ALL samples : 5 Down syndrome (DS), 7 Hyperdiploid (HeH), 3 iAMP21 and 5 others.

  Dataset EGAD00001005425

• Reference epigenome KNIH011 WGBS data generated from KEP study

  A KNIH011 Whole-Genome Bisulfite Sequencing(WGBS) paired end data for podocytes

  Dataset EGAD00001002759

• GoNL raw sequence data in fastq format

  Raw sequencing data for all samples in fastq format.

  Dataset EGAD00001000821

• Reference epigenome DB31_N_Alpha_WGBS data generated from KEP study

  A DB31_N_Alpha_WGBS paired end data for alpha cells(PSA-NCAM(-), pancreas)

  Dataset EGAD00001003472

• Fecal 16S UC metadata

  Metadata for fecal 16S-V4 rRNA gene sequence data from NCT02749630 ulcerative colitis patients.

  Dataset EGAD00001008822

• Reference epigenome ADMSC01_ChIP-Seq(H3K27me3) data generated from KEP study

  A ADMSC01_ChIP-Seq(H3K27me3) paired end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003854

• Reference epigenome SMC06_smRNA-Seq data generated from KEP study

  A SMC06_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003863

• Reference epigenome SMC04_smRNA-Seq data generated from KEP study

  A SMC04_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003861

• Reference epigenome SMC05_smRNA-Seq data generated from KEP study

  A SMC05_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003862

• Sporadic multiple meningioma Genomics

  Wes for 15 multiple meningioma samples from 6 individual

  Dataset EGAD00001008710

• Validation of a genome-wide polygenic score for body mass index in South Asians

  Genotyping data of BMI model markers generated using Global Screening Array (Illumina Inc.)

  Dataset EGAD00001015700

• Reference epigenome KNIH010 WGBS data generated from KEP study

  A KNIH010 Whole-Genome Bisulfite Sequencing(WGBS) paired end data for podocytes

  Dataset EGAD00001002758

• Reference epigenome ADMSC04_ChIP-Seq(H3K27me3) data generated from KEP study

  A ADMSC04_ChIP-Seq(H3K27me3) paired end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003857

• PDX WES

  2 patient-derived xenograph tumours, and associated normal blood samples. Duplicated samples for each gave 8 pairs of fastq files

  Dataset EGAD00001009657

• Variant Calls

  Variant Calls for all 97 consenting participants in the study.

  Dataset EGAD00001009971

• WES data for HCC patients from Y90+Nivolumab trial

  This dataset includes WES fastq files of pre-treatment and on-treatment samples from 29 patients

  Dataset EGAD00001010133

• DLP+ Single Cell Genomic Library A98193

  DLP+ Single Cell Genomic Library A98193 for Follicular lymphoma patient sample TFRI_Pair_6_FL

  Dataset EGAD00001010241

• DLP+ Single Cell Genomic Library A98205

  DLP+ Single Cell Genomic Library A98205 for Follicular lymphoma patient sample TFRIPAIR9_FL

  Dataset EGAD00001010243

• Reference epigenome SMC02_smRNA-Seq data generated from KEP study

  A SMC02_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003859

• DLP+ Single Cell Genomic Library A98297

  DLP+ Single Cell Genomic Library A98297 for Follicular lymphoma patient sample TFRIPAIR3_FL

  Dataset EGAD00001010248

• Reference epigenome KNIH008 mRNA-seq data generated from KEP study

  A KNIH008 mRNA-seq paired end data for adipocytes

  Dataset EGAD00001002174

• Reference epigenome ADMSC03_ChIP-Seq(H3K27me3) data generated from KEP study

  A ADMSC03_ChIP-Seq(H3K27me3) paired end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003856

• Reference epigenome ADMSC01_WGBS data generated from KEP study

  A ADMSC01_WGBS paired end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003878

• Reference epigenome ADMSC02_WGBS data generated from KEP study

  A ADMSC02_WGBS paired end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003879