Search Results - EGA European Genome-Phenome Archive

Ultra-deep Error-corrected Sequencing of Peripheral Blood for Clonal Hematopoiesis in Patients Undergoing AAA Surgery

Patients undergoing elective abdominal aortic aneurysm (AAA) surgery were enrolled to investigate the prevalence and characteristics of clonal hematopoiesis. Peripheral blood was collected at the time of surgical admission using PAXgene DNA tubes. Genomic DNA was extracted and subjected to ultra-deep error-corrected targeted sequencing using a custom gene panel covering recurrent clonal hematopoiesis-associated genes. Sequencing libraries were prepared following established protocols and sequenced on an Illumina NovaSeq X system. The resulting dataset includes paired-end FASTQ files for each participant, enabling high-sensitivity detection of somatic variants down to low variant allele frequencies. This study provides a comprehensive sequencing resource for evaluating clonal hematopoiesis in AAA patients.

Study JGAS000864

Evaluating potential drug candidates for the treament of Henamgiopericytoma on patient derived cell line models

Solitary fibrous tumor/Hemangiopericytoma (SFT/HPC) is a rare subtype of soft tissue sarcoma associated with NAB2-STAT6 gene fusions. This study established and characterized a novel SFT/HPC patient-derived cell line called SFT-S1. Potential drug candidates that could be repurposed for the treatment of SFT/HPC were screened.

Study EGAS50000000027

HCC cfMeDIP-seq

This dataset comprises 406 raw fastq files derived from 203 plasma cfDNA samples from 90 patients diagnosed with hepatocellular carcinoma (HCC). The samples include 90 baseline HCC (b-HCC) samples, collected during liver transplant or resection surgeries, and 113 postoperative follow-up (f-HCC) samples. For each sample, 10ng of cfDNA was used for library preparation utilizing cfMeDIP-seq technology based on the 5mC antibody-immunoprecipitation strategy. Libraries were validated via Bioanalyzer trace analysis and sequenced on Illumina NovaSeq 6000 or HiSeq 2500 platform with paired-end 150-bp (NovaSeq) or 125-bp (HiSeq) model for ~100 million reads per sample.

Dataset EGAD50000000651

RNA sequencing of Notch inhibitor treated HCC PDX models across timepoints

To study the effect of Notch inhibition on the transcriptional profiles our PDX models, we generated bulk RNA-seq data for two Notch inhibition sensitive and three Notch inhibition insensitive models across multiple timepoints (8h, 24h, 48h, 7d, 17d) using anti-JAG1, anti-Notch2, anti-Notch1 or anti-gD antibodies. 3-5 samples were collected for each treatment condition, resulting in a total of 96 samples. Isolated RNA was used as input for library preparation using TruSeq RNA Sample Preparation Kit v2 (Illumina). The libraries were multiplexed and sequenced on Illumina HiSeq 2500 (Illumina) to produce FASTQ files.

Dataset EGAD50000000737

Transcriptomics for IMMU-SCCHN1 cohort

The goal of IMMUcan is to understand how the immune system interacts with tumors and how therapeutic interventions influence this interaction. This cohort includes patients with confirmed locally recurrent and/or metastatic squamous cell carcinoma of the head and neck (SCCHN) who receive first-line treatment with immune checkpoint inhibitors (ICI) or ICI combined with chemotherapy. This dataset contains transcriptomics data processed with the STAR pipeline. Two sequencing kits were employed: Roche KAPA for high-quality RNA samples and Takara SMART-Seq Stranded for samples with limited RNA quantities.

Dataset EGAD50000002206

10X single-cell Multiome (RNA+ATAC) and single-cell RNAseq of xenograft-derived HSPC, progenitors and myeloid progeny

10X genomics chromium single-cell ATAC+RNA (Multiome) was use to prepare single-nucleus RNA- and ATAC-seq libraries for human HSPC sorted from xenografts recovered from inflammatory challenge. 10X genomics 3' kits were used to prepare single-cell RNA-seq libraries for committed progenitors and myeloid cells from the same xenografts. These were sequenced on an Illumina NovaSea 6000 platform; BAM files are provided. Samples from 3 conditions were sequenced for each cell type; 3 chromatin accessibility runs and 9 gene expression runs are provided.

Dataset EGAD50000002328

Variant calling for LUNG-NSCLC2 cohort

The goal of IMMUcan is to understand how the immune system interacts with tumors and how therapeutic interventions influence this interaction. This cohort includes patients with non-small cell lung cancer who had surgical resection. This is a subcohort of the EORTC SPECTAlung study. This dataset contains whole exome sequencing data from tumor samples processed with the Vegan pipeline. In the absence of matching germline samples, variant calling is performed using two panel of normals (one for female and one for male). It includes SNP and CNV calling.

Dataset EGAD50000002235

Longitudinal profiling of circulating tumour DNA for tracking tumour dynamics in pancreatic cancer

The utility of circulating tumour DNA (ctDNA) for longitudinal tumour monitoring in pancreatic ductal adenocarcinoma (PDAC) has not been explored beyond mutations in the KRAS proto-oncogene. We aim to characterise and track patient-specific somatic ctDNA variants, to assess longitudinal changes in disease burden and explore the landscape of actionable alterations.

Study EGAS00001005981

Long-read sequencing for cell-free DNA analysis (mouse)

In this study, we compared the two long-read sequencing platforms, namely the single-molecule real-time sequencing by Pacific Biosciences and nanopore sequencing by Oxford Nanopore Technologies, for the analysis of cell-free DNA from plasma. Artificial mixtures of sonicated human and mouse DNA at different sizes were sequenced with the two platforms.

Study EGAS00001006329

Cancer sequencing for somatic variant calling

This dataset includes WES and RNAseq for 11 patients with metastatic melanoma, lung, kidney, and stomach cancers, enrolled in phase I clinical trials of TIL ACT (NCT03475134 & NCT04643574). WES was performed on matched cancer and healthy tissues, whereas RNAseq was performed on cancer tissues, using Illumina HiSeq 2500/4000 and Illumina NextSeq 550 systems.

Study EGAS00001007101

H3K27ac/H3K27me3 landscape of medulloblastoma

We profiled 111 patient medulloblastoma primary tumor samples by bulk RNA-seq (19 samples), 27ac (98 samples) / 27me3 (61 samples) ChIP-Seq, WGS (4 samples) and 27ac hichip (8 samples). Submitted data consists of data generated from previously unpublished tumors as well as complementary data for data sets already published for identical medulloblastoma tumors (ex: 27me3 ChIP-Seq and RNA-Seq data submitted for a tumor with publicly available WGS data). The raw fastqs and hg19 aligned RNA-Seq bams are provided.

Dataset EGAD00001009709

IntEnd study

Formalin-fixed, paraffin-embedded samples from 643 colorectal adenomas collected in different hospitals in Norway, from which DNA was extracted, were analysed for DNA copy number alterations. Some adenomas had more than one block (n=42), thus 643 individuals, 685 blocks. Low-coverage whole genome sequencing was run in all samples. For 529 individuals all the clinical information was available. A subset was matched for follow-up time, age and sex in a nested case-control approach (n=366; cases - individuals who developed later on CRC; controls - individuals who did not develop CRC within the same follow-up time).

Dataset EGAD00001010119

GenomeDenmark Phase 2 - MHC haplotypes

The MHC vcf call set was generated using a modified AsmVar and BayesTyper pipeline. In contrast to the original pipeline, where variant calling is performed using alignment of collapsed assemblies to a reference genome, the MHC call set was produced using alignment of phased MHC haplotypes. Two iterations of BayesTyper was run, a first iteration for each haplotype seperately and a second iteration performing joint variant calling on all haplotypes. The sample IDs for the fathers and mothers are TrioID-01 and TrioID-02, respectively, and the IDs for the children are TrioID-0x, where x is a number between 3 and 7.

Dataset EGAD00001003455

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

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

Dataset EGAD00001012103

Progressive multifocal leukoencephalopathy (PML)

We generated RNAseq, ATACseq, Multiomics and CITEseq for patients with Progressive multifocal leukoencephalopathy (PML) to profile the chromatin accessibility, transcriptional profiles and surface proteins at single-cell level.

Study EGAS50000000139

Human TRIM24-MET fusion HGG RNA-seq

Human RNA-seq data for TRIM24-MET fusion tumor primary samples and patient/PDOX-derived cell lines. Cells were treated with 0.1% DMSO or EC90 of MET inhibitors (capmatinib, cabozantinib, crizotinib) for 4 hours. BAM files containing aligned (and unaligned) reads to hg19 are provided.

Dataset EGAD50000000194

Treatment of genetic screening of hypertriglyceridemia type I, III, and V - HTG Amsterdam

This is a cohort of patients with extreme hypertriglyceridemia. Patients have been screened for loss of function mutations in LPL, GPIHBP1, APOC2, APOA5 and LMF1.

Study phs000511

cfRRBS data plasma healthy donors

This is cfRRBS data produced from the cfDNA in plasma isolated from healthy, adult donors. Used as a reference for background signal in diseased (onclolytic) plasma cfDNA.

Study EGAS50000000376

DAC for "Cancer-associated fibroblasts promote drug resistance in ALK-driven lung adenocarcinoma cells by upregulating lipid biosynthesis"

DAC for the data of "Cancer-associated fibroblasts promote drug resistance in ALK-driven lung adenocarcinoma cells by upregulating lipid biosynthesis" with: • Holger Sültmann • h.sueltmann@dkfz.de • German Cancer Research Center (DKFZ) • Ann-Kathrin Daum • a.daum@dkfz.de • German Cancer Research Center (DKFZ)

Dac EGAC50000000076

CITE-seq data of six CK-AML patient samples

We performed cellular indexing of transcriptomes and epitopes (CITE-seq) of six primary leukemia samples from CK-AML patients. The dataset contains BAM files for each individual sample (D1922 and R0836) or for the hashed pool (HIAML47-HIAML85, P9D-P9R)

Dataset EGAD50000000633

Whole genome sequencing raw data for fragile X associated unmethylated expansion carrier 1

Whole genome sequencing raw data for fragile X associated unmethylated expansion carrier 1. DNA was sequenced using the illumina NovaSeq6000 system. 8x paired end FASTQ files from one DNA sample (UFM 1), 4x R1 files and 4x R2 files.

Dataset EGAD50000000917

Whole genome sequencing raw data for fragile X associated unmethylated expansion carrier 2

Whole genome sequencing raw data for fragile X associated unmethylated expansion carrier 2. DNA was sequenced using the illumina NovaSeq6000 system. 8x paired end FASTQ files from one DNA sample (UFM 2), 4x R1 files and 4x R2 files.

Dataset EGAD50000000921

Dataset for "HPV integration induces gene fusions" (Illumina)

Illumina whole-genome sequencing data for "HPV integration induces gene fusions" We performed short read whole-genome sequencing of five HPV+ head and neck cancer samples using Illumina. The reads are submitted in bam or fastq file format.

Dataset EGAD50000001305

Genetic Analysis of Tuberous Sclerosis Complex (TSC) Hypomelanotic Macules

In this study we searched for mutations in the TSC1 and TSC2 genes in cultured melanocytes from individuals with the genetic disease Tuberous Sclerosis Complex.

Study phs001236

Single cell and spatial transcriptomics of adult human adrenal glands

The data set is composed of single cell transcriptomics data for adrenal glands from 11 deceased organ donors (10x 3') and spatial transcriptomic data for adrenal glands from 4 deceased organ donors (10x Visium).

Dataset EGAD50000000394

WGS data for Pancreatic Cancer samples (misc.)

WGS data for pancreatic cancer samples. These are miscellaneous samples which are part of a tandem duplicator phenotype paper (Farooq et al, NPJ Precision Oncology, 2025). The dataset includes tumour and normal BAM files sequenced on Illumina NovaSeq 6000 and aligned against GRCh38 (n=7 tumour/normal pairs).

Dataset EGAD50000001833

Sequencing dataset for the Predictive Endocrine ResistanCe Index (PERCI) in Breast Cancer cases

Sequencing dataset for the Predictive Endocrine ResistanCe Index (PERCI) in Breast Cancer cases of the WSG-ADAPT trial (NCT01779206). The Oncomine Comprehensive v3 assay (ThermoFisher) and a customized amplicon-based NGS-Panel (DERv2) was performed.

Dataset EGAD50000001595

Raw scRNA-seq data for “Characterization of intestinal immune responses in generalized human and murine lipodystrophy”

Raw 10X 5' v3 scRNA-seq data (GEX) for PBMC of 3 helthy donors and two AGLCD patient timepoints as well as of LPMC of 3 Crohns disease patients and one timepoint of the AGLCD patient.

Dataset EGAD50000002190

Raw scTCR-seq data for “Characterization of intestinal immune responses in generalized human and murine lipodystrophy”

Raw 10X 5' v3 scTCR-seq data (TCR) for PBMC of 3 helthy donors and two AGLCD patient timepoints as well as of LPMC of 3 Crohns disease patients and one timepoint of the AGLCD patient.

Dataset EGAD50000002191

Circulating immune cell landscape in colorectal cancer patients

To understand the circulating immune cells in colorectal cancer patients, we performed single-cell RNA sequencing for 14 PBMC samples from 14 colorectal cancer patients.

Study EGAS50000000590

ChIP-seq for 10 samples

This dataset contains ChIP-seq data generated from two patient-derived xenograft (PDX) models. For each model, sequencing data are provided from input control (n=1), IgG control (n=2), and FOXF1 immunoprecipitation (n=2). All the 10 samples are in bam file type.

Dataset EGAD50000001786

Epi2Diag raw methylation array data for patients with neurodevelopmental disorders

Raw methylation array data (Illumina Infinium EPIC v2.0) from patients with rare neurodevelopmental disorders. IDAT files were generated from DNA extracted from whole blood at the ASGARD platform, Inserm U1245, Univ Rouen, CHU Rouen, to investigate new episignatures and provide independent validation datasets for existing episignatures.

Dataset EGAD00010002724

Analysis_of_somatic_mutations_in_normal_blood__AML_and_MDS_samples

Targeted pulldown of genes known to be recurrently mutated in AML & MDS from patient and normal samples using Agilent Sureselect and for some cases also using Illumina Truseq technology.

Study EGAS00001000525

Prognostic_factors_in_prostate_cancer__deep_sequencing_pilot_project_TAPG

This is a pilot project to determine whether the TAPG FFPE DNA’s are suitable for deep sequencing. If successful an investigation of SNP distribution in a larger cohort will follow.

Study EGAS00001000879

Organoid_Derivation_Project_GRCh38_RNAseq

Generation of normal and cancer organoids for sequencing as part of the WTSI-CRUK international collaboration with NCI (in HCMI) to generate the next generation of cancer cell lines.

Study EGAS00001004677

Organoid_Derivation_Project_GRCh38_WGS

Generation of normal and cancer organoids for sequencing as part of the WTSI-CRUK international collaboration with NCI (in HCMI) to generate the next generation of cancer cell lines

Study EGAS00001004712

Bulk and single-cell AML RNA-seq post ex vivo culture

Bulk and single-cell RNA-seq performed on primary AML blasts cultured ex vivo for 3 days in standard or niche-like conditions.

Study EGAS00001006265

Organoid_Derivation_Project_GRCh38_TGS

Generation of normal and cancer organoids for sequencing as part of the WTSI-CRUK international collaboration with NCI (in HCMI) to generate the next generation of cancer cell lines.

Study EGAS00001007349

WGS of NPC268_Tumor and NPC268_Cell_line

This dataset contains WGS data in the form of BAM files for NPC268 - "Tumor" derived from snap-frozen tissue while "Cell line" derived from late passage NPC268 cell line. Extracted DNA was sent for 100x and 60x WGS with Novogene via Apical Scientific Sdn Bhd.

Dataset EGAD00001010292

A95720A

Whole genome sequencing for single cells for library A95720A 1467 cells; filetype=bam

Dataset EGAD00001007613

A96240B

Whole genome sequencing for single cells for library A96240B 1683 cells; filetype=bam

Dataset EGAD00001007122

Single-cell RNA-seq counts and merged data for 28 Hodgkin lymphomas and 5 reactive lymph nodes

This dataset contains raw count data (10X CellRanger) for 28 Hodgkin lymphoma samples and 5 reactive lymph nodes, and merged data from all samples (RData object) including cell cluster assignments.

Dataset EGAD00001008270

Oncoprint GSCCs

This dataset contains DNA sequencing data from 7 GSCCs (data for BT569 is not available). We performed focused exome sequencing (43 most mutated genes in GBM), combined with OneSeq analysis (Agilent) on the GSCCs and identified both shared and GSCC-specific mutations. For CME038, whole genome sequencing was performed.

Dataset EGAD00001011276

Single Cell Genome Sequence for DLP+ library A118389B

Single Cell Genome Sequence for Telomerase immortalized breast epithelium cell line 184-hTERT 85.14 p20 cell line AT135 on DLP+ library A118389B

Dataset EGAD00001009433

A96233B

Whole genome sequencing for single cells for library A96233B 1601 cells; filetype=bam

Dataset EGAD00001007623

Single Cell Genome Sequence for DLP+ library A110660A

Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA610 passage 3 on DLP+ library A110660A

Dataset EGAD00001009430

RNA-seq for identification of neotranscripts

This dataset contains RNA-seq data (Fastq files of paired-end data) of 18 patient tumors used for identification of neotranscripts in 18 different types of fusion-driven sarcomas and other cancers as described in Vibert et al., Mol Cell 2022 (PMID: 35550257)

Dataset EGAD00001008832

CBD-RAW-REPERTOIRE-B: B cell bulk repertoire sequence files

fastq and filtered fasta files for B-cell receptor sequencing. For further information regarding this dataset, please contact Rachael Bashford-Rogers at contact@combat.ox.ac.uk.

Dataset EGAD00001007960

Breast implant-associated anaplastic large cell lymphoma shallow whole genome sequencing for copy number analysis and Whole exome sequencing data.

Shallow whole genome sequencing of 29 BIA-ALCL patients for copy number analysis and 24 Alk-negative ALCL samples as control cohort. 7 Whole exome sequencing BIA-ALCL samples.

Dataset EGAD00001006433

A96174B

Whole genome sequencing for single cells for library A96174B 1447 samples; filetype=bam

Dataset EGAD00001008242

Single Cell Genome Sequence for DLP+ library A118857B

Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1184D passage 1 on DLP+ library A118857B

Dataset EGAD00001009445

Single Cell Genome Sequence for DLP+ library A95663A

Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1053F passage 1 on DLP+ library A95663A

Dataset EGAD00001009446

Single Cell Genome Sequence for DLP+ library A96187A

Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 11, patient-derived xenograft SA609 passage 7 on DLP+ library A96187A

Dataset EGAD00001009464

A96177B

Whole genome sequencing for single cells for library A96177B 683 samples; filetype=bam

Dataset EGAD00001008244

Single Cell Genome Sequence for DLP+ library A98181A

Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA919 passage 7, patient-derived xenograft SA1050A passage 1 on DLP+ library A98181A

Dataset EGAD00001009479

Whole-genome sequencing data for inflammatory breast cancer patients

This study reports the whole-genome sequencing data for 20 inflammatory breast cancer patients, each of whom has one normal blood sample and one breast tumor sample. Overall, there are 40 files included in this study, in the format of BAM.

Dataset EGAD00001005749

Single Cell Genome Sequence for DLP+ library A96109A

Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 2 on DLP+ library A96109A

Dataset EGAD00001009452

Single Cell Genome Sequence for DLP+ library A118782A

Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1050B passage 1, patient-derived xenograft SA1050E passage 1 on DLP+ library A118782A

Dataset EGAD00001009435

Genotyping of GM samples

This dataset contains a vcf file for 99 GM individuals genotyped on the Illumina HumanOmni2.5 array . The vcf file is originated after imputation (IMPUTE2) and filtering for minor allele frequency MAF≥0.05, imputation confidence score INFO of >0.4 and Hardy-Weinberg Equilibrium (HWE) p>1e-06, yielding ~6.3 million variants.

Dataset EGAD00001010255

A95707A

Whole genome sequencing for single cells for library A95707A 1359 samples; filetype=bam

Dataset EGAD00001008228

A96155B

Whole genome sequencing for single cells for library A96155B 1028 samples; filetype=bam

Dataset EGAD00001008236

A96165A

Whole genome sequencing for single cells for library A96165A 779 samples; filetype=bam

Dataset EGAD00001008239

Single Cell Genome Sequence for DLP+ library A118812B

Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1180C passage 1 on DLP+ library A118812B

Dataset EGAD00001009442

16S bacterial amplicon sequencing data for Guangzhou cohort

Dataset EGAD00001010268

GenomeDenmark Phase 2 - variants called on chrY for 62 males.

Variants on the Y chromosome for 62 danish males in VCF format from the GenomeDenmark Phase 2 cohort. Variants were called using reference based approaches such as the haplotype-caller module from GATK and using alignment of denovo assemblies to the reference using ASMvar.

Dataset EGAD00001003186

BLUEPRINT release August 2015, DNase-Hypersensitivity for monocyte, on genome GRCh38

DNase-Hypersensitivity data for 2 monocyte sample(s). 4 run(s), 2 experiment(s), 2 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_dnaseseq_analysis_20150820

Dataset EGAD00001001560

BLUEPRINT release January 2015, RNA-Seq for monocyte

RNA-Seq data for 8 monocyte sample(s). 8 run(s), 8 experiment(s), 8 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

Dataset EGAD00001001191

Carcinoid study - WGS dataset

Fernandez-Cuesta et al., 2014, Nature Communication, Whole genome sequencing was performed using a read length of 2x100 bp for all samples. On average, 110 Gb of sequence were produced per sample, aiming a mean coverage of 30x for both tumour and matched normal.

Dataset EGAD00001000813

BLUEPRINT release January 2015, DNase-Hypersensitivity for macrophage

DNase-Hypersensitivity data for 14 macrophage sample(s). 18 run(s), 14 experiment(s), 14 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_dnaseseq_analysis_20140811

Dataset EGAD00001001198

BLUEPRINT release August 2014, DNase-Hypersensitivity for macrophage

DNase-Hypersensitivity data for 1 macrophage sample(s). 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_dnaseseq_analysis_20140811

Dataset EGAD00001000931

Whole Exome Sequencing of healthy Spanish individuals - Fastq files

The need for a detailed catalogue of local variability for the study of rare diseases within the context of the Medical Genome Project motivated the whole exome sequencing of 267 unrelated individuals, representative of the healthy Spanish population.

Dataset EGAD00001001012

H3Africa ReMAC Shotgun Metagenomic Phenotype

Phenotype data for shotgun metagenomic sequencing data of nasopharyngeal fluid for studying nasopharyngeal colonization dynamics with Streptococcus pneumoniae and associated antimicrobial-resistance in a South African birth cohort. https://www.ebi.ac.uk/ena/data/view/PRJEB37312

Dataset EGAD00001006244

A Single-cell Atlas of Progressive TKI Resistance in Chronic Myeloid Leukemia

57 Bone marrow specimens for 5 healthy bone marrow and 24 CML samples profiled with 10X scRNA-seq 5' upon separation using MACS for CD34.

Dataset EGAD00001007946

NIDA Genetic Epidemiology of Opioid Dependence in Bulgaria (GEODB)

The study was conducted in Bulgaria as a collaboration between the Molecular Medicine Center (Medical University, Sofia), the Bulgarian Addictions Institute (Sofia), the Initiative for Health Foundation (Sofia) and Washington University School of Medicine (Saint Louis, MO, USA). This is a case-population control study. Participants (N=2,499 interviews; N=1,995 with GWAS data) in our study are primarily active heroin users (78%) aged 18 and over, with a median 8.9 years of daily heroin use (minimum of 12 months). Virtually all participants (99.1%) qualified for a lifetime DSM-IV diagnosis of dependence, symptom endorsement probabilities ranging from 77% to 99.3%; and 61% endorsing all symptoms. 85% were heroin injectors and 97% reported using (or having used) heroin daily. Anonymized, population-representative control samples (no phenotypic data, N = 1,062 with GWAS data) were drawn from the MMC repository. Unlike other studies, where samples are clinically ascertained (e.g., from ORT programs), our participants are primarily untreated. PTSD, major depression, suicidality, mania, alcohol, nicotine and substance dependence (separating heroin and opioids) were assessed using the MAGIC (Todd et al., 2003, PMID: 14627881), a broad semi-structured psychiatric assessment tool with good reliability and prospective stability. The onset of (any) illicit drug use in this sample is not particularly remarkable (age~16), but the age of onset of heroin use is much lower than in the USA: 25% by age 16; 50% by age 19, with no differences between ethnicities or gender. This is consistent with findings from other studies in Bulgaria. In contrast, in 2008, SAMHSA estimated an average age of 23.4 for new heroin initiates in the USA. Further, progression from first use to regular use appears to be more rapid, with the majority of participants reporting onset of dependence problems within one year of initiation. Another unique aspect of this study population are the comparatively low levels of polysubstance use (outside of nicotine and alcohol). This early-use, fast-transition and monosubstance heroin pattern stands in sharp contrast to what is reported in many studies in the Western World. In our study, 47% of Ethnic Bulgarians and 66% of Roma used only opioids, even years after the onset of drug use. 15.4% of Ethnic Bulgarians and 66.2% of Roma reported heroin as the very first illicit drug used. Lifetime, 12.5% of Ethnic Bulgarians and 52.8% of Roma report never having used another illicit drug other than heroin more than five times. While both Roma and Ethnic Bulgarians certainly experiment with other drugs, abuse/dependence rates for these drugs are low in Ethnic Bulgarians (from 8.4% for inhalants to 28% for stimulants) and in the Roma population (from 1.3% for hallucinogens to 11.2% for stimulants).

Study phs001804

Pathogenesis and Immunity in Endemic Burkitt Lymphoma

This genomic landscape of Burkitt lymphoma represents a multimodal sequencing of tumors and control tissues and individuals to better understand the etiology, and molecular pathogenesis of Burkitt lymphoma including the roles of the associated Plasmodium falciparum malaria and EBV infections. Comprehensive sequencing set includes genomic, transcriptomic, and epigenomic datasets in concert with variable clinical phenotypes and outcome information such as anatomical presentation site, in-hospital survival rates, and EBV genome type. This deposit includes additional small RNA-seq data from endemic BL (eBL) tumors and RNA-sequencing data from sorted NK cell subsets from the peripheral blood of eBL patients. The additional small RNA-seq data are from tumor specimens from 17 eBL cases, of the previously deposited polyA RNA-seq data from 28 eBL cases (phs1282.V1). The additional RNAseq data from Fluorescence-activated cell sorting (FACS) of NK cell subsets, isolated from the peripheral blood of 7 eBL cases. The additional RNAseq data are from eBL tumor samples of 5 eBL cases, BL cell lines established from these tumor samples and also NSG mouse tumors created by implanting the established BL cell lines into NSG mice. The dataset also includes RNAseq data of the NSG mouse tumors after treatment with Rituximab (RIT) and Phosphate buffered saline (PBS). The fifth batch of data includes mRNA-seq from different passages of a single eBL cell line that have variable EBV phenotypes (low passage with lytic EBV and high passage with latent EBV). This batch also includes lytic and latent lymphoblastoid cell line (LCL) clones from a single blood donor generated through infection with the strain of EBV isolated from the aforementioned eBL cell line. For initial transcriptome analysis see: Kaymaz et al. "Comprehensive Transcriptome and Mutational Profiling of Endemic Burkitt Lymphoma Reveals EBV Type-specific Difference." Molecular Cancer Research, 2017, PMID: 28465297. For initial microRNA analysis see: Oduor et al. "Human and Epstein-Barr Virus miRNA Profiling as Predictive Biomarkers for Endemic Burkitt Lymphoma." Frontiers in Microbiology 8, 2017, PMID: 28400759. For initial NK subsets RNA expression analysis see: Forconi et al. "A new hope for CD56negCD16pos NK cells as unconventional cytotoxic mediators: an adaptation to chronic diseases." Frontiers in Cellular and Infection Microbiology, 2020, PMID: 32373555.For initial eBL cell line and mouse avatar RNA expression analysis see: Saikumar et al. "Endemic Burkitt lymphoma avatar mouse models for exploring inter-patient tumor variation and testing targeted therapies." Life Science Alliance, 2023, PMID: 36878637. For general overview of cohort study see: Buckle et al. "Factors influencing survival among Kenyan children diagnosed with endemic Burkitt lymphoma between 2003 and 2011: A historical cohort study." Int J Cancer. 15:1231, 2016, PMID: 27136063.

Study phs001282

Sickle Cell Disease Natural History Data Resource (SCD NHDR)

Data Access NOTE Please refer to the “Authorized Access” section below for information about how access to the data from this accession differs from many other dbGaP accessions. BackgroundThe NHLBI initiated the Sickle Cell Disease Natural History Data Resource (SCD NHDR) to leverage existing SCD studies to build a data resource to provide phenotypic data on contemporaneous control cohorts for gene therapy trials and other studies. The potential uses of the NHDR are broad, including providing matched controls for clinical trials, observing a cohort of untreated patients as part of a natural history study, or conducting comparative effectiveness research on a subpopulation with selected co-morbidities or drug treatment profiles. The SCD NHDR includes two multi-center SCD longitudinal cohort studies with extensive annual clinical data and patient surveys, the Sickle Cell Disease Implementation Consortium (SCDIC-I and SCDIC-II) and the Globin Research Network for Data and Discovery (GRNDaD). The SCDIC-I, initiated in 2017 and concluded in June 2022, enrolled over 2400 patients with SCD from 8 US treatment centers. The SCDIC-II reconsented 1220 of the SCDIC-I patients as well as enrolling an additional 450 new patients from the same 8 centers between September 2022 and October 2023, for a total of 1670 in SCDIC-II. Only two of the many GRNDaD centers are submitting data to the SCD NHDR for deposit into BDC (approximately 122 patients). Data submissions from the SCD NHDR to BDC will be annually starting in early 2024. Description of the Study Data All data obtained for the SCD NHDR are considered common data elements (CDEs) important for studies of SCD. These CDEs were identified from several workgroups of SCD experts convened under the NHLBI's Cure-SC Initiative (https://curesickle.org/cde-catalog). The following types of data elements were abstracted from the medical record – variables that describe the SCD diagnosis, SCD co-morbidities and other clinical complications, elements from the physical examination, medications and transfusions, insurance type, laboratory measurements, healthcare utilization, history of SCD-related procedures, and measurements from cardiac procedures. The following types of data were self-reported on the patient survey – demographics, diagnosis information, pain experience, social and mental health information, other patient reported outcome domains (e.g., sleep, fatigue), and alcohol and smoking history. Standard PRO measures were used from ASCQ-Me, PROMIS and Neuro-QoL. Patient surveys were completed approximately annually. Under SCDIC-I, two medical record abstractions and labs were reported at enrollment and approximately 3 years later. Under SCDIC-II and GRNDaD, all data are collected annually. Patients may refuse to complete the survey.

Study phs003529

Providing safe access to sensitive human data across borders: Federated EGA becomes a reality

Enabling discovery and access to sensitive data across national boundaries is vital for improving human health. It enables more powerful and efficient research by increasing the volume and diversity of data available for analysis. It allows us to better understand the causes of diseases – cancer, rare diseases, infectious diseases like COVID-19 – and develop new medicines and treatments. Sensitive human omics data are typically generated by research initiatives and shared using specialist repositories which provide services for data submission, discovery, and access. The EGA is one such repository. Established in 2008 at EMBL’s European Bioinformatics Institute (EMBL-EBI) in the UK, since 2012 the EGA (“Central EGA”) has been jointly managed by EMBL-EBI and the Centre for Genomic Regulation (CRG) in Spain. Many countries have emerging personalised medicine programmes which generate data from national initiatives. These programmes are driving a transition in human genomics from being research-driven to receiving funding through healthcare. Data generated in a clinical context are subject to stricter governance than research data and must follow national data protection legislation. To solve these challenges, the Federated EGA provides a network of connected resources to enable transnational discovery of and access to human omics data for research, while also respecting jurisdictional data protection regulations. In this way, the Federated EGA infrastructure supports the goals of European initiatives such as the 1+ Million Genomes initiative (1+MG), the European Health Data Space, and a number of EU-funded 1+MG implementation projects including Beyond 1 Million Genomes. The Federated EGA is made up of “Nodes” – typically nationally funded and operated – which store and manage data locally while allowing global discovery within the Federated EGA network. Since 2016, multiple parallel efforts, supported by ELIXIR and other transnational and national initiatives, have created technical and legal frameworks for establishing the Federated EGA. In 2022, the first five Nodes – Finland, Germany, Norway, Spain, and Sweden – officially joined the Federated EGA by signing Federated EGA Collaboration Agreements with Central EGA. The Finnish FEGA Node is operated by CSC - IT Center for Science and provides data management services according to national laws and the requirements of the EU General Data Protection Regulation (GDPR). These services provide tools and support for the whole life-cycle of sensitive research data from collating to analysis, publication, and authorised re-use. The development of the services has been a joint effort with the other Nordic nodes within NeIC's Tryggve and Heilsa projects, and funded by Finnish Ministry of Education and Culture and projects coordinated by ELIXIR Finland. Read more about Finnish FEGA signing. The German Human Genome-Phenome Archive (GHGA) strives to provide a national infrastructure as well as an ethical and legal framework that balances FAIR omics data usage and data protection needs for Germany. As a Germany-wide consortium funded by the German Research Foundation under the umbrella of the NFDI association, GHGA combines the expertise of 21 universities and research institutions to form a federated national infrastructure. Read more about GHGA signing. The German Human Genome-Phenome Archive (GHGA) strives to provide a national infrastructure as well as an ethical and legal framework that balances FAIR omics data usage and data protection needs for Germany. As a Germany-wide consortium funded by the German Research Foundation under the umbrella of the NFDI association, GHGA combines the expertise of 21 universities and research institutions to form a federated national infrastructure. Read more about GHGA signing. In Norway, a key component of the infrastructure is the services of The Sensitive Data Service (TSD) offered by USIT at University of Oslo. The Federated EGA Norway Node is developed by ELIXIR Norway and operated by the University of Oslo as the responsible legal entity. Core software modules are developed jointly with the other Nordic nodes in the NeIC Tryggve and Heilsa projects. Read more about FEGA Norway signing. The Spanish FEGA (es-FEGA) is a national service for storing sensitive biomedical data in Spain. Supported by the Spanish Institute of Bioinformatics (INB) in collaboration with Central EGA, sensitive research datasets are primarily hosted at the Barcelona Supercomputing Centre facilities. The Swedish Sensitive Data Archive is a secure data archive and sharing platform for sensitive datasets. It was developed by the National Bioinformatics Infrastructure Sweden (NBIS) in collaboration with other Nordic ELIXIR Nodes through the Tryggve and Heilsa projects funded by NeIC and coordinated with Central EGA through ELIXIR. Read more about the Swedish Node signing. The Swedish Sensitive Data Archive is a secure data archive and sharing platform for sensitive datasets. It was developed by the National Bioinformatics Infrastructure Sweden (NBIS) in collaboration with other Nordic ELIXIR Nodes through the Tryggve and Heilsa projects funded by NeIC and coordinated with Central EGA through ELIXIR. Read more about the Swedish Node signing. By providing a solution for secure and efficient management of human omics data, the Federated EGA aims to foster data reuse, enable reproducibility, accelerate biomedical research, and improve human health. Find out more Interested in setting up your own Federated EGA Node? Check out the FEGA Onboarding Knowledge Base for more information. The ELIXIR Federated Human Data Community is a great entry point for anyone interested in learning more about the Federated EGA. You can: Join the ELIXIR Federated Human Data Community mailing list (select “Human Data”) Attend the ELIXIR Federated Human Data Community calls

Blog safe-access-to-sensitive-human-data-federated-ega

From research to data sharing: exploring EGA user's experiences

The Cell Plasticity and Regeneration Group at the Bellvitge Biomedical Research Institute-IDIBELL focuses on the process of recruitment of macrophages that takes place in the small intestine during injury and healing. They recently published a paper titled “Mucosal Macrophages Govern Intestinal Regeneration in Response to Injury" in Gastroenterology Journal. As part of the research, some experiments were conducted using human intestinal organoid lines. These cells were processed for RNA sequencing, and the sequencing data were deposited at the EGA to be made available to the scientific community. When dealing with human genomic information, repositories must ensure the availability of the datasets while ensuring data protection. In this context, the European Genome-phenome Archive stands as a service for secure archiving and sharing of genetic, phenotypic and clinical data resulting from biomedical research. Following the recent publication of their paper, we took the opportunity to talk to Ilias Moraitis, first author, and Jordi Guiu, group leader, to find out about their experience with data sharing. Could you explain the focus of your research? In the lab, we study intestinal regeneration and how immune cells participate in this process. We use several techniques: engineered mouse models, image tracing, as well as mouse and human cells intestinal organoids. What challenges do you face regarding data management? We didn’t have a lot of problems. Always the informatic part, the data analysis, can give problems. But everything was smooth and working. We think it’s very important to deposit the data in repositories. For the mouse data it is very straightforward, but for us it was the first time depositing human data, which is sensitive because it comes from patients, and this is legally regulated. That’s why we thought about the EGA, and it was our first time. Why do you think it is important to submit data to repositories such as EGA? Because we think data is important for the science. Nowadays, we are sequencing a lot everywhere worldwide and having these resources shared with the scientific community it’s not only good for science, but it is also saving money and reducing costs. And there is so much data in there that can be used for other projects and for other questions. And it saves time! How was the process of submitting the data to the EGA? The communication worked very well but it was slower than we would like. But I think this is something we learn on the way. People usually wait until the last minute to do this before publication but if you know it in advance, you can start the process earlier. If you had to repeat the process, would you do anything differently? It’s like everything, isn’t it? For the first time you don’t know, you aren’t sure, but for the second time you know the steps, you know what to do and it’s faster. Of course, we would to do it earlier. Also, there were some things related to the control access that we didn’t know how to manage, but it was something more internal to us. Who is going to receive the communications and when, is our ethical committee going to evaluate this? All these technicalities that we didn’t know before starting this process, so we had to think about all these things. Next time it will be faster on our side. Do you have any suggestions for us to improve the submission process? Maybe we’ll need to ask our bioinformatician who did it! He was in charge of this part. Besides that, we think if the process were faster, it would be better for everyone. Also, in science a lot of times you have to do a lot of things at the very last minute, because of the nature of the experiments, or the need to accomplish for submitting a paper. Being faster in the process would be a plus. On your side, we went through the process and were able to find some information, but there were aspects we weren't aware of and weren't sure how to handle. I'm not sure if other institutes have different protocols for managing data requests or how they handle these internally. We believe the main issue lies in how the forms are filled out. Do you have any recommendations for other submitters? The main advice is to do this with enough time. Submit the data when you have the sequences, and don’t wait until the last minute. And then it can be under embargo, so you don’t need to make it public at that moment. As soon as you sequence, upload it and then it will be there for whenever you need to publish. How do you think we could encourage other researchers to submit their data? There are different layers here. One is that it is mandatory. You have to do this. The other is that we belong to a research community, to the same community and sharing this is making our research community stronger and more efficient. And then, also because this gives you visibility. The data is there, other people can analyse your data, and this also will bring citations to your papers. It has many advantages.

Blog from-research-to-data-sharing

DAC for OAMZL

Dac EGAC00001003298

DAC for EGAS00001002275

Dac EGAC00001000600

DAC for IRCR dataset

Dac EGAC00001000693

DAC for IL2

Dac EGAC00001001176

DAC for Pearl

Dac EGAC00001002255

DAC for EGAS00001006660

Dac EGAC00001002900

DAC for glioblastoma studies

Dac EGAC00001003461

DAC for EGAS00001007510

Dac EGAC00001003402

CIDR: NINDS High Throughput Genotyping Resource Access for Structural Hindbrain Disorders

Recessive forms for many structural brain disorders (SBDs), including cerebellar hypoplasia (CBH), corpus callosum hypoplasia (CCH), cobblestone lissencephaly (COB), classical lissencephaly (LIS), microcephaly (MIC), and pontocerebellar hypoplasia (PCH), have been described. Patients usually present to the clinic with neurodevelopmental disorders (NDDs), including autism, epilepsy, intellectual disability, and cerebral palsy. Subsequent brain MRI usually leads to the diagnosis of an SBD. While a handful of genes that cause these diseases have been identified, most patients do not have mutations in these genes, suggesting that there is still much work to be done in identifying the genetic causes of these disorders. We have recruited a cohort of families with inherited SBDs, most from families with documented parental consanguinity with more than one affected member. Current approaches utilizing exome sequencing of SBD affected members still leave most cases unsolved, in part because there are too many possible variants to consider. Linkage analysis will allow us to extract all the useful genetic information from the family, taking advantage of each genetically informative meiosis in each family. We propose to analyze up to 100 SBD families for 500 samples per year for 4 years, through the CIDR SNP Genotyping Service, in parallel with already-funded exome sequencing of affects, leveraging off an already funded Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) effort to identify new causes of SBDs. Understanding the pathogenetic mechanisms underlying recessive pediatric SBDs will lead to better diagnostic and therapeutic opportunities for patients, and may shed light on complex NDDs. Ultimately, this work will identify new genetic disease loci to provide insight into pediatric SBD, with the hope to mitigate the impact of SBD worldwide.Public Health Significance: NDDs affect approximately 4-6% of the general population, most notably children, and are evident in disorders, such as intellectual disability, epilepsy, and autism, with estimated yearly costs of $51.2B for intellectual disability, $11.5B for cerebral palsy, and $2.5B for visual impairment, making up >5% of total health care costs. Recessive NDDs impose enormous personal, social, and economic costs because of the early onset and the lifetime of medical dependence that often ensues. Genetic testing is critical to the diagnosis and treatment of these diseases. Many of the diseases under study have no treatment or cure. Structural brain diseases (SBDs) underlie a large percentage of these diseases, and are defined loosely as any condition in which defects in the structure or volume of a key brain component. SBDs are divided into major categories based upon the anatomical brain location. The major types involve the anatomical structures of the developing neural tube, forebrain, hindbrain, cerebral cortex, and those affecting overall brain size. For the purposes of this application, we have excluded SBDs resulting from environmental causes (i.e. prematurity-induced), those below the brainstem, or those unlikely to be due to a germline or de novo mutation (i.e. focal SBDs or simplex disease).

Study phs002621

Submitting array based metadata

Submitting array based metadata For further information please check our Submission FAQs, submission quickguide as well as submission terms! The submission metadata required for Array-based submission must be submitted using EGA programmatic submission and by completing the Array-based format (AF) template. The guidelines for this workflow are described on this page. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA Array-based-Format (AF) template. You can request a legacy EGA submission account (ega-box-XXXX) by populating this form. Please, allow two business days for our Helpdesk team to contact you after populating this form. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA-Array-based-Format (AF) template. Metadata Model Registering Metadata Use the EGA programmatic submission to register your Study, Samples, Data Access Committee (DAC) and Policy. This online interface enables you to create new and edit existing submissions. Do not use the Submitter Portal to register the metadata objects for array submissions. Please, go to the EGA programmatic submission. Registering Study Go to the EGA programmatic submission Submit an study XML Take a note of your study accession number (EGASXXXXXXXXXXX) To use the study accession number in a publication, we suggest the following format: "Sequence data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX. Further information about EGA can be found on https://ega-archive.org "The European Genome-phenome Archive of human data consented for biomedical research"(https://doi.org/10.1093/nar/gkab1059 ). Registering Samples Go to the EGA programmatic submission Submit a sample XML Save the list of accession numbers. These will be needed for the AF template. Registering Data Access Committee Further information on the role of your DAC. Go to the EGA programmatic submission Submit a DAC XML Take a note of the DAC accession number (EGACXXXXXXXXXXX) Registering Policy Your Data Access Policy provides the terms and conditions of data use. This is also referred to as the Data Access Agreement (DAA). Completion of a DAA by the applicant/s should form part of the application process to the Data Access Committee (DAA) Go to the EGA programmatic submission Submit a Policy XML Take a note of the Policy accession number (EGAPXXXXXXXXXXX) Complete the Array-based format (AF) spreadsheet Once you have completed the registration of your Study, DAC and Policy using the programmatix submission, you must then complete and return the AF spreadsheet The AF spreadsheet consists on four components: Do not use EGA IDs registered using the Submitter Portal. You can easily identify objects registered in the Submitter Portal by their EGA ID pattern: EGA[A-Z]5{10 more digits} (e.g. for a sample EGAN50000002506). For populating an AF spreadsheet, please exclusively use EGA IDs following this specific pattern: EGA[A-Z]0{10 more digits} (e.g. for a sample EGAN00001691542). Tab1) Webin accessions : Provide the accession numbers for your study, DAC and policy. Please, also add your ega-box number. Tab2) Sample & phenotypes: Please, leave this tab blank. Tab3) Dataset: Describe the dataset to be created Tab4) Data files: Define how your data is going to be organised into datasets and packets for distribution (linkage between samples and files). Should further assistance be required after going through the guide below; please do not hesitate to contact the EGA helpdesk Once the AF spreadsheet is populated, please send it to our EGA helpdesk for further validation. AF spreadsheet Should your submission require multiple DAC's or policies, use ' ; ' to separate the accession numbers. Accessions AF spreadsheet: Samples & phenotypes Samples and phenotypes AF spreadsheet: Datasets We suggest that each dataset consists of a common set of data. The example below consists of two datasets, grouped according to shared data type, technology and by case/control. We also like to capture the number of unique samples that make up the dataset and the Data Access Committee (DAC) responsible for providing the named dataset and their policy (EGAP). Datasets AF spreadsheet: Data files What follows is an example of how to map your samples to the array based files added to your upload account (4th tab). Data Files Please, find below some practical examples on how to register the linkage between samples-files Case 1) 1 sample or list of samples in different datasets: Data Files In case you have a list of samples that belong to different datasets, please, repeat the samples accession number/s in the first column and link the sample to the corresponding dataset each time (each row). Each row is one linkage between sample-file-dataset. Case 2) 1 sample links to several files: Data Files In order to add multiple files to one sample you MUST use “ ; “ between filenames. Example: file1.gpg;file2.gpg;file3.gpg In case that you want to add an extra file to the sample (phenotype or .Rdata), please use “Additional files” column. Important note: You MUST upload the encrypted and unencrypted md5sum values of all files uploaded to your submission account using the filename nomenclature (file.gpg, file.md5,file.md5.gpg). Your submission will not be processed without md5values supplied for all files in the CORRECT format. What happens after the submission of a dataset? All datasets affiliated to unreleased studies are automatically placed on hold until the authorised submitted or DAC contact instructs our EGA helpdesk for the study to be released. Finally, your data is archived within our databases and prepared for encrypted distribution upon the request of permitted EGA account holders. We strongly advise you NOT to delete your data until we confirm that your data has been successfully archived.

Documentation submission/metadata/submission/array

Whole Transcriptome Sequencing of Human Tumor Cells and Hematopoietic Stem and Progenitor Cells During Aging and Bone Marrow Disorders

Whole transcriptome sequencing analyses were performed on human hematopoietic stem and progenitor cells during aging and bone marrow disorders. For this purpose, normal and diseased bone marrow or peripheral blood-derived mononuclear cells were FACS-purified into hematopoietic stem (CD34+CD38-Lin-) or hematopoietic progenitor (CD34+CD38+Lin-) populations. Primary human samples from healthy individuals were used along with comparative analyses in some pre-malignant and malignant conditions, including myelodysplastic syndrome (MDS), acute myeloid leukemia (AML) and plasma cell dyscrasias (e.g., multiple myeloma). Studies were also performed on total mononuclear cell fractions from patients with newly-diagnosed or relapsed multiple myeloma, or plasma cell leukemia. Briefly, RNA was extracted from all samples and quality was assessed using a Bioanalyzer instrument. Only samples with RNA integrity (RIN) values >7 were subjected to library preparation for RNA-sequencing. The SMARTer v4 cDNA amplification kit was used along with library construction by the NEB Ultra DNA kit for Illumina. Paired-end sequencing was performed on Illumina NextSeq instruments (Scripps Next Generation Sequencing Core), with all samples run over two separate flow-cells (to generate additional sequencing reads); >95M reads were generated in total for each sample. The RNA-sequencing data are available as FASTQ files.

Study phs002291

The National Heart, Lung, and Blood Institute (NHLBI)-funded Next Generation Genetic Association Studies (NextGen) Consortium: Phenotyping Lipid traits in iPS derived hepatocytes Study (PhLiPS Study)

The goal of the PhLiPS study is to create a library of induced pluripotent stem cell (iPSC) lines and iPSC-derived hepatocytes of diverse genotypes for use in metabolic profiling and interrogating lipid phenotypes. These cell lines were created as a part of the Next Generation Genetic Association Studies (Next Gen) Program, which was a five-year, $80 million program to investigate functional genetic variation in humans by assessing cellular profiles that are surrogates for disease phenotypes. To achieve this, researchers from multiple institutions across the U.S. were awarded grants to derive iPSC lines from more than 1,500 individuals representing various conditions as well as healthy controls for use in functional genomic ("disease in a dish") research. This extensive panel includes a diverse set of age, gender, and ethnic backgrounds, and therefore will be an invaluable tool for evaluations across demographics. Further enhancing the utility of these cell lines are data sets such as phenotyping, GWAS, genome sequencing, gene expression and -omics analyses (e.g., lipidomic, proteomic, methylomic) that can be matched to the cell lines. The PhLiPS Study focuses on individuals free of cardiovascular disease or with lipoprotein metabolism disorders in the community served by the Hospital of the University of Pennsylvania.

Study phs001341

CPTAC: Proteogenomic Studies of Ovarian Tumor Responses to Agents Targeting the DNA Damage Response

There is a critical unmet need for predictors of chemo-refractory High-grade serous ovarian cancer (HGSOC). Despite over 3 decades of research on platinum responses in cancer, no predictive biomarker has been translated into clinical use. Predictors of refractory disease could spare these patients the unnecessary toxicity of a platinum-based regimen and provide a means to triage these patients to clinical trials to identify effective therapies for refractory disease. This study has approached the development of a predictor in a novel way enabling detection of a multi-analyte panel biomarker capable of detecting a subset of refractory HGSOCs with high specificity and implicating potential treatment vulnerabilities for refractory disease. We reported the first proteogenomic analysis of platinum refractoriness in HGSOC and made the novel dataset available as a resource to the community, including a ProTrack portal: http://ptrc.cptac-data-view.org/. Detailed proteogenomic profiles were generated for this study from a retrospective cohort of 242 treatment-naive HGSOCs with enriched representation of "exceptional non-responders" (i.e., patients with chemo-refractory disease). Whole genome sequence (WGS) and RNA seq FASTQ files are available through dbGaP. Proteomic data are available through the NCI Proteomic Data Commons (PDC): https://pdc.cancer.gov/pdc/.

Study phs003152

Genetic Epidemiology of Lung Cancer Consortium GWAS of Familial Lung Cancer

Familial lung cancer cases were collected by the Genetic Epidemiology of Lung Cancer Consortium (GELCC) recruitment sites: University of Cincinnati, Karmanos Cancer Institute at Wayne State University, Louisiana State University Health Sciences Center-New Orleans, Mayo Clinic, and Medical College of Ohio. Familial cases for this study came from three sources: 1) one case each from families (at least 3 affected lung cancer cases in the family) included in the linkage analysis; 2) one case from linkage-eligible families (at least 3 affected lung cancer cases in the family) but where there were insufficient biospecimens available to make them informative for linkage analysis; and 3) one case from families not eligible for the linkage study with a family history of at least one first or second degree relative affected with lung cancer. Unrelated controls were selected from 1) among the spouses of family members, thus matching on socio-economic status (SES) and ethnicity (typically) of the cases or 2) from case-control studies of lung cancer conducted in the same location in which the linkage families were collected and matched on age, sex, and race. All cases and controls self-reported as European American. Biospecimens used for normal DNA extraction included blood, saliva or mouthwash. Some samples underwent whole genome amplication.

Study phs000629

Longitudinal Study of Bone and Endocrine Disease in Children with MPS I, II, and VI: A Multicenter Study of the Lysosomal Disease Network

Mucopolysaccharidoses (MPS) I, II, and VI are a group of rare, lysosomal storage diseases in which certain enzymes are missing that are normally required for breaking down specific complex carbohydrates. Deficiencies in these enzymes result in the build-up of toxic substances in various tissues and cause damage to the body's organs. In the past, children diagnosed with MPS I, II, or VI frequently died in early to late childhood. Recent advances in treatments including hematopoietic cell transplantation (HCT) and enzyme replacement therapy (ERT) have been helping children with these disorders live into adulthood. The long term-course of the disease is now unknown. Unfortunately, severe skeletal disease persists for most individuals despite current therapies. This is a longitudinal study of 50 children and adults with MPS I, II, and VI. Study participants will be evaluated every year for up to 3 years. The purpose of this study is to document the progression of skeletal disease and identify biomarkers that either predict disease severity or could be used as therapeutic targets. This information is needed to: determine long-term benefits and limitations of current treatments including HCT or ERT direct development of further treatment options improve clinical care for children affected by the disorder

Study phs001576

Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA)

The OPPERA project has two sub-studies investigating effects of genetic, physiological, psychological and clinical factors on the risk of developing painful temporomandibular disorder (TMD). OPPERA I baseline case-control study of chronic TMJD (dbGaP study accession phs000762). This sub-study uses a case-control study design to investigate characteristics associated with chronic TMD. The parent OPPERA phenotype file contains an indicator variable (PHASE1CASECONTROL) identifying participants in this sub-study. Data users should use the relevant case-classification variable (CHRNONICTMDCASE) and apply statistical methods appropriate for case-control studies when analyzing TMJD in this sub-study. OPPERA I prospective cohort for first onset of TMJD (dbGaP study accession phs000850). This sub-study uses a prospective cohort study design to investigate characteristics that predict risk of developing first-onset TMD. The parent OPPERA phenotype file contains an indicator variable (PROSPECTIVECOHORT) identifying participants in this sub-study. Data users should use the relevant case-classification variable (INCIDENTCASE) and the relevant time-in-study variable (FOLLOWUPYEARS) when analyzing TMJD in this sub-study. It is recommended that data users apply statistical methods appropriate for cohort studies of a dynamic population (for example, Cox regression models, where INCIDENTCASE is the censoring variable and FOLLOWUPYEARS is time in study).

Study phs000761

PIEZO1 Loss of Function Compound Heterozygous Mutation in the Rare Congenital Human Disorder Prune Belly Syndrome

Prune Belly Syndrome (PBS) is a rare congenital myopathy that is incompletely understood genetically. In this IRB approved study, one white Non-Hispanic 35 year-old male PBS proband was prospectively enrolled and phenotyped. Blood lymphocyte DNA from the proband underwent paired-end Whole Exome Sequencing using the Illumina SureSelect kit and HiSeq2500 sequencer with data pipeline analysis. We aligned the subject's sequence against the GRCh38/hg19 assembly and variant calling was performed using data training sets from the 1000 Genomes Project, Omni 2.5 M SNP microarray, and HapMap phase 3.3. These variants were filtered for rare minor allele frequency ( 0.9, CADD score > 20, GERP > 4, MutationTaster = “DC”, Mutation Assessor = “M” or “NE”, and Vest > 3.00. Rare and novel candidate functional variants were screened in the ClinVar database for prior association with smooth muscle myopathy (SMM). Only variants that met these criteria were included for further analysis. Confirmed variants had inheritance analysis performed on available samples. Our WES analysis identified novel compound heterozygous variants in the PIEZO1 gene. We conducted an extensive functional analysis of the PIEZO1 PBS variants that revealed loss-of-function characteristics. Thus, PIEZO1 mutations may be causal for PBS. Our WES data will be available through dbGaP.

Study phs003475

Tumor detection by analysis of both symmetric- and hemi-methylation of plasma cell-free DNA

Aberrant DNA methylation plays a critical role in tumorigenesis. While DNA methylation has been used for cancer detection and classification, DNA hemi-methylation, a novel epigenetic mark has not been analyzed extensively in cancer epigenomes. Here we report a strand-specific (ss) sequencing method (MeDIP-Seq) for plasma cell free (cf) DNA (sscf-MeDIP-Seq), which can analyze both symmetrically methylated DNA regions (DMRs) as well as hemi-methylated regions (DHMRs). Using the sscf-MeDIP-Seq method, we analyzed plasma cfDNA methylomes of 271 samples from subjects with liver cancer and brain cancer and from individuals without cancer (controls). Among them, 215 samples were chosen as the discovery cohort for the identification of DMRs and DHMRs specific in each subject group and for the training of machine learning models of multi-cancer detection (MCD) using DMRs, DHMRs and DMRs+DHMRs as inputs. These models were then used to predict the 56 samples in the validation cohort. We found that models trained with DMRs+DHMRs as inputs in general outperformed models trained with DMRs or DHMRs alone, with AUROC being 0.971, 0.981, and 0.99 in predicting control, liver and brain cancer samples in the validation cohort, respectively, by the DMR+DHMR-trained models.

Study phs003462

IMPRESS: Improved methylation profiling using restriction enzymes and smMIP sequencing, combined with a new biomarker panel, creating a multi-cancer detection assay

Background Despite the worldwide progress in cancer diagnostics, more sensitive diagnostic biomarkers are needed. The methylome has been extensively investigated in the last decades, but a low-cost, bisulfite-free detection method for multiplex analysis is still lacking. Methods We developed a methylation detection technique called IMPRESS, which combines methylation-sensitive restriction enzymes and single-molecule Molecular Inversion Probes. We used this technique for the development of a multi-cancer detection assay for eight of the most lethal cancer types worldwide. We selected 1791 CpG sites that can distinguish tumor from normal tissue based on DNA methylation. These sites were analysed with IMPRESS in 35 blood, 111 tumor and 114 normal samples. Finally, a classifier model was built. Results We present the successful development of IMPRESS and validated it with ddPCR. The final classifier model discriminating tumor from normal samples was built with 358 CpG target sites and reached a sensitivity of 0.95 and a specificity of 0.91. Moreover, we provide data that highlight IMPRESS’s potential for liquid biopsies. Conclusions We successfully created an innovative DNA methylation detection technique. By combining this method with a new multi-cancer biomarker panel, we developed a sensitive and specific multi-cancer assay, with potential use in liquid biopsies.

Study EGAS50000000624

Single-Cell Genomic and Transcriptomic Analysis of the Aging Human Brain

We studied human brain aging using single-cell whole genome sequencing and single-nucleus RNA sequencing of the pre-frontal cortex from 19 donors ranging in age from 0.4 years to 104 years. All donors were free of neuropathology and none received a clinical neurological diagnosis prior to death. snRNA-seq libraries were prepared in batches that always included old and young donors to mitigate batch effects in the preparations. Neurons for single-cell WGS were collected from the same tissue region as the snRNA-seq for each donor but represent independent cells. Multiplexed error-robust fluorescent in-situ hybridization (MERFISH) data was also collected from pre-frontal cortex samples of a subset of the 19 donors. Transcriptomic results identified infant-specific cell clusters, and common-down regulation of cell-essential genes involved in ribosomes, transport, homeostasis and metabolism during aging, across all cell types. scWGS identified two-age associated mutational signatures, as well as gene-length and expression-level dependent somatic mutation rates in neurons. Raw sequencing data in fastq format for single-nucleus RNA-seq and single-cell DNA-seq, as well as cell and gene count matrix, transcript metadata, and cell metadata for MERFISH will be available in dbGaP.

Study phs003445

BHD-associated kidney cancer

Background Birt-Hogg-Dub? (BHD) syndrome is a hereditary kidney cancer syndrome, which predisposes patients to develop kidney cancer, cutaneous fibrofolliculomas and pulmonary cysts. The responsible gene, FLCN is a tumor suppressor for kidney cancer which plays an important role for energy homeostasis through the regulation of mitochondrial oxidative metabolism. However, how FLCN-deficiency leads to renal tumorigenesis is unclear. Results Whole-exome sequencing analysis of BHD-associated kidney cancer revealed that copy number variations (CNV) of BHD-associated kidney cancer are considerably different from those already reported in sporadic cases. Somatic mutation analysis revealed BHD-associated kidney cancer has quite few common mutations in specific genes; however, chromatin remodeling genes in total were frequently mutated in BHD-associated kidney cancer (17/29 tumors, 59%). Metabolite analysis revealed that glycolysis, pentose phosphate pathway and lipid metabolism were upregulated whereas the rate of beta oxydation and TCA cycle is decreased in BHD-associated kidney cancer compared to normal adjacent kidney. Conclusions BHD-associated kidney cancer displays a unique molecular characteristics which is totally different from that of sporadic kidney cancer, providing the mechanistic insight into tumorigenesis under FLCN deficiency as well as the foundation for the development of novel therapeutics for kidney cancer.

Study JGAS000115

Assessing gene expression profiling from FFPE Patient Samples: A Comparison of Two Library Preparation Approaches and Recommendations

Next-Generation Sequencing (NGS) has transformed cancer research and clinical practice, with Whole Exome Sequencing (WES) driving advances in mutational profiling and personalized oncology. Yet, transcriptomic signatures remain essential for understanding disease mechanisms, including therapy resistance pathways. RNA sequencing (RNA-seq), however, faces unique challenges when dealing with low-input or degraded RNA, as often found in archival formalin-fixed paraffin-embedded (FFPE) tissues. Although previous studies have compared library preparation protocols, rapidly evolving technologies call for updated evaluations. Here, we present a direct comparison of two FFPE-compatible stranded RNA-seq library preparation kits: TaKaRa™ SMARTer® Stranded Total RNA-Seq Kit v2 (Kit A) and Illumina Stranded Total RNA Prep Ligation with Ribo-Zero Plus (Kit B). Both kits generated high-quality RNA-seq data, yet important differences emerged. Notably, Kit A achieved comparable gene expression quantification to Kit B while requiring 20-fold less RNA input, a crucial advantage for limited samples, albeit with increased sequencing depth. We critically discuss these results in relation to RNA availability, technical performance, cost-effectiveness, processing time, and automation potential, offering practical guidance for selecting optimal RNA-seq strategies in clinical and translational research settings

Study EGAS50000001066

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