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• 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

• 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

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

  A KNIH008 mRNA-seq paired end data for adipocytes

  Dataset EGAD00001002174

• Reference epigenome SMC02_smRNA-Seq data generated from KEP study

  A SMC02_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003859

• 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

• Reference epigenome ADMSC03_WGBS data generated from KEP study

  A ADMSC03_WGBS paired end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003880

• Reference epigenome ADMSC04_WGBS data generated from KEP study

  A ADMSC04_WGBS paired end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003881

• CRISPR screen M14, NCI-H3122

  Genome wide CRISPR screen was performed to find resistance to targeted drugs for melanoma and lung

  Dataset EGAD00001001389

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

  A KNIH007 miRNA-seq single end data for adipocytes

  Dataset EGAD00001002766

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

  A KNIH008 miRNA-seq single end data for adipocytes

  Dataset EGAD00001002767

• Dataset for the study of ulcerative colitis

  Whole exome sequencing and RNAseq data.

  Dataset EGAD00001005237

• Res1_H23_exp2_MC_13.07.22

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

  Dataset EGAD00001012234

• 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

• Exome chip analysis file

  This data set includes the following summary level data file used for the exome chip analysis: exome_chip.sv.assoc.txt: results from single variant association analysis in exome chip

  Dataset EGAD00010001187

• Immunochip_genotypes

  Intensity files for Immunochip genotypes from blood

  Dataset EGAD00010001495

• MS-Twins-EPIC-PBMCs

  Genome-wide DNA methylation profiles of MZ twins clinically discordant for MS generated using Illumina’s Infinium MethylationEPIC BeadChip assay (EPIC array)

  Dataset EGAD00010001618

• GR@ACE_StageI.v02

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

  Dataset EGAD00010001653

• Sequencing data for ICGC Oesophageal Adenocarcinoma tissue samples - pilot normal and pre/post-chemo

  Dataset EGAD00001000704

• uganda_autosomes

  Genotype data (in binary PLINK format) and imputed data (with merged 1000Gp3 and AGVP reference panel in GEMMA BIMBAM dosage format) for 1391 individuals from EMaBS in Entebbe, Uganda.

  Dataset EGAD00010002578

• The CCA Genome Core is responsible for archiving the sequencing data generated by the Cancer Centre Amsterdam (CCA).

  Dac EGAC00001000181

• tRCC UTSW 2024 Cohort

  This submission contains the WES and RNA-Seq raw data for the study titled 'Comparative Genomics of a Translocation Renal Cell Carcinoma Mouse Model Reveals Molecular Mechanisms of Tumorigenesis.

  Dac EGAC50000000105

• bfast CohortD OSPL

  ctDNA sample data (one sample per line) for BFAST Cohort D including sample-level summaries like tumor fraction (cTF).

  Dataset EGAD50000000147

• Walter and Eliza Hall Institute - University of Melbourne

  Data Access Committee for Gocuk, Lancaster et al. 2024: nanopore sequencing data of patients with choroideremia and X-linked retinitis pigmentosa, to determine X inactivation skew.

  Dac EGAC50000000301

• Whole exome sequencing data for two samples

  Whole exome sequencing of two human samples run on the Illumina HiSeq2500 platform. It contains two BAM files aligned to the refrence genoeme GRCh38.

  Dataset EGAD50000000825

• SMPaeds PanelSeq of cfDNA at relapse with UMIs

  Deep sequencing of a targeted panel of 67 paediatric cancer related genes for cfDNA with UMIs at approximately 1500xUMI coverage.

  Dataset EGAD50000000781

• Psoriasis PBMCs

  This DAC is for the purpose of controlling access to Psoriasis PBMC scRNA-seq data of the study Spermidine/spermine N1-acetyltransferase controls tissue-specific regulatory T cell function in chronic inflammation

  Dac EGAC50000000470

• HUG-CELL ASD Data Access Committee

  Data Access Committee for data generated by projects involving ASD and associated phenotypes by the Human Genome and Stem Cell Research Center (HUG-CELL), University of São Paulo

  Dac EGAC50000000475

• Picuris Pueblo Genomic Project – Ancient Dataset

  Ancient individual from Picuris Pueblo. Please refer to paper for more information.

  Dataset EGAD50000001246

• APOBEC Breast Cancer Whole Genome

  Five tumor-normal human breast cancer cases driven by APOBEC mutagenesis, including normal tissue, primary, and metastatic samples for each patient.

  Dataset EGAD50000001275

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

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

  Dataset EGAD00001004829

• TLR7 variants in human lupus patients

  Set of 2 bam files from patients affected with Lupus. Fastq alignments for exonic variants present in TLR7 gene.

  Dataset EGAD00001008534

• Reconstructed BCRs

  Computationally reconstructed B-cell receptor sequences (using BraCeR) from scRNA-seq data for all cells passing quality control.

  Dataset EGAD00001008456

• DLP+ Single Cell Genomic Library A98288

  DLP+ Single Cell Genomic Library A98288 for Follicular lymphoma patient sample TFRIPAIR2_FL

  Dataset EGAD00001010247

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

  A KNIH001 miRNA-seq single end data for islet cells

  Dataset EGAD00001002760

• scRNA PDX

  Single cell RNA-seq for PT1-derived PDX samples

  Dataset EGAD00001006134

• Leeds Paired Primary and Recurrent GBM RNAseq

  Whole transcriptome, strand-directional RNAseq data for paired primary dn recurrent samples from patients with glioblastoma

  Dataset EGAD00001005224

• Additional Sanger Sequencing and qPCR data to PSCNL samples

  Sanger sequencing and RT-qPCR data for validation used in Primary lymphomas of the central nervous system (PCNSL).

  Dataset EGAD00001008583

• Melanoma post mortem analysis

  We have been applying whole genome and transcriptome sequencing across metastases collected during post mortem. Herein we show the findings for the first such patient.

  Dataset EGAD00001005072

• Clinical data

  Clinical data for KATHERINE: Clinical data include Treatment Arm, Invasive Disease Free Survival (IDFS), Clinical Stage at Presentation, Hormone Receptor Status, Preoperative HER2 Directed Therapy, Pathological Node.

  Dataset EGAD00001008785

• Reference epigenome KNIH002 miRNA-seq data generated from KEP study

  A KNIH002 miRNA-seq single end data for islet cells

  Dataset EGAD00001002761

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

  A KNIH005 miRNA-seq single end data for islet cells

  Dataset EGAD00001002764

• Reference epigenome KNIH003 miRNA-seq data generated from KEP study

  A KNIH003 miRNA-seq single end data for islet cells

  Dataset EGAD00001002762

• Reference epigenome KNIH004 miRNA-seq data generated from KEP study

  A KNIH004 miRNA-seq single end data for islet cells

  Dataset EGAD00001002763

• ITS amplicon sequencing dataset

  This dataset contains raw ITS amplicon sequencing data for 719 sputum samples from individuals in Guangdong province, China.

  Dataset EGAD00001009817

• DLP+ Single Cell Genomic Library A98221A

  DLP+ Single Cell Genomic Library A98221A for Follicular lymphoma patient sample TFRIPAIR5_FL

  Dataset EGAD00001010245

• COMET TCRseq raw data

  This is the raw TCRseq data for the manuscript T cell receptor repertoire sequencing reveals chemotherapy-driven clonal expansion in colorectal liver metastases.

  Dataset EGAD00001010269

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

  A KNIH005 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002171

• Reference epigenome IPS02_N_NPC_mRNA-Seq data generated from KEP study

  A IPS02_N_NPC_mRNA-Seq paired end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003489

• Reference epigenome IPS03_N_ENeuron_smRNA-Seq data generated from KEP study

  A IPS03_N_ENeuron_smRNA-Seq single end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003503

• Reference epigenome IPS02_N_NPC_smRNA-Seq data generated from KEP study

  A IPS02_N_NPC_smRNA-Seq single end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003502

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

  A KNIH001 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002167

• Reference epigenome IPS05_X_NPC_mRNA-Seq data generated from KEP study

  A IPS05_X_NPC_mRNA-Seq paired end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003492

• RNA-seq data of LMS tumors

  This dataset contains paired fastq files for LMS tumor samples

  Dataset EGAD00001003828

• Oxford Human islet ATAC-seq dataset of 5 human islets

  ATAC-seq data for 5 non-diabetic human pancreatic islet samples

  Dataset EGAD00001003759

• Reference epigenome KNIH002 mRNA-seq data generated from KEP study

  A KNIH002 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002168

• Reference epigenome KNIH003 mRNA-seq data generated from KEP study

  A KNIH003 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002169

• Benchmark and validation of whole exome sequencing of a trio and singleton for mobile element detection.

  Illumina HiSeq paired-end exome sequencing of a trio and singleton.

  Dataset EGAD00001000883

• Illumina HiSeq X Ten

  Three files of patients 10, 11 and 13 with WGS done on Illumina HiSeq X Ten. For research purpose and authorised user only.

  Dataset EGAD00001003439

• Reference epigenome IPS06_X_ENeuron_mRNA-Seq data generated from KEP study

  A IPS06_X_ENeuron_mRNA-Seq paired end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003493

• Reference epigenome IPS03_N_ENeuron_mRNA-Seq data generated from KEP study

  A IPS03_N_ENeuron_mRNA-Seq paired end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003490

• Whole Genome Bi-Sulfite Sequencing files for RMS.

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

  Dataset EGAD00001004315

• Reference epigenome IPS05_X_NPC_smRNA-Seq data generated from KEP study

  A IPS05_X_NPC_smRNA-Seq single end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003505

• Reference epigenome KNIH006 mRNA-seq data generated from KEP study

  A KNIH006 mRNA-seq paired end data for beta cells

  Dataset EGAD00001002172

• Reference epigenome IPS06_X_ENeuron_smRNA-Seq data generated from KEP study

  A IPS06_X_ENeuron_smRNA-Seq single end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003506

• Reference epigenome KNIH004 mRNA-seq data generated from KEP study

  A KNIH004 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002170

• HIV exome pilot, exome data hs37d5

  exome sequence data for 57 HIV elite long term non-progressors and rapid progressors. Complete dataset of improved BAMs mapped to hs37d5 and including phenotype information.

  Dataset EGAD00001003345

• Reference epigenome KNIH006 miRNA-seq data generated from KEP study

  A KNIH006 miRNA-seq single end data for beta cells

  Dataset EGAD00001002765

• Melanoma - Exome sequencing

  whole exome sequencing of tumor- as well as PBMC-derived DNA of five melanoma patients for identification of naturally presented patient-specific neoepitopes

  Dataset EGAD00001002731

• Small RNA Sequencing across Diverse Biofluids Identifies Optimal Methods for exRNA Isolation

  We tested the efficiency and reproducibility of 10 RNA isolation methods on 5 different biofluids. Pooled human plasma and serum, bile and urine were used along with cell culture supernatants from human embryonic stem cells (hESC), primary neonatal rat ventricular myocytes (NRVM) and human cholangiocarcinoma cells (KMBC). 4 RNA isolation methods designed to isolate total RNA from bio fluids without enrichment of exRNA carriers were used. 6 methods that preferentially enriched for exRNA carriers were also compared. Small RNA libraries were constructed using the NEBNext small-RNA library prep. The efficiency and reproducibility of the different RNA isolation methods was compared using small-RNA seq.

  Study phs002143

• Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) Center for Environmental Health Disparities

  MADRES is a prospective pregnancy cohort based in urban Los Angeles, recruiting predominantly low income Hispanic mothers-to-be as part of a children's health study, funded by Environmental Influences on Child Health Outcomes (ECHO). Participants were measured for a number of chemical exposures and biomarkers. Two epigenetic markers will be available through dbGaP: miRNA expression in pregnancy (measured via Nanostring human assay, v3) and DNA methylation from pregnancy and children's cord blood (measured via EPIC array). Principal findings of this research described differences in miRNA expression profiles between early and late pregnancy, as well as miRNA expression affected by psychosocial stress.

  Study phs003194

• PAX4 loss of function increases diabetes risk by altering human pancreatic endocrine cell development

  The coding variant (p.Arg192His) in the transcription factor PAX4 is associated with an altered risk for type 2 diabetes (T2D) in East Asian populations. In mice, Pax4 is essential for beta cell formation but its role on human beta cell development and/or function is unknown. Participants carrying the PAX4 p.His192 allele exhibited decreased pancreatic beta cell function compared to homozygotes for the p.192Arg allele in a cross-sectional study in which we carried out an intravenous glucose tolerance test and an oral glucose tolerance test. In a pedigree of a patient with young onset diabetes, several members carry a newly identified p.Tyr186X allele. In the human beta cell model, EndoC-βH1, PAX4 knockdown led to impaired insulin secretion, reduced total insulin content, and altered hormone gene expression. Deletion of PAX4 in human induced pluripotent stem cell (hiPSC)-derived islet-like cells resulted in derepression of alpha cell gene expression. In vitro differentiation of hiPSCs carrying PAX4 p.His192 and p.X186 risk alleles exhibited increased polyhormonal endocrine cell formation and reduced insulin content that can be reversed with gene correction. Together, we demonstrate the role of PAX4 in human endocrine cell development, beta cell function, and its contribution to T2D-risk.

  Dataset EGAD50000000516

• Peripheral blood RNA sequencing of samples for a healthy cohort and a cohort with cancer patients

  RNA sequencing (fastq files) of white blood cells (WBCs) from healthy donors (n=376) and cancer patients (n=421) with different diagnoses, stages of disease and previously administered treatments, was performed. Samples from cancer patients were collected from the BostonGene clinical program; all patients provided written consent per IRB-approved protocols. Blood samples from healthy donors were purchased from multiple collection centers throughout the United States. Whole blood samples (3 ml) in K2-EDTA tubes received within 24 hours of collection at RT underwent red blood cell (RBC) lysis to isolate WBCs. Isolated WBCs for RNA sequencing were centrifuged at 300 x g for 5 minutes with a maximum of 10^6 cells per vial. The supernatant was removed, and the cells were resuspended in cold Homogenization Buffer (2% 1-Thioglycerol, Promega). Samples were then frozen at -80°C until extraction. RNA extraction was performed from frozen samples with Maxwell RSC simplyRNA Cells Kit (Promega) using the benchtop automated Maxwell RSC Instrument (Promega). Libraries were prepared with Illumina TruSeq® Stranded mRNA Library Prep (Poly-A mRNA; stranded). Libraries were sequenced on NovaSeq 6000 as Paired-End Reads (2x150) with targeted coverage of 50 mln reads.

  Dataset EGAD50000000414

• Whole-genome bisulfite sequencing for high-grade glioma

  We conducted whole-genome bisulfite sequencing to delineate base-resolution methylomes of gliomas harboring G34R mutation as well as those harboring K27M and no mutations in H3F3A. Comparative analysis of these three glioma subgroups revealed that G34 subgroup exhibited lower global methylation levels, comparable CpG island (CGI) methylation levels, and compromised hypermethylation of telomere-proximal CGIs, compared to the other two subgroups. Genomic regions specifically hypermethylated in G34 subgroup were enriched for CGIs. CGIs with G34V-mutated histone H3.3 in the glioma cell line KNS-42 were hypermethylated compared to those without it, suggesting a correlation between G34-mutated histone H3.3 in CGI hypermethylation.

  Study JGAS000197

• Development of hunanized mice for human hematopoisis and immunity research

  We obtained peripheral blood or bone marrow samples from 112 acute myeloid leukemia patients and examined sensitivity and resistance of the AML samples to molecular targeting drugs. By doing RNA sequencing using AML cells, we found heterogenous gene expression patterns in AML with different mutational landscapes. From the RNA sequencing, we identified anti-apoptosis, cell cycle progression, and immune-related signals as pathways up-regulated in AML cells as compared with normal CD34+ cells. From the pathway analyses, we further setup in vitro experiments to find vulnerability genes in clinically-aggressive AML. BIRC, BCL2, MCL1, AURKB, and KIF10 may be essential molecules for AML cell survival and proliferation.

  Study JGAS000253

• How are we funded?

  How are we funded? The EGA is provided infrastructure, administrative support, advisory input, and other relevant and necessary aspects by: CRG The EGA at the CRG is funded and supported by “La Caixa Foundation”, The Generalitat de Catalunya (Catalan Government), the Spanish Ministerio de Asuntos Económicos y Transformación Digital (Spanish Government) and the Instituto de Salud Carlos III. The Generalitat de Catalunya contributes with funding to the CRG and to the Barcelona Supercomputing Centre (BSC). The Barcelona Supercomputing Center is decisively contributing to EGA by providing the required compute, storage and networking resources, as well as key personnel for shared operations of EGA infrastructure. EMBL-EBI As part of the European Molecular Biology Laboratory (EMBL), the EGA at EMBL’s European Bioinformatics Institute receives funding from the governments of EMBL’s member states. The UK government, via UK Research and Innovation, also continues to support EMBL-EBI technical infrastructure, with EMBL supporting operational costs. A major source of funding for the EGA both at CRG and EMBL-EBI are competitive projects.The EGA resource continues to be funded through strategic engagement in collaborative partnerships and projects. Please see our projects page for more information. Other funders include the European Commission, the US National Institutes of Health, the Wellcome Trust, UK Research Councils, and our Industry Programme partners. Last updated on October, 2024.

  Documentation about/projects-and-funders/funders

• A personalised medicine approach for ponatinib-resistant chronic myeloid leukaemia

  Chronic myeloid leukaemia (CML) is characterised by the presence of a fusion driver oncogene, BCR-ABL1, which is a constitutive tyrosine kinase. Tyrosine kinase inhibitors (TKIs) are the central treatment strategy for CML patients and have significantly improved survival rates, but the T315I mutation in the kinase domain of BCR-ABL1 confers resistance to all clinically approved TKIs, except ponatinib. However, compound mutations can mediate resistance even to ponatinib and remain a clinical challenge in CML therapy. Here, we investigated a ponatinib-resistant CML patient through whole genome sequencing (WGS) to identify the cause of resistance and to find alternative therapeutic targets.

  Study EGAS00001001150

• Sequencing data of multiple sarcoma samples for personalized medicine and endotype identification

  Sequencing data derived from multiple patient samples was used to guide multiple patient-specific experiments and identify potential therapeutic options. These diseases are rare and high-risk (undifferentiated pleomorphic sarcoma, rhabdomyosarcoma) and thus have few available validated clinical options. This leaves the population of patients with few options if, and when, standard therapy fails to cure or control disease. An aspect of the personalize therapy design included the identification of broader disease endotypes (a combination of genetic and clinical data describing patient status) to identify subgroups of the individual disease that could better define treatments and could segment patient populations for deeper preclinical research.

  Study EGAS00001003981

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

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

  Study EGAS00001000327

• Understanding Self- Organising Capacity of Stem Cells during Implantation and Early Post-implantation Development in vitro and in vivo: Implications for Human Development (2020-04-20)

  The aim of this project is to differentiate human embryonic stem cells to an extra-embryonic fate, specifically the hypoblast. This is of uttermost importance given the current lack of human hypoblast stem cells. We hypothesized that the pluripotent characteristics of the starting human embryonic stem cell population may dictate the competency for extra-embryonic cell fate specification. Based on this hypothesis and using human embryonic stem cells maintained in different naïve-like culture regimes, we have now developed conditions that allow the differentiation of human embryonic stem cells to a stable GATA6+ SOX2- population. This suggests that these cells may be putative human hypoblast stem cells. To validate this finding here we propose to perform RNA sequencing experiments of the differentiated human embryonic stem cells. By comparing their RNA expression profile to the single cell sequencing data of the human embryo that we are currently generating, we will be able to determine the identity of our GATA6+ SOX2- cells, and establish whether they represent the in vivo human hypoblast. This dataset contains all the data available for this study on 2020-04-20.

  Dataset EGAD00001006056

• A spatial human thymus cell atlas mapped to a continuous tissue axis

  T cells develop from circulating precursor cells, which enter the thymus and migrate through specialised sub-compartments that support their maturation and selection. In humans, this process starts in early fetal development and is highly active until thymic involution in adolescence. To map the micro-anatomical underpinnings of this process in pre- and early postnatal stages, we established a novel quantitative morphological framework for the thymus, the Cortico-Medullary Axis, and used it to perform a spatially resolved analysis. By applying this framework to a curated multimodal single-cell atlas, spatial transcriptomics, and high-resolution multiplex imaging data, we demonstrate establishment of the lobular cytokine network, canonical thymocyte trajectories and thymic epithelial cell distributions within the first trimester of fetal development. We pinpoint tissue niches of thymic epithelial cell progenitors and distinct subtypes associated with Hassall’s corpuscles and uncover divergence in the timing of medullary entry between CD4 vs. CD8 T cell lineages. These findings provide a basis for a detailed understanding of T lymphocyte development and are complemented with a holistic toolkit for cross-platform imaging data analysis, annotation, and Organ Axis construction (TissueTag), which can be applied to any tissue.

  Dataset EGAD00001015384

• Field effect of healthy and diseased livers (2019-04-08)

  Recent work in the Campbell group has revealed somatic mutations present in normal, non-cancerous human skin. A subset of the mutations conferred selective advantages to the host cells, leading to clonal expansions and raising the risk for future cancer development. Capturing such somatic mutations in normal tissue is important to advance our understanding about carcinogenesis and could provide prospective medical insights. In this project, our goal is to detect somatic mutations in normal (pre-cancerous) liver tissue. Using Laser Microdissection technology, we will dissect individual liver lobules from patient samples and submit these to sequencing. For each patient sample, we aim to sequence multiple lobules to characterise the mutagenic burden. Samples will be taken from patients with different liver disease aetiologies, including alcoholism and obesity, with a view on distinguishing the prevalent mutation types occurring in each disease context. We will perform targeted sequencing, initially using the WTSI cancer panel. Later we aim to use a novel bait set that captures both cancer genes as well as genes relevant to the non-cancerous samples (ie. genes implicated in hereditary disorders, immune sequences). . This dataset contains all the data available for this study on 2019-04-08.

  Dataset EGAD00001004941

• Single-cell level characterization of B cell depletion and repopulation following rituximab in systemic lupus erythematosus

  Rituximab, a CD20+ B cell depletion therapy, is frequently used in the treatment of systemic lupus erythematosus (SLE). However, variability in patient response highlights the need for a deeper understanding of the underlying immune cell dynamics of B cell depletion and repopulation. In this study, we conducted longitudinal single-cell profiling of nine SLE patients treated with rituximab from pretreatment to up to 15 months post-treatment. These were compared to eight healthy controls. We profiled PBMCs via 10X Genomics single-cell RNA, surface protein (CITE-seq), B cell receptor (BCR), and T cell receptor (TCR) sequencing and sequenced bulk BCR repertoires in parallel. For single cell sequencing, 10 pools were created with an equal number of cells from 4 samples each. Samples collected at different timepoints from the same patient were distributed across different pools to allow demultiplexing of individuals using genotypes captured from the scRNA-seq data. Libraries were pooled using a ratio of GEX:CITE:TCR:BCR=9:2:1:1 and were sequenced across two lanes of the NovaSeq 6000. For bulk BCR sequencing, individual samples were demultiplexed using primer barcodes and paired-end BCR amplicon reads were merged prior to submission; data are provided as unmapped single-end reads.

  Dataset EGAD00001015817

• BLUEPRINT EpiVar ChIP-seq in lineage specifying transcription factors

  The BLUEPRINT project is a large-scale project investigating epigenetic mechanisms involved in blood formation, in health and disease. The human variation workpackage (WP10) of the project seeks to characterize the effect of common sequence variation on the epigenome status of a cell. To do this, the project will use highly purified blood cells to minimise "experimental noise" and therefore enhance the power to discover modest effects. Two peripheral blood cell types, the CD14+CD16- monocyte (an important central orchestrator of adaptive immunity and a bridge between innate and adaptive immunity) and the CD65+CD9- neutrophilic granulocyte (the frontline cell for innate immunity) have been selected for this purpose. The two types of cells will be obtained at high purity from adult blood (AB) of 200 healthy males and females, respectively. Cells will be purified by using already validated and fully operational protocols that are based on density gradient centrifugation of the buffy coat obtained from whole blood, followed by magnetic bead-based purification using monoclonal antibodies against Cluster of Differentiation (CD) lineage-specific cell surface markers. This data set contains functional genomics data for gene expression and chromatin state.

  Dataset EGAD00001004571

• MorphoITH: A Framework for Deconvolving Intra-Tumor Heterogeneity Using Tissue Morphology

  This study introduces MorphoITH, a framework for deconvolving intra-tumor heterogeneity (ITH) in clear cell renal cell carcinoma (ccRCC) based on tissue morphology from histopathological slides. The study tested MorphoITH's ability to capture different aspects of morphology and to reflect genetic ITH. The data deposited here is derived from a morphology guided multi-regional sequencing dataset used to study the relationship between morphology and genetic evolution. This dataset consists of whole exome sequencing of tumor from multiple sites and adjacent normal samples which were collected from two patients treated in affiliated hospitals of the University of Texas Southwestern Medical Center (UTSW), including Parkland Hospital.

  Study EGAS50000001064

• How to upload Crypt4GH files

  Uploading files Users who hold an ega-box-XXX account can upload files using either INBOX or FTP. Users who have a Submitter role associated with their email will only be able to upload files using INBOX. Before uploading your files, please make sure that any files that will be uploaded to EGA do not use special characters in their naming convention, such as # ? ( ) [ ] / \ = + < > : ; " ' , * ^ | &. This can cause issues with the archiving process, leading to problems for end users. The EGA is a shared, public service with limited storage. To manage the available resources, we enforce a limit of 10TB per submission account at any one time. If you exceed this limit, a “permission denied” message will be displayed. This will prevent you from uploading more files, but connecting to your inbox.For submissions larger than 10TB, please perform uploads in 10TB batches: register all the metadata and then finalise the submission. Upload the next batch of files and repeat the same metadata registration and finalisation process until you have completed the file upload. Further information can be found in the SP documentation. INBOX FTP The INBOX is only compatible with files encrypted using the Crypt4gh tool Before uploading If you are not a registered EGA user, you will first need an EGA user account. Please note that it may take a few days for your account to be activated, as it needs to be vouched for by the EGA Helpdesk. Once your account is validated, you will be able to request a submitter role. [Optional] Meanwhile, you can create and add your public key to your EGA account profile. This option is not available for old submission accounts (e.g., ega-box-NNN). As soon as you have been granted a submitter role, you will be able to connect with your username and password to the EGA inbox using the SFTP protocol. If you have also registered a public key in your profile, you can also connect using this key. To upload files to your account, you can use the graphical user interface (GUI) or the command line. Graphical User Interface (GUI)We recommend using FileZilla, a free, open-source FTP client. However, you can use any other GUI that allows connecting over the SFTP protocol. For FileZilla as your GUI, follow these steps to upload files: Create a new connection in Site Manager (File > Site Manager) and select the following options (Figure 1): Protocol: SFTP - SSH File Transfer ProtocolHost: __EGA_INBOX_DOMAIN__Logon Type: Key fileUser: your EGA usernameKey file: Path/to/your/private_keyFigure 1: Process of establishing a new connection to __EGA_INBOX_DOMAIN__ using a key file as the logon method in FileZilla. The figure showcases the FileZilla version 3.52.2 operating on IOS v11.2.3. By following the depicted steps, users can create a secure and efficient connection to the inbox, ensuring seamless data transfers.Click Connect, and you will log in remotely to your home directory. You can think of this folder as a storage "in the EGA cloud" in which you will add your files for the EGA. The uploading area has three folders:To-encrypt: Files uploaded in this folder will be encrypted automatically on the fly.Encrypted: Files uploaded in this folder must already be encrypted with Crypt4gh. Upload your files here if your connection is unstable or you have problems completing the upload into-encrypt.Etc: This folder contains two files that allow the server to show you your username and group instead of some internal numbers. Please do not upload files here; otherwise, you will obtain a permission denied error. Find the files you want to upload by browsing your local storage (left side of your screen in FileZilla). Select all the files you want to upload, then right-click on them and select Upload (Figure 2). Figure 2: Step-by-step process of manually uploading files to __EGA_INBOX_DOMAIN__ using FileZilla, with FileZilla version 3.52.2 operating on IOS v11.2.3. The figure demonstrates how users can transfer data from their local storage to the "EGA cloud" by following the depicted steps Please note that regardless of which folder you upload your files in, both folders (to-encrypt, encrypted) will point to the same path (/) (Figure 3). Therefore, you will see your files in both folders. Figure 3: Both folders, to-encrypt and encrypted, point to the same path (/)" If your connection is unstable, please encrypt your files first using Crypt4gh. Then upload them to the ‘encrypted’ folder. The example above shows how to connect to __EGA_INBOX_DOMAIN__ using the private key. However, if you prefer to log in using your credentials, you can do so. Please go to the Frequently Asked Questions (FAQs)  for more information. SFTP command line To upload files securely to your private area of the EGA, you can use SFTP(Secure File Transfer Protocol) with your favorite FTP client. Here's what you need to know to get started: Connect to the target host __EGA_INBOX_DOMAIN__. This is the new hostname for the EGA SFTP service. Log in with your EGA username and key files (or password). Upload files to your private EGA inbox to ensure that only you can access the files. By following these steps, you can securely upload your files to the EGA for safe storage and sharing. Using the SFTP command line client in Linux/Unix Open a terminal and type sftp username@hostnameEnter your EGA passwordTo see a list of available SFTP commands, type helpsftp> put – Upload filesftp> get – Download filesftp> cd path – Change remote directory to ‘path’sftp> pwd – Display remote working directorysftp> lcd path – Change the local directory to ‘path’sftp> lpwd – Display local working directorysftp> ls – Display the contents of the remote working directorysftp> lls – Display the contents of the local working directoryType the "put" command to upload files. For example: put *.bamUse the bye command to close the connection (SFTP session). After uploading- Once you have uploaded files to the inbox, please bear in mind that the checksum needs to be calculated, which can take up to two days. You will only be able to link your files to a run/analysis once the encrypted checksum has been calculated.- When linking your files to the 'Run' or 'Analysis', ensure that the file name matches the file path '/name' in the INBOX folder.- Please delete the files from your SFTP INBOX after all the runs/analyses have been registered and files are ingested (SP > Files > Files ingested). This will clear your inbox space an allow you to upload more files. This will also prevent the files from reappearing in your Submitter Portal inbox. Frequently Asked Questions Specific to the inbox What username should I use to log in to my inbox? The authentication process for logging in to the EGA website, as well as accessing your inbox and outbox, requires the use of your username.  If you have forgotten your registered username, please contact our Helpdesk team for assistance. How are checksums calculated in your inbox? If you encrypt the file beforehand and upload it to the "encrypted" folder, the unencrypted checksum will not be calculated until the file is ingested (i.e., until it is used in a run/analysis). If the file is uploaded to the "to-encrypt" folder, then both checksums are calculated.Please bear in mind that after files have been uploaded to the inbox, the checksum must be calculated, which can take from a few hours to two days.  Specific to using keys to authenticate Can I access one EGA account from different devices? Yes, you can access your account from different devices by linking several public keys to your EGA account. Each device can generate a unique public-private key pair, and the corresponding public keys can be linked to the same account. This way, you can use different public keys on different devices and still have access to the same account and data. I have several keys and I don't remember which one is which When generating SSH keys, it's a good practice to add a comment using the -C flag. This will allow you to add a descriptive tag to your key, making it easier to identify later on. Here's an example command that generates an SSH key with a comment: ssh-keygen -t ed25519 -C work-pass In this example, we're generating an ed25519 SSH key with the comment work-pass. Once you have multiple keys with different comments, you can use the comments to easily identify each key. To view the comments for your existing SSH keys, you can use the following command: ssh-keygen -l -f /path/to/key This will display the key fingerprint and the associated comment. By checking the comments, you should be able to identify which key is which. What if I can't find my SSH keys for uploading files with a key file, and how can I use new keys? If you can't find your SSH keys, don't worry - you can make new ones. To do this, open your terminal or command prompt and type a command to make a new SSH key. You can pick a name for the key, and choose a password to keep it safe. After making the key, you can add the new key to your account or server where you want to upload files using the key file. This usually involves copying and pasting the key's "public" (e.g. file.pub) part to the right place. If you lose track of the key again, just make a new one and add it again. Keep in mind that SSH keys belong to you and your computer, so if you switch computers or accounts, you'll need to make new keys. I don't want to type the passphrase every time I use the key. What can I do? You can use an ssh-agent to avoid typing the passphrase every time you use the key. An ssh-agent is a program that stores your private keys in memory and provides them to ssh when needed. You can add your key to the ssh-agent using the command ssh-add followed by the path to your key file.Here's an example of the steps to follow: Open a terminal window.Start the ssh-agent by typing the command eval $(ssh-agent).Add your key to the ssh-agent by typing the command ssh-add [key filepath]. For instance, if your key file is located in the home directory with the name mykey, the command will look like this: ssh-add ~/mykey After adding your, key to the ssh-agent, you should be able to use ssh without having to enter your passphrase every time. Can I use my password for authentication (without my private key)? If you prefer to use your username and password for authentication instead of your private key, you can still do so. When using a Graphical User Interface (GUI) such as FileZilla, you can select Ask for password as your Logon Type (Figure 3). This option will prompt you to enter your password when you click Connect, instead of using your private key. Figure 3: This option will prompt you to enter your password when you click "Connect", instead of using your private key. Figure 3: Process of establishing a new connection to __EGA_INBOX_DOMAIN__ using your password as the logon method in FileZilla. The figure showcases the FileZilla version 3.52.2 operating on IOS v11.2.3. By following the depicted steps, users can create a secure and efficient connection to the inbox, ensuring seamless data transfers. It's worth noting that using a password for authentication can be less secure than using an SSH key, as passwords can be more easily compromised through various means. However, if you choose to use your password for authentication, selecting "Ask for password" as your Logon Type is a good way to do so securely via a GUI. Why is it better to use my key and not my password? SSH keys for authentication is generally considered to be more secure and convenient than using passwords. SSH keys are more difficult to crack than passwords, and they can be restricted to specific users and machines, giving you more control over access. Once you set up your SSH keys, you can use them to authenticate quickly and easily, without having to enter a password every time. This makes automation of tasks, such as uploading encrypted files, much simpler. Additionally, SSH keys provide better logging, allowing you to keep track of who is accessing your systems and when. All in all, using SSH keys is a good practice for improving security and convenience in your authentication process.

  Documentation submission/data/uploading-files/inbox

• Dataset of methylation data from whole blood DNA

  This dataset includes raw data (.idat) for the Illumina Human450k beadchip and methylation levels (.txt files). Methylation level were treated for normalization and background substraction. We removed probes with at least one of the following characteristics: (1) weak signal (p &gt; 0.01) (2128 CpG sites), (2) SNP-enriched sites (4100 sites), (3) out of a CpG context (not on a CG) (3149 sites), or (4) located on sex chromosomes (11,129 sites). A total of 465,071 CpG sites were analyzed initially. Signal was then normalized, first by scaling to the internal controls using the methylumi R package, then by applying the method of subset-quantile within array normalization (SWAN) implemented in the minfi R package.

  Dataset EGAD00010001593

• Genome-wide genotyping data and exome sequencing of 100 European-descent and 100 African-descent Belgians used in the EGAS00001001895 study

  The 100 European-descent (EUB) and 100 African-descent (AFB) Belgians studied were genotyped for a total of 4,301,332 SNPs on the Illumina HumanOmni5-Quad BeadChips. Whole-exome sequencing was carried out for the same 200 individuals with the Nextera Rapid Capture Expanded Exome kit, on the Illumina HiSeq 2000 platform, with 100-bp paired-end reads. This kit delivers 62 Mb of genomic content per individual, including exons, untranslated regions (UTR), and microRNAs. Omni5 and exome datasets were merged, yielding a concordance rate between platforms of 99.93%.

  Dataset EGAD00010001131

• NPC genome-wide human SNP array data for HongKong population

  nasopharyngeal carcinoma genome-wide human SNP array data for 423 NPC cases and 573 controls

  Dataset EGAD00010002298

• Summary Statistics GWAS SSNS

  Column 1 rsid: SNP identifier;Column 2 chromosome: name of chromosome on which the SNP is located;Column 3: position: base pair position on the chromosome;Column 4 minor_test_allele: the base that constitutes the minor allele;Column 5 major_allele: the base that constitutes the major allele;Column 6 maf: the frequency of the minor allele, indicated as a fraction of 1;Column 7 allele_freq_cases: the minor allele frequency in cases;Column 8 allele_freq_controls: the minor allele frequency in controls;Column 9 regression_pvalue: the p-value for the difference in allele frequency between cases and controls;Column 10 odds_ratio: the odds ratio, as calculated using logistic regression under an additive model with adjustment for the first ten principal components of ancestry

  Dataset EGAD00010002316

• Geisinger Health System - MyCode, eMERGE III Exome Chip

  DNA samples were obtained from participants of the Geisinger MyCode biobank. Phenotype data to determine case or control status for abdominal aortic aneurysm or extreme obesity were derived from Geisinger electronic medical record data through an enterprise data warehouse. Samples were genotyped using the Illumina Human CoreExome-12 v1.0 Array. Genotype data were filtered using standard quality control measures.

  Study phs000957

• High-throughput single-cell DNA sequencing of acute myeloid leukemia tumors with droplet microfluidics

  We developed a novel microfluidic platform to automatically barcode genomic DNA from individual cancer cells. Using this approach, we sequenced acute myeloid leukemia (AML) tumors targeting up to 62 loci relevant for the disease from thousands of cells. We predict that our platform will enable analysis of heterogeneity in AML and improve stratification and therapy selection.

  Study phs001627

• Substance Dependence GWAS in European- and African - Americans

  This study includes SSADDA (Semi Structured Assessment for Drug Dependence and Alcoholism) assessed subjects (mostly unrelated, but including some affected sibling pairs) recruited in the course of several substance dependence genetics projects. The sample includes 1,531 self-reported African-American (AA) subjects, 1,339 self-reported European-American (EA) subjects, and 2 who reported race as "unknown" or "missing".

  Study phs000952

• Feasibility and Clinical Utility of Whole Genome Profiling in Pediatric and Young Adult Cancers

  Comprehensive Whole Genome Sequencing and Whole Transcriptome (RNASeq) analyses from 114 pediatric patients at Memorial Sloan Kettering. This dataset was used as a prospective clinical utility and feasibility study. By using our Isabl infrastructure for precision medicine, we developed a 2-week end-to-end pipeline to analyze cfDNA, WGS and WTS, which we refer to as cWGTS.

  Study phs002620

• Kids First: Pediatric Research Project on Adolescent Idiopathic Scoliosis

  Both childhood cancers and structural birth defects are critical and costly conditions associated with substantial morbidity and mortality. Elucidating the underlying genetic etiology of these diseases has the potential to profoundly improve preventative measures, diagnostics, and therapeutic interventions. For the current study, families with inherited adolescent idiopathic scoliosis were included. Both affected and unaffected family members were included.

  Study phs001410

• The Genomic Complexity of Primary Human Prostate Cancer

  Seven primary prostate cancers and their paired normal counterparts were DNA-sequenced on a massively parallel sequencing platform. This approach was taken to investigate the genomes for mutations and genomic alterations that would be undetectable by lower-resolution methods. As result, tumors containing chains of balanced rearrangements that occurred within or adjacent to known cancer genes were discovered.

  Study phs000330