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• Colon cancer targeted sequencing study contaning WBCs, primary tumor tissue and plasma samples

  In the setting of localized colon cancer (CC), circulating tumor DNA (ctDNA) monitoring in plasma has shown potential for detecting minimal residual disease (MRD) and predicting higher risk of recurrence. With the tumor-only sequencing approach, however, germline variants may be misidentified as somatic variations, precluding the possibility of tracking in up to 11% of patients due to a lack of known somatic mutations. In this study comprising 148 prospectively recruited localized CC patients, a custom 29-gene panel was utilized to sequence both tumor tissue and matched white blood cells (WBCs) to enhance the accuracy of sequencing results. Performing targeted sequencing of paired tumor tissue and WBCs samples detected additional somatic mutations and increased the number of patients eligible for MRD tracking in plasma, although MRD detection sensitivity was not increased. Furthermore, the germline testing approach revealed the presence of pathogenic germline variants, thereby helping identify patients at elevated risk of hereditary cancer syndromes.

  Study EGAS50000000059

• Methylome sequencing of cell-free DNA and RRBS of solid tissue

  Early cancer detection by cell-free DNA (cfDNA) faces multiple challenges: low fraction of tumor cfDNA, molecular heterogeneity of cancer, and sample sizes not sufficient to reflect diverse patient population. We develop a cancer detection approach to address these challenges. It consists of an assay, cfMethyl-Seq, for cost-effective sequencing of cfDNA methylome (with >12-fold enrichment over whole genome bisulfite sequencing in CpG islands), and a computational method to extract methylation information and diagnose patients. Applying our system to 408 colon, liver, lung, and stomach cancer patients and controls, at 97.9% specificity we achieve 80.7% and 74.5% sensitivity in detecting all-stage and early-stage cancer, and 89.1% and 85.0% accuracy for locating tissue-of-origin of all-stage and early-stage cancer, respectively. Our approach cost-effectively retains methylome profiles of cancer abnormalities, thereby permitting the classification models to learn new features as training cohorts grow, and expanding their scope to other cancer types.

  Study EGAS00001006020

• Accumulation of copy number alterations and clinical progression across advanced prostate cancer

  The burden of copy number alterations positively associated with radiologically-evident distant metastases at diagnosis (P=0.00006) and showed a non-linear relationship with clinical outcome on univariable and multivariable analysis, characterised by a sharp increase in the relative risk of progression (P=0.003) and death (P=0.045) for each unit increase, stabilising into more modest increases with higher burdens. This association between copy number burden and outcome was similar in each of the four metastatic states. Copy number loss occurred significantly more frequently than gain at the lowest copy number burden quartile (q=4.1X10-6). Loss of segments in chromosome 5q21-22 and gains at 8q21-24, respectively including CHD1 and cMYC, occurred more frequently in cases with higher copy number alteration (for either region: Kolmogorov–Smirnov distance, 0.5; adjusted P<0.0001). Copy number alterations showed variability across tumor regions in the same prostate. This variance associated with increased risk of distant metastases (Kruskal-Wallis test P=0.037).

  Study EGAS00001006251

• Clonally resolved single-cell multi-omics identifies routes of cellular differentiation in acute myeloid leukemia

  In this study we generated single-cell whole transcriptome and surface marker expression data for 24 samples of 19 AML patients as well as one healthy donor. We followed the CITEseq protocol with the 3' 10x Genomics scRNAseq kit version 3.1 To increase the coverage of the mitochondrial genome we generated mitochondrial libraries following a protocol termed Optimized 10x. Based on TAPseq, we generated libraries to increase the coverage of selected nuclear SNVs. Exome sequencing was generated for 15 patients to identify nuclear variants. Bulk ATAC was obtained for 9 samples to facilitate the discovery of mitochondrial SNVs. MutaSeq (modified version of SmartsSeq2) was performed on cells from 3 patients. Targeted DNAseq from single-cell derived colonies was generated for 1 patient.

  Study EGAS00001007078

• RNA sequencing and whole-genome mate-pair sequencing of osteosarcoma

  This data set includes 72 mate pair sequenced osteosarcomas (36 as part of a discovery cohort and 36 as part of a validation cohort). It also includes RNA-sequencing data on 67 osteosarcomas (mostly overlapping with the above mate pair sequenced cases) and 13 osteoblastomas used as controls for gene expression levels.

  Dataset EGAD00001005307

• HV31 - De novo assembly of eight immune system regions

  De novo assembly of eight immune system regions for individual HV31, generated using a multi-platform pipeline. A full description of the generation of these assemblies can be found at https://doi.org/10.1101/2021.02.03.429586.

  Dataset EGAD00001007050

• Single Cell DNA amplicon sequencing of 12 B-ALL patients (at diagnosis and during treatment)

  This dataset contains single cell DNA amplicon sequencing of 12 B-ALL patients. For all patients a diagnosis sample was processed, while 4 patients were also followed up during treatment, summing up to a total of 23 samples. Mutations were called in the predefined set of amplicons.

  Dataset EGAD00001006955

• Sant Joan de Déu Research Institute (IRSJD)

  Sant Joan de Déu Research Institute (IRSJD) institutional Data Access Committee. The objective of the DAC is to manage, review and assess datasets access requests. The DAC is managed by the Knowledge Management Unit, and an adhoc committee per dataset will be formed to take into consideration each access request. Contact the Knowledge Management Unit at gestio.coneixement@sjd.es

  Dac EGAC50000000253

• Error-Corrected Next-Generation Sequencing Rectal Mucus

  Error-corrected next-generation sequencing of rectal mucus samples from patients suspected to have colorectal cancer. This is 1 of 4 sequencing experiments on the same sample type.

  Study EGAS50000001398

• 71 Whole-exome sequencing of Esophageal Squamous Cell Carcinoma on Chinese Patients

  Esophageal Squamous Cell Carcinoma (ESCC) is one of the deadliest cancers worldwide. We performed 71 Whole-exome sequencing of Esophageal Squamous Cell Carcinoma on Chinese Patients.

  Study EGAS00001001475

• Role_of_HPV_and_Interferon_in_APOBEC_mutational_signature

  The aim of this project is to test whether HPV oncogenes and /or interferon induce the APOBEC mutational signature in vitro, and to test the role of APOBEC3A in this process.

  Study EGAS00001002988

• 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 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 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

• 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 OB56_N_PreA_smRNA-Seq data generated from KEP study

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

  Dataset EGAD00001003495

• 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 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

• 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

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

  RNA sequencing data for the PDOX model EPD-613FH

  Dataset EGAD00001003573

• RNASeq_EGAS00001001306

  RNASeq sequencing. Each library was sequenced using TruSeq SBS Kit v3-HS, in paired-end mode with a read length of 2 × 76 bp. We generated more than 20 million paired-end reads for each sample in a fraction of a sequencing lane on HiSeq2000 (Illumina Inc.) following the manufacturer’s protocol. Image analysis, base calling and quality scoring of the run were processed using the manufacturer’s software Real Time Analysis (RTA 1.13.48) and followed by generation of FASTQ sequence files.

  Dataset EGAD00001001443

• BAM files ChIP-Seq

  Data from the paper Context-specific Effects of TGFβ/SMAD3 in Cancer Are Modulated by the Epigenome. Tufegdzic et al, Cell Reports 2015

  Dataset EGAD00001001669

• Somatic Mutations in Epilepsy: Whole Genome Sequence Analysis of Single Neurons

  Low-coverage whole-genome sequencing was performed on single-cells from resected brain tissues obtained from an individual with hemimegalencephaly (HMG) along with a somatic copy number variant (sCNV) of chromosome 1q in order to characterize the sCNV at the single cell level. Single neuronal nuclei and non-neuronal nuclei were isolated by fluorescence-activated cell sorting (FACS) using NeuN immunoreactivity to identify neurons. 46 neurons and 30 non-neuronal cells, as well as a batch of 10 and a batch of 100 cells from each group, were genome amplified using multiple displacement amplification (MDA) and sequenced on an Illumina HiSeq 2000 sequencer. Thirty-two samples were multiplexed into each HiSeq lane for single-end 50 bp runs, obtaining ~5 million pass-filter reads per sample.

  Study phs002615

• Single-Cell Atlas of Human Liver and Blood Immune Cells Across Fatty Liver Disease Stages

  The goal of the study was to investigate immunological correlates of human fatty liver disease progression. We generated a single-cell RNA-seq (scRNA-seq) atlas of immune cells from liver fine needle aspirates (FNAs) and paired peripheral blood mononuclear cells (PBMCs) from a full-spectrum Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD)/Metabolic Dysfunction-Associated Steatohepatitis (MASH) human cohort, using the 3' 10X Genomics platform. Using computational analyses to assess changes in cellular frequencies and function, we found heightened immunoregulatory programs with profibrotic potential alongside a shift from antigen-specific to natural killer cytotoxic functions in the liver with MASH progression. Available data include raw single-cell transcriptomic data (FASTQ files) for each sample type (PBMC, FNA) per patient alongside clinical and histological measurements of fatty liver disease.

  Study phs004044

• MPM cell lines

  RNA-sequencing data for 12 MPM cell lines treated with 0.1% DMSO or 1 µM palbociclib for 9-10 days. Experiment was performed in duplicates for sensitive cells (MPM08, MPM21, MPM38, MPM57, MPM59, Meso11, Meso13, Meso34 and Meso56) and only once for resistant cells (MPM31, MPM34 and MPM36) except for Meso11 which was done in triplicates. For Meso11, Meso13, Meso34 and Meso56, a drug washout of 48 hours was also performed. 4 MPM cell lines (MPM31, MPM34, MPM59 and MPM66) were also analyzed in untreated condition. 2 FASTQ files for each sample (n=56) (paired)

  Dataset EGAD00001008741

• Genomics-based personalized oncology in cancer of unknown primary (CUP, project H021)

  To perform a comprehensive genomic characterization of 70 patients suffering from cancer of unknown primary (CUP) we used whole-exome, whole-genome, transcriptome and methylome analysis. We detected a substantial mutational heterogeneity with genes most commonly affected by SNVs, indels and fusions being TP53, TTN, MUC16, ABCA13, COL6A3, KRAS, LRP1B, XIRP2 and CSMD3. The most common fusion involved FGFR2, the most common focal deletion affected CDKN2A. A molecular tumor board recommended genomics-based therapies in 56/70 (80%) patients which were applied in 20/56 (35.7%) cases. Entity predictions based on transcriptome and methylome data could be made in up to 62/70 (88.6%) cases but were conclusive in only 16/48 (33.3%) cases. Germline analysis revealed 6 (likely) pathogenic mutations in 5 patients. Recommended therapies translated into a mean PFS2/1 ratio of 3.61 (median=2.25) with a median PFS1 of 89 days (n=17) compared to a median PFS2 of 182.5 days (n=20). Our data emphasize the clinical benefit of comprehensive genetic approaches in diagnostic and therapeutic management and underline the need for innovative, mechanism-based clinical trials in this heterogeneous group of diseases.

  Study EGAS00001004786

• 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

• 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

• 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 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 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 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 SMC01_smRNA-Seq data generated from KEP study

  A SMC01_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003858

• 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 SMC03_smRNA-Seq data generated from KEP study

  A SMC03_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003860

• 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 SMC06_smRNA-Seq data generated from KEP study

  A SMC06_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003863

• 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 SMC08_smRNA-Seq data generated from KEP study

  A SMC08_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003865

• 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 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

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

  A KNIH007 mRNA-seq paired end data for adipocytes

  Dataset EGAD00001002173

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

  A KNIH008 mRNA-seq paired end data for adipocytes

  Dataset EGAD00001002174

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

  A KNIH009 mRNA-seq paired end data for preadipocytes

  Dataset EGAD00001002175

• 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 KNIH011 WGBS data generated from KEP study

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

  Dataset EGAD00001002759

• 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

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

  A KNIH009 miRNA-seq single end data for preadipocytes

  Dataset EGAD00001002768

• 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

• 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

• The dataset of Southeast Borneo individuals (Banjar and Ngaju ethnic groups) was used as comparative data to determine the Asian parental population of the Malagasy. Our study found strong support for an origin of the Asian ancestry of Malagasy among the Banjar.

  Malagasy genetic diversity results from an exceptional proto-globalisation process that took place over a thousand years ago across the Indian Ocean. Previous efforts to locate the Asian origin of Malagasy have highlighted Borneo broadly as a potential source, but so far no firm source populations have been identified. Here, we have generated genome-wide data from two Southeast Borneo populations, the Banjar and the Ngaju, together with published data compiled from populations across the Indian Ocean region. We find strong support for an origin of the Asian ancestry of Malagasy among the Banjar. This group emerged from the long-standing presence of a Malay Empire trading post in Southeast Borneo, which favoured admixture between the Malay and an autochthonous Borneo group, the Ma’anyan. Reconciling genetic, historical and linguistic data, we show that the Banjar, in Malay-led voyages, were the most probable Asian source among the analysed groups in the founding of the Malagasy gene pool.

  Study EGAS00001001841

• Center for Common Disease Genomics (CCDG)-Cardiovascular: University of Pennsylvania Cohort

  The National Human Genome Research Institute (NHGRI) has funded the Centers for Common Disease Genomics (CCDG), a collaborative large-scale genome sequencing effort to comprehensively identify rare risk and protective variants contributing to multiple common disease phenotypes and to better understand the general principles of genomic architecture underlying common, complex inherited diseases. This study of early onset cardiovascular disease and metabolic risk contains data derived from the whole genome sequence data of individuals participating in Penn Medicine's BioBank.

  Study phs001502

• Generation of hypoimmunogenic induced pluripotent stem cells by CRISPR-Cas9 system and detailed evaluation for clinical application

  We established hypoimmunogenic human induced pluripotent stem cells (iPSCs) to minimize the immune rejection caused by mismatches of HLA type. Here, we optimized the protocol for good manufacturing practice (GMP)-compatible CRISPR-Cas9 genome editing to deplete the three gene locus (HLA-A, HLA-B, and CIITA genes) simultaneously in HLA homozygous iPSCs. RNA-seq and flow cytometry analyses confirmed the successful depletion of HLAs, and lineage-specific differentiation into cardiomyocytes was verified.

  Study EGAS50000001194

• Tools

  EGA Tools This page provides details of the applications available at the EGA. Submission Tool Description EgaCryptor Encrypt and create md5sum values. Crypt4gh Encrypt, decrypt or re-encrypt files. INBOX Upload your files once encrypted. FTP Upload your files once encrypted. Submitter Portal Submit and manage your submission metadata online (SEQUENCING AND PHENOTYPE ONLY) Submitter Portal API Submit and manage your submission metadata programmatically. Webin Submit and manage your submission metadata online (array data) Webin API Submit metadata using XML files. Aimed to array-based submissions. Star2xml Format conversion tool to transform tabular formats (e.g. spreadsheets) into EGA's XMLs. Used for large programmatic submissions. Data Access Tool Description DAC Portal Manage and control data access request. Download Tool Description PyEGA3 Download and decrypt datasets requested through your EGA web account. Live Outbox Download and decrypt datasets requested through your EGA web account. Fuse Client A quick and convenient way to browse through EGA files without the need for complete downloads. EGA QuickView A quick and convenient way to browse through EGA files without the need for complete downloads.

  Documentation tools

• Oesophageal adenocarcinoma scRNAseq from LUD2015-005 study (NCT02735239, EudraCT 2015-005298-19)

  This dataset contains single-cell RNA-seq generated from the LUD2015-005 study (NCT02735239, EudraCT 2015-005298-19) and additional donors with Barrett's oesophagus using the 5' Single Cell Gene Expression assay from 10x Genomics. Samples were generated from oesophageal tumours, Barrett's oesophagus, and normal oesophagus and gastric tissues, with the aim of generating a reference atlas for cell types found in normal and disease-associated tissue states in the upper GI tract. This reference atlas was used as the basis for a deconvolution workflow to estimate the cell composition of bulk transcriptomes from these tissues, and to assess cell type-specific expression patterns of potential predictive biomarkers for immunochemotherapy regimens in this setting.

  Dataset EGAD00001009401

• Platelets contain a repertoire of DNA fragments that map over the human nuclear genome, including tumour-derived DNA in patients with active malignancy.

  This dataset consists of WGS from six patients with a recent diagnosis of gastrointestinal adenocarcinoma. DNA-Seq libraries were prepared from platelet DNA (pDNA) and plasma cfDNA obtained from the same peripheral blood sample. pDNA was size selected into 2 groups (small, s-pDNA: less than 600 bp and long, b-pDNA: greater than 600 bp). The short fragments were further cleaned to remove <100 bp fragments and large platelet fragments were fragmented via sonication. Libraries were then prepared using the NEBNext Ultra II DNA Sample Preparation Kit for Illumina according to the manufacturer’s protocol and sequenced on an Illumina NextSeq 500 (300 cycle PE) at low-pass (0.1X) for all samples, and 10X for the cfDNA and s-pDNA, using four lanes in each sample.

  Dataset EGAD00001009856

• Human embryonic stem cells dopaminergic neurons

  These are two biological replicates at timepoint day 17 and day 28 of dopaminergic neuron differentiation of human embryonic stem cells. Ásgrimsdottir et al. (Arenas Lab, Karolinska Institutet)

  Dac EGAC50000000652

• WES data of the pediatric patients with myelodysplastic syndrome

  This dataset consists of 6 BAM files. These are whole exome sequencing data of pediatric patients with myelodysplastic syndrome.

  Dataset EGAD00001005272

• Colorectal Adenoma Gene Screen

  A pilot to establish the feasability of using a custom Agilent targeted pulldown of 110 genes implicated in colorectal tumourigensis to sequence for driver mutations in a set of 30 FFPE colorectal adenomas. If successful, we propose to sequence an additional 350 adenomas as part of a MRC research study in order to define the pattern of driver mutations across the spectrum of pathological subtypes including coventional adenomas, serrated adenomas and hyperplastic polyps

  Dataset EGAD00001001879

• MATISSE bulk RNA-sequencing data

  Tumor availability: Bulk RNA sequencing was performed on tumor biopsies of 48 patients at baseline and 45 patients across time points (baseline n=48, week 1 n=38, week 2 n=34, week 4 n=37), including lymph node metastasis that were sequenced in addition to the primary tumor. Tumor RNA was extracted from formalin-fixed paraffin-embedded (FFPE) primary tumor and lymph node metastasis sections containing at least 10% viable tumor cells present in the sample, except for on-treatment samples with a complete pathological response in which we isolated samples with at least 10% immune-infiltration with no viable tumor cells. A pathologist (LS) scored the tumor percentage and indicated the most tumor-dense region on a hematoxylin and eosin (H&E) stain slide for subsequent RNA isolation. According to the manufacturer's protocol, five to 10 FFPE slides (10 µm) were used for RNA isolation using the AllPrep DNA/RNA FFPE isolation kit (Qiagen, 80234) and the QIAcube. Genetic Diagnostics and Sequencing Services (CeGaT) performed RNA sequencing in Germany. Data processing: Demultiplexing of the sequencing reads was performed with Illumina bcl2fastq (v2.20). Adapters of the reads (Pico v2 SMART adapter, first three nucleotides of the second sequencing read) were trimmed with Skewer (v0.2.2), without quality trimming. RNA-sequencing data was aligned to GRCh38 (Ensembl v109) using STAR (v2.7.9) in 2-pass mode with default settings. Gene counts were generated with the HTSeq (v2.0.2). FastQC (v0.12.1) and MultiQC (v1.14) were used for assessing data quality, on FASTQ files and intermediate processing steps.

  Dataset EGAD50000001470

• UK10K NEURO ABERDEEN

  In the UK10K project we propose 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 analyse directly 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. This sample set comprises cases of schizophrenia with additional cognitive measurements, collected in Aberdeen, Scotland.For further information on the Aberdeen cohort please contact David St Clair (d.stclair@abdn.ac.uk).

  Study EGAS00001000109

• UK10K RARE CHD

  In the UK10K project we propose 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 analyse directly 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 CHD (congenital heart disease) samples will be part of the “rare disease” group, and will undergo exome sequencing.For further information with regard to this cohort please contact Shoumo Bhattacharya (shoumo@me.com).

  Study EGAS00001000125

• Comprehensive genomic and transcriptomic analysis of three synchronous primary tumours and a recurrence from a head and neck cancer patient

  Synchronous primary malignancies occur in a small proportion of head and neck squamous cell carcinoma patients. Here, we analysed three synchronous primaries and a recurrence from one patient, by comparing the genomic and transcriptomic profiles among the tumour samples and determining the recurrence origin. We found remarkable levels of heterogeneity among the primary tumours, and through the patterns of shared mutations, we traced the origin of the recurrence. Interestingly, the patient carried germline variants which might have predisposed him to carcinogenesis, together with a history of alcohol and tobacco consumption. Mutational signature analysis confirmed the impact of alcohol exposure, with Signature 16 present in all tumour samples. Characterisation of immune cell infiltration highlighted an immunosuppressive environment in all samples, which exceeded the potential activity of T cells. Studies such as the one described here have important clinical value and contribute to personalised treatment decisions for patients with synchronous primaries and matched recurrences.

  Study EGAS00001004857

• High-resolution, patient-level dissection of IL-23 blockade in cutaneous psoriasis

  Psoriasis vulgaris and other chronic inflammatory diseases improve markedly with therapeutic blockade of IL-23 signaling in T cells, but the genetic mechanisms of response remain poorly understood. We single-cell transcriptomically profiled CD45+ immune cells isolated from lesional psoriatic skin before and after IL-23 blockade. In clinically responsive patients, a psoriatic transcriptional signature in skin-resident memory T cells was sharply attenuated. In contrast, poorly responsive patients were distinguished by enduring T17 cell activation, a mechanism distinct from alternative cytokine signaling or downstream resistance. Spatial transcriptomic analysis suggests that successful IL-23 blockade requires dampening of > 90% of IL-17-induced signaling in lymphocyte-adjacent keratinocytes, an unexpectedly high threshold. We also detected a subset of persistent, disease-specific T17 abnormalities in responsive patients, revealing an irreversible cell identity that may necessitate ongoing IL-23 inhibition. Collectively, our data establishes a patient-level paradigm for dissecting response to immunomodulatory treatments.

  Study EGAS00001007373

• Clinical Outcomes by Tumor Mutational Burden and Inflammatory Gene Expression With Combined Nivolumab and Ipilimumab or Monotherapy in Advanced Melanoma

  Purpose Exploratory analyses of CheckMate 066 and 067 trials were conducted to investigate associations of tumor mutational burden (TMB), a 4-gene inflammatory gene expression signature, and BRAF mutation status with tumor response, progression-free survival (PFS), and overall survival (OS) in patients with advanced melanoma. Patients and Methods Patients with known programmed death ligand 1 (PD-L1) expression and BRAF mutation status received nivolumab (NIVO) or dacarbazine in CheckMate 066 and either NIVO, ipilimumab (IPI), or NIVO+IPI in CheckMate 067. Whole exome sequencing and RNA sequencing were used to determine TMB and inflammatory gene expression signature scores, respectively. These biomarkers were evaluated in terms of their association with PFS and OS. Results In the NIVO, NIVO+IPI, and IPI arms of CheckMate 067, longer survival was associated with high (> median) versus low (≤ median) TMB with hazard ratios (HRs) (95% confidence interval [CI]) for PFS of 0.45 (0.30–0.65), 0.55 (0.38–0.81), and 0.60 (0.43–0.82), and for OS of 0.46 (0.30–0.71), 0.53 (0.34–0.82), and 0.52 (0.36–0.74), respectively. For NIVO-treated patients, these results were confirmed in CheckMate 066. A survival benefit was observed with high TMB and absence of BRAF mutation. Survival was associated with high versus low inflammatory signature scores with HRs (95% CI) for PFS of 0.56 (0.34–0.94), 0.40 (0.23–0.72), and 0.43 (0.27–0.70), and for OS of 0.37 (0.20–0.66), 0.38 (0.19–0.74), and 0.46 (0.27–0.79), in the NIVO, NIVO+IPI, and IPI arms, respectively. Weak correlations were observed between PD-L1, TMB, and the inflammatory signature. Conclusions Combined assessment of TMB, inflammatory gene expression signature, and BRAF mutation status may be predictive for response to immunotherapy in advanced melanoma.

  Study EGAS00001004564

• Clinical Outcomes by Tumor Mutational Burden and Inflammatory Gene Expression With Combined Nivolumab and Ipilimumab or Monotherapy in Advanced Melanoma - CM066-WES

  Purpose Exploratory analyses of CheckMate 066 and 067 trials were conducted to investigate associations of tumor mutational burden (TMB), a 4-gene inflammatory gene expression signature, and BRAF mutation status with tumor response, progression-free survival (PFS), and overall survival (OS) in patients with advanced melanoma. Patients and Methods Patients with known programmed death ligand 1 (PD-L1) expression and BRAF mutation status received nivolumab (NIVO) or dacarbazine in CheckMate 066 and either NIVO, ipilimumab (IPI), or NIVO+IPI in CheckMate 067. Whole exome sequencing and RNA sequencing were used to determine TMB and inflammatory gene expression signature scores, respectively. These biomarkers were evaluated in terms of their association with PFS and OS. Results In the NIVO, NIVO+IPI, and IPI arms of CheckMate 067, longer survival was associated with high (> median) versus low (≤ median) TMB with hazard ratios (HRs) (95% confidence interval [CI]) for PFS of 0.45 (0.30–0.65), 0.55 (0.38–0.81), and 0.60 (0.43–0.82), and for OS of 0.46 (0.30–0.71), 0.53 (0.34–0.82), and 0.52 (0.36–0.74), respectively. For NIVO-treated patients, these results were confirmed in CheckMate 066. A survival benefit was observed with high TMB and absence of BRAF mutation. Survival was associated with high versus low inflammatory signature scores with HRs (95% CI) for PFS of 0.56 (0.34–0.94), 0.40 (0.23–0.72), and 0.43 (0.27–0.70), and for OS of 0.37 (0.20–0.66), 0.38 (0.19–0.74), and 0.46 (0.27–0.79), in the NIVO, NIVO+IPI, and IPI arms, respectively. Weak correlations were observed between PD-L1, TMB, and the inflammatory signature. Conclusions Combined assessment of TMB, inflammatory gene expression signature, and BRAF mutation status may be predictive for response to immunotherapy in advanced melanoma.

  Study EGAS00001004567

• Clinical Outcomes by Tumor Mutational Burden and Inflammatory Gene Expression With Combined Nivolumab and Ipilimumab or Monotherapy in Advanced Melanoma - CM67 WES

  Purpose Exploratory analyses of CheckMate 066 and 067 trials were conducted to investigate associations of tumor mutational burden (TMB), a 4-gene inflammatory gene expression signature, and BRAF mutation status with tumor response, progression-free survival (PFS), and overall survival (OS) in patients with advanced melanoma. Patients and Methods Patients with known programmed death ligand 1 (PD-L1) expression and BRAF mutation status received nivolumab (NIVO) or dacarbazine in CheckMate 066 and either NIVO, ipilimumab (IPI), or NIVO+IPI in CheckMate 067. Whole exome sequencing and RNA sequencing were used to determine TMB and inflammatory gene expression signature scores, respectively. These biomarkers were evaluated in terms of their association with PFS and OS. Results In the NIVO, NIVO+IPI, and IPI arms of CheckMate 067, longer survival was associated with high (> median) versus low (≤ median) TMB with hazard ratios (HRs) (95% confidence interval [CI]) for PFS of 0.45 (0.30–0.65), 0.55 (0.38–0.81), and 0.60 (0.43–0.82), and for OS of 0.46 (0.30–0.71), 0.53 (0.34–0.82), and 0.52 (0.36–0.74), respectively. For NIVO-treated patients, these results were confirmed in CheckMate 066. A survival benefit was observed with high TMB and absence of BRAF mutation. Survival was associated with high versus low inflammatory signature scores with HRs (95% CI) for PFS of 0.56 (0.34–0.94), 0.40 (0.23–0.72), and 0.43 (0.27–0.70), and for OS of 0.37 (0.20–0.66), 0.38 (0.19–0.74), and 0.46 (0.27–0.79), in the NIVO, NIVO+IPI, and IPI arms, respectively. Weak correlations were observed between PD-L1, TMB, and the inflammatory signature. Conclusions Combined assessment of TMB, inflammatory gene expression signature, and BRAF mutation status may be predictive for response to immunotherapy in advanced melanoma.

  Study EGAS00001004555

• Single-Cell Mitochondrial Mutation Lineage Tracing of Non-Dysplastic and Dysplastic Barrett's Esophagus

  This study addresses a critical knowledge gap in understanding Barrett's esophagus (BE), a common metaplastic condition and precursor to esophageal adenocarcinoma, by identifying lineage connections and cell states underlying its development and malignant transformation. We collected biopsies from 13 Barrett's esophagus patients undergoing routine surveillance endoscopy, sampling metaplastic tissue alongside normal squamous esophagus and gastric cardia tissues when clinically appropriate. Our novel approach integrated single-cell RNA sequencing of nearly 180,000 cells with mitochondrial DNA mutation analysis for lineage tracing, coupled with spatial transcriptomics and comprehensive whole-exome sequencing. For one patient with high-grade dysplasia, we performed detailed whole-exome sequencing analysis at 100x coverage on dissociated Barrett's esophagus cells alongside matched normal esophagus and gastric cardia tissues. This genomic profiling revealed critical genetic drivers of malignant transformation, including a truncating mutation in the mutation cluster region of APC (a negative WNT regulator), mutations in the tumor suppressor genes CDKN2A and TP53, and significant copy number alterations including loss of CDKN2A on chromosome 9. These genetic alterations corresponded with the transcriptional changes observed in our single-cell data, particularly WNT pathway dysregulation and the expansion of LGR5-expressing stem cells, providing molecular evidence for how single clones can initiate the dysplastic transformation. Data deposited in dbGaP consist specifically of the whole-exome sequencing raw files, processed variant calls, and copy number analyses from the single patient with high-grade dysplasia and matched normal esophagus and stomach control tissue. Whole exome sequencing was performed on an Illumina NovaSeq 6000. Sequencing data was preprocessed following the Genome Analysis Toolkit's (GATK) Best Practices. Sequencing data was analyzed with Mutect2 (GATK, v4.2.5.0) to identify short somatic mutations including single-nucleotide polymorphisms and insertions and deletions. The Barrett's esophagus sample was run with the two matched normals, as well as the publicly available germline resource somatic-hg38_af-only-gnomad.hg38.vcf.gz (GATK). Somatic variant calls were filtered using FilterMutectCalls (GATK) and loaded into the Integrative Genomics Viewer (v2.12.3) for identification of high-quality variants in known esophageal adenocarcinoma regulators. Copy number alterations in the Barrett's esophagus sample were estimated using CNVkit (v0.9.9). CNV, Mutect2, and SVC analyses will be made available through dbGAP. Copy number alterations in the Barrett's esophagus sample were estimated using CNVkit (v0.9.9) with the default run settings; a pooled reference was generated from the normal esophagus and stomach samples. Copy number results were plotted directly within CNVkit using the function scatter.

  Study phs003949

• 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

• 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_mRNA-Seq data generated from KEP study

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

  Dataset EGAD00001003490

• 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

• 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 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 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 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 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 KNIH001 mRNA-seq data generated from KEP study

  A KNIH001 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002167

• 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

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

  A KNIH004 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002170

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

  A KNIH005 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002171

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

  A KNIH006 mRNA-seq paired end data for beta cells

  Dataset EGAD00001002172

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

  A KNIH001 miRNA-seq single end data for islet cells

  Dataset EGAD00001002760

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

  A KNIH002 miRNA-seq single end data for islet cells

  Dataset EGAD00001002761

• 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

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

  A KNIH005 miRNA-seq single end data for islet cells

  Dataset EGAD00001002764

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

  A KNIH006 miRNA-seq single end data for beta cells

  Dataset EGAD00001002765

• A Pilot Study Using Next Generation Sequencing in Advanced Cancers: Feasibility and Challenges

  In this study, we used NGS to identify somatic events in advanced stage cancer patients to demonstrate the feasibility for using this approach to improve patient treatment.

  Study phs000657

• Exome sequencing of patients with rare neurological disorders

  Exome sequencing of patients and their families with diverse rare neurological disorders. Some families have prior linkage data identifying a specific chromosomal interval or interest, other families do not have linkage data available. Many of these families come from special populations whose demography or preference for consanguineous marriages make them particularly tractable for genetic studies.

  Dataset EGAD00001000346

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB22_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB22_M

  Dataset EGAD00001001708

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB23_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB23_C

  Dataset EGAD00001001709

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW4_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW4_M

  Dataset EGAD00001001834