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• Single Cell Genome Sequence for DLP+ library A96228A

  Single Cell Genome Sequence for Immortalized lymphoblastoid cell line GM18507 cell line SA928,Triple negative breast cancer patient-derived xenograft SA609 passage 2 patient-derived xenograft SA609 passage 2 on DLP+ library A96228A

  Dataset EGAD00001009645

• Tumour sample for patient SA238

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA238

  Dataset EGAD00001009352

• Tumour sample for patient SA221

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA221

  Dataset EGAD00001009351

• Tumour sample for patient SA683

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA683

  Dataset EGAD00001009350

• Tumour sample for patient SA682

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA682

  Dataset EGAD00001009349

• Tumour sample for patient SA679

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA679

  Dataset EGAD00001009346

• Tumour sample for patient SA678

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA678

  Dataset EGAD00001009345

• Tumour sample for patient SA677

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA677

  Dataset EGAD00001009344

• Tumour sample for patient SA676

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA676

  Dataset EGAD00001009343

• Tumour sample for patient SA675

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA675

  Dataset EGAD00001009342

• Tumour sample for patient SA674

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA674

  Dataset EGAD00001009341

• Tumour sample for patient SA673

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA673

  Dataset EGAD00001009340

• Tumour sample for patient SA680

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA680

  Dataset EGAD00001009347

• Tumour sample for patient SA681

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA681

  Dataset EGAD00001009348

• A98294A

  Whole genome sequencing for single cells for library A98294A 994 samples; filetype=bam

  Dataset EGAD00001008266

• A98293B

  Whole genome sequencing for single cells for library A98293B 703 samples; filetype=bam

  Dataset EGAD00001008265

• A98289B

  Whole genome sequencing for single cells for library A98289B 1032 samples; filetype=bam

  Dataset EGAD00001008264

• A98284B

  Whole genome sequencing for single cells for library A98284B 1582 samples; filetype=bam

  Dataset EGAD00001008263

• A98284A

  Whole genome sequencing for single cells for library A98284A 1265 samples; filetype=bam

  Dataset EGAD00001008262

• A98176B

  Whole genome sequencing for single cells for library A98176B 1389 samples; filetype=bam

  Dataset EGAD00001008261

• A98176A

  Whole genome sequencing for single cells for library A98176A 1003 samples; filetype=bam

  Dataset EGAD00001008260

• A96216A

  Whole genome sequencing for single cells for library A96216A 1001 samples; filetype=bam

  Dataset EGAD00001008257

• A96215A

  Whole genome sequencing for single cells for library A96215A 1312 samples; filetype=bam

  Dataset EGAD00001008256

• A96210C

  Whole genome sequencing for single cells for library A96210C 1191 samples; filetype=bam

  Dataset EGAD00001008254

• TENX069

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX069 1 samples; filetype=fastq

  Dataset EGAD00001006483

• TENX068

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX068 1 samples; filetype=fastq

  Dataset EGAD00001006482

• TENX066

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX066 1 samples; filetype=fastq

  Dataset EGAD00001006481

• TENX065

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX065 1 samples; filetype=fastq

  Dataset EGAD00001006480

• TENX064

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX064 1 samples; filetype=fastq

  Dataset EGAD00001006479

• SCRNA10X_SA_CHIP0063_000

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0063_000 1 samples; filetype=fastq

  Dataset EGAD00001006478

• SCRNA10X_SA_CHIP0172_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0172_002 1 samples; filetype=fastq

  Dataset EGAD00001006476

• SCRNA10X_SA_CHIP0172_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0172_001 1 samples; filetype=fastq

  Dataset EGAD00001006475

• SCRNA10X_SA_CHIP0163_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0163_002 1 samples; filetype=fastq

  Dataset EGAD00001006474

• SCRNA10X_SA_CHIP0163_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0163_001 1 samples; filetype=fastq

  Dataset EGAD00001006473

• SCRNA10X_SA_CHIP0152_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0152_002 1 samples; filetype=fastq

  Dataset EGAD00001006472

• SCRNA10X_SA_CHIP0152_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0152_001 1 samples; filetype=fastq

  Dataset EGAD00001006471

• SCRNA10X_SA_CHIP0150_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0150_002 1 samples; filetype=fastq

  Dataset EGAD00001006470

• SCRNA10X_SA_CHIP0150_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0150_001 1 samples; filetype=fastq

  Dataset EGAD00001006469

• SCRNA10X_SA_CHIP0149_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0149_002 1 samples; filetype=fastq

  Dataset EGAD00001006468

• SCRNA10X_SA_CHIP0149_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0149_001 1 samples; filetype=fastq

  Dataset EGAD00001006467

• SCRNA10X_SA_CHIP0146_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0146_002 1 samples; filetype=fastq

  Dataset EGAD00001006466

• SCRNA10X_SA_CHIP0142_003

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0142_003 1 samples; filetype=fastq

  Dataset EGAD00001006465

• SCRNA10X_SA_CHIP0142_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0142_001 1 samples; filetype=fastq

  Dataset EGAD00001006464

• SCRNA10X_SA_CHIP0141_004

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0141_004 1 samples; filetype=fastq

  Dataset EGAD00001006463

• SCRNA10X_SA_CHIP0080_002

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0080_002 1 samples; filetype=fastq

  Dataset EGAD00001006462

• SCRNA10X_SA_CHIP0080_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0080_001 1 samples; filetype=fastq

  Dataset EGAD00001006461

• SCRNA10X_SA_CHIP0006_001

  Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0006_001 1 samples; filetype=fastq

  Dataset EGAD00001006460

• TENX062

  Transcriptome profiling by high-throughput sequencing for single cells for library TENX062 1 samples; filetype=fastq

  Dataset EGAD00001006477

• Whole-genome variant calling of individuals from the study of allergic diseases in the Canary Islands

  Research study of genetic susceptibility and analyses of polygenic risk scores in allergic diseases. The historic influences in the Canary Islanders have resulted in an admixture of European, North Africans and Sub-saharan African genetic ancestries. Genetic risks linked to any of these ancestries could underlie disease peculiarities in the current inhabitants. To leverage genectic ancestry to identify and assess genetic risks in allergic diseases in the Canary Islands, we combine different genomic technologies in case-control studies and reference samples from the three parental populations to characterize and identify genetic risks factors and the intersection with ancestry.

  Study EGAS50000000299

• Whole exome sequencing of atypical 3q26 samples

  The Genomic DNA Clean & Concentrator kit (ZYMO Research) was used to remove EDTA from the DNA samples. Sample libraries were prepared using 100 ng of input according to the KAPA HyperPlus Kit (Roche) using Unique Dual Index adapters (Integrated DNA Technologies, Inc.). Exomes were captured using the SeqCap EZ MedExome (Roche Nimblegen) according to SeqCap EZ HyperCap Library v1.0 Guide (Roche) with the xGen Universal blockers – TS Mix (Integrated DNA Technologies, Inc.). The amplified captured sample libraries were paired-end sequenced (2x100 bp) on the Novaseq 6000 platform (Illumina) and aligned to the hg19 reference genome using the Burrows-Wheeler Aligner (BWA).

  Dataset EGAD00001006102

• Multimodal Genomic Features Predict Outcome of Immune Checkpoint Blockade in Non-small Cell Lung Cancer

  Despite progress in immunotherapy, identifying patients that respond has remained a challenge. Through analysis of whole-exome and targeted sequence data from 5,449 tumors, we found a significant correlation between tumor mutation burden (TMB) and tumor purity, suggesting that low tumor purity tumors are likely to have inaccurate TMB estimates. We developed a new method to estimate a corrected TMB (cTMB) that was adjusted for tumor purity and more accurately predicted outcome to immune checkpoint blockade (ICB). To identify improved predictive markers together with cTMB, we performed whole-exome sequencing for 104 lung tumors treated with ICB. Through comprehensive analyses of sequence and structural alterations, we discovered a significant enrichment in activating mutations in receptor tyrosine kinase (RTK) genes in non-responding tumors in three immunotherapy-treated cohorts. An integrated multivariable model incorporating cTMB, RTK mutations, smoking-related mutational signature, and HLA status provided an improved predictor of response to immunotherapy that was independently validated.

  Study EGAS00001003892

• Bioinformatics analysis of chimerism in monochorionic dizygotic twins

  We conducted a bioinformatics analysis of hematopoietic chimerism of the monochorionic dizygotic twins. Whole-genome sequencing was used to understand the genomic compositions of the chimeras.

  Study EGAS00001005997

• SCANDARE TNBC

  Prospective biobanking study in breast patients aiming at better understand the link between the molecular alterations of the tumor itself, its microenvironment and immune response.

  Study EGAS50000000970

• Genesis of Two Most Prevalent Variants Causing Combined Pituitary Hormone Deficiency in 21 Populations

  Two variants (c.[301_302delAG];[301_302delAG] and c.[150delA];[150delA]) in the PROP1 gene are the most common genetic causes of recessively inherited combined pituitary hormones deficiency (CPHD). Our objective was to analyze in detail the origin of the two most prevalent variants. In the multicentric study were included 237 patients with CPHD and their 15 relatives carrying c.[301_302delAG];[301_302delAG] or c.[150delA];[150delA] or c.[301_302delAG];[ 150delA]. They originated from 21 different countries worldwide. We genotyped 21 single nucleotide variant markers flanking the 9.6Mb region around the PROP1 gene that are not in mutual linkage disequilibrium in the general populations - a finding of a common haplotype would be indicative of ancestral origin of the variant. Haplotypes were reconstructed by Phase and Haploview software, and the variant age was estimated using an allelic association method. We demonstrated ancestral origin of both variants: c.[301_302delAG] was carried on 0.2Mb-long haplotype in a majority of European patients arising approximately 101 generations ago (confidence interval 90.1-116.4). Patients from the Iberian Peninsula displayed a different haplotype which was estimated to have emerged 23.3 (20.1-29.1) generations ago. Subsequently, the data indicated that both haplotypes were transmitted to Latin American patients approximately 13.8 (12.2-17.0) and 16.4 (14.4-20.1) generations ago, respectively. The c.[150delA] variant that was carried on a haplotype spanning about 0.3Mb was estimated to appear 43.7 (38.4-52.7) generations ago. We present strong evidence that the most frequent variants in the PROP1 gene are not a consequence of variant hot-spots as previously assumed, but are founder variants.

  Study EGAS00001001165

• Ovarian cancer sample size analysis

  The dataset contains exome sequencing fastq from 5 ovarian cancer patients, paired with tumor normal blood samples. Three tumor samples were sequenced from each patient: a biopsy sample ("-1" suffix in the file name), a local sample (multiple regions around the biopsy pooled together, with the "-2" suffix in the file name), and a global sample (multiple regions from the tumor pooled together, with a "-3" suffix in the file name).

  Dataset EGAD00001005947

• 16S metagenomics on NASH patients complicated with diabetes

  16S metagenomic analysis was performed on 20 NASH patients complicated with diabetes. Sum of the several Bacteroides species was smaller in the patients treated with insulin analogues than those who did not receive insulin treatment. The results were consistent with the study performed with model mice treated with insulin.

  Study JGAS000574

• Radiotherapy_induced_Sarcoma_exome

  Whole exome library making will be performed on genomic DNA derived from radiotherapy induced sarcoma samples and matched normal DNA from the same patients. Next Generation sequencing will be performed on the resulting libraries and mapped to build 37 of the human reference genome to facilitate the identification of mutations

  Study EGAS00001000194

• Comparison of HCC cell lines and primary HCCs

  There are concerns about whether cancer cell lines could faithfully represent the matched primary cancer cells. Comparison of the HCC cell lines and primary HCCs demonstrated that, during long-term in vitro culture, cell lines retain the genetic landscape of the matched primary HCCs.

  Study EGAS00001001678

• Radiotherapy_induced_sarcoma

  Whole genome library making will be performed on genomic DNA derived from radiotherapy induced sarcoma samples and matched normal DNA from the same patients. Next Generation sequencing will be performed on the resulting libraries and mapped to build 37 of the human reference genome to facilitate the identification of mutations

  Study EGAS00001000138

• Genetic Mechanisms of Disease Lab DAC

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

  Dac EGAC50000000087

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

  For mature miRNAs and hairpins, BAM and FASTq files

  Dataset EGAD50000000468

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

  Sequencing files for TOPARP-B patients as described in manuscript

  Dataset EGAD50000000407

• Genentech_cell_line_SNP

  SNP array data for 668 cancer cell lines

  Dataset EGAD00010000951

• 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

• PDX WES

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

  Dataset EGAD00001009657

• Sporadic multiple meningioma Genomics

  Wes for 15 multiple meningioma samples from 6 individual

  Dataset EGAD00001008710

• Whole Exome Sequencing

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

  Dataset EGAD00001005425

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

  A KNIH008 mRNA-seq paired end data for adipocytes

  Dataset EGAD00001002174

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

  A KNIH007 mRNA-seq paired end data for adipocytes

  Dataset EGAD00001002173

• 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

• GoNL raw sequence data in fastq format

  Raw sequencing data for all samples in fastq format.

  Dataset EGAD00001000821

• DATA FILES FOR SJHYPO

  Hypodiploid acute lymphoblastic leukemia whole genome sequencing

  Dataset EGAD00001000260

• Myocardial Infarction Genetics Exome Sequencing Consortium: University of Lubeck

  MI cases were recruited from the German MI Family Study and the Angio-Lub study. Controls were recruited from the German MI Family Study and the Angio-Lub study. All exome sequencing was performed at the Broad Institute of Harvard and MIT; samples sequence capture was performed using Illumina's ICE Capture reagent and sequencing was performed on an Illumina HiSeq 2000 or 2500.

  Study phs000990

• Rearrangements of Viral and Human Genomes at Human Papillomavirus Integration Events and Their Allele-Specific Impacts on Cancer Genome Regulation

  We discovered genomic and epigenomic dysregulation originating at sites of human papillomavirus (HPV) integration following the Oxford Nanopore long-read analysis of 72 cervical cancer genomes. The integration events had allele-specific effects on the genome, methylome, and transcriptome, which sometimes resulted in the activation of cancer genes. We also examined the 72 samples using Illumina short-read analysis in phs000528.

  Study phs003780

• Roma Sequencing Study

  Present day Roma genome were shaped by the extensive inbreeding and admixture during their Diaspora. Here, we shed light on the Roma demographic history by analyzing the whole genome sequence of 46 Roma individuals pertaining to four migrant groups in six European countries. The strong reduction in effective population size (~44%), that occurred around 2kya, was not masked by the subsequent high admixture in Middle Eastern and European countries.

  Study EGAS00001004287

• Genetic characterization patients affected by Cancer of Unknown Primary

  The genetic changes sustaining the development of human Cancers of Unknown Primary (CUP) remain elusive. Here we report the genomic profiling of 14 rigorously selected CUP samples. Although no specific recurring mutation in known driver genes was found, a comparison of the mutational landscape of CUPs with that of most other human tumor types revealed a consistent change in diverse genes belonging to the axon guidance KEGG pathway.

  Study EGAS00001006621

• RNA sequencing data from visceral and abdominal subcutaneous adipose tissue from morbidly obese women with normal glucose tolerance or type 2 diabetes

  The study group consisted of 17 obese women with normal glucose tolerance and 15 obese women with T2DM. Adipose tissue specimens were taken from the epigastric region of the abdominal wall (SAT) and from the major omentum (VAT). RNA was isolated and RNA sequencing was used to analyse the transcriptome. Dharuri H et al, Diabetologia. 2014;57(11):2384-92.

  Study EGAS00001001872