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• Guardians_of_the_genome__protecting_DNA_from_endogenous_sources_of_damage_

  Analysis of mutational signatures caused by DNA repair defects in human induced pluripotent stem (iPS) cells. A reference human iPS cell line will be used for genetic manipulation to introduce homozygous knockouts of 100 genes known to be involved in or connected to DNA repair or DNA editing. Following a defined period of growth after homozygous knockout of each gene, sub clones will be generated and sequenced. The progenitor “parental” IPS cell line will be used to generate reference sequence data, in order to determine the mutational signature acquired due to the gene knockout.

  Study EGAS00001000800

• Genetics of non-syndromic idiopathic autism spectrum disorders in India

  The study explores the genetic architecture of patients diagnosed with non-syndromic autism spectrum disorders (according to the DSM-V criteria) in India. Patients were previously screened for aneuploidies using chromosomal G-banded karyotyping (at 500 band resolution) and Fragile-X using TP-PCR. Whole exome sequencing was performed on germline DNA from 100 idiopathic patients. Target enrichment was carried out using either Agilent SureSelect V6 or SureSelect Clinical Research Exome V2 kits. Pooled libraries were sequenced on Illumina HiSeq platform at an average coverage of 80-100x in 2x150bp configuration.

  Study EGAS00001006060

• Predicting the Prevalence of Complex Genetic Diseases from Individual Genotype Profiles Using Capsule Networks

  Diseases with a complex genetic architecture, such as amyotrophic lateral sclerosis (ALS) as a most prominent example, are hard to disentangle. "Human mind-friendly" methods, such as linear approaches used in genome wide association studies (GWAS), remain blind to many genetic variants that matter. On the other hand, methods that are sufficiently sophisticated to entirely capture the characteristic interplay of genetic variants remain black boxes for the human mind. Here, we give this a decided, and as we argue, a very promising try. We present DiseaseCapsule, a capsule network-based approach that enables us to predict prevalence/occurrence of a genetically complex disease from individual genotype profiles. Importantly, capsule networks are considerably easier to decompose into their components than ordinary deep neural networks, which promotes the interpretability of their results. The data we used are from Project MinE, a large-scale study that aims to reveal the genetic and epigenetic mechanisms that underlie ALS in the framework of a globally concerted collaboration. Specifically, we used data from the Dutch cohort of the project, which contains 7213 healthy ('control') individuals and 3192 individuals affected with ALS. The cohort includes 5208 females and 5197 males. All participants of the study were genotyped using an Illumina 2.5M SNP array.

  Study phs003146

• Single-cell RNA and TCR sequencing of 16 PSCC and 6 non-malignant samples

  We included 16 fresh tumor samples of biopsy-proven invasive penile squamous cell carcinomas and 6 adult non-malignant inner prepuce samples. Single-cell RNA sequencing was performed (10x genomics). An HPV reference genome of 15 high-risk HPV types was generated and we mapped all single cell reads to the GRCh38 human and HPV reference genome using CellRanger. Targeted next-generation sequencing (tNGS) was performed for the detection of TP53 loss-of-function mutations.

  Dataset EGAD50000000317

• Comprehensive single cell study of lung adenocarcinoma from early to metastatic stages

  We performed single cell RNA sequencing (scRNA-seq) for 208,506 cells derived from 58 lung adenocarcinomas from 44 patients, which covers primary tumour, lymph node and brain metastases, and pleural effusion in addition to normal lung tissues and lymph nodes. The extensive single cell profiles depicted a complex cellular atlas and dynamics during lung adenocarcinoma progression which includes cancer, stromal, and immune cells in the surrounding tumor microenvironments.

  Dataset EGAD00001005054

• Dataset of 4 WGS and 35 WES tumor and control runs linked for the ImmuNEO publication out of EGAS00001004813

  Sequencing data of 39 tumor and control runs (different tumors and blood controls), which were uploaded to EGAS00001004813 and reused in this ImmuNEO publication. The sequencing was always paired.

  Dataset EGAD00001009671

• Circulating tumor DNA, pathological and immunologic responses to neoadjuvant nivolumab or nivolumab plus relatlimab and chemoradiotherapy in resectable esophageal/gastroesophageal junction cancer

  The dataset for the study “Circulating tumor DNA, pathological and immunologic responses to neoadjuvant nivolumab or nivolumab plus relatlimab and chemoradiotherapy in resectable esophageal/gastroesophageal junction cancer” includes 173 bam files from hybrid capture targeted error-correction next-generation sequencing (TEC-Seq) from plasma cell-free DNA and matched white blood cell DNA from 32 individuals with esophageal/gastroesophageal junction cancer, who received immunotherapy-containing regimens.

  Dataset EGAD00001011822

• The BEACCON study: tumour sequencing

  This dataset consists of paired-end DNA sequencing (whole exome and targeted-capture) of tumours for the BEACCON study. There are 240 unique samples consisting of 92 matched tumour-germline pairs and 56 unmatched tumours totalling 148 patients. There are 33 paired and 6 unpaired tumours sequenced using the Agilent SureSelect All Human Exon v6 libraries, 1 paired and 8 unpaired tumours sequenced using Agilent SureSelect All Human Exon v7 libraries and 48 paired and 3 unpaired tumours sequenced using Twist Bioscience Comprehensive Human Exome v1 libraries totalling 176 whole exome samples. There are 3 paired and 58 unpaired tumours sequenced using a custom Agilent SureSelectXT library totalling 64 targeted capture samples.

  Dataset EGAD00001009299

• BLUEPRINT DNase accessibility of Epigenetic programming during monocyte to macrophage differentiation and trained innate immunity

  Monocyte differentiation into macrophages represents a cornerstone process for host defense. Concomitantly, immunological imprinting of either tolerance or trained immunity determines the functional fate of macrophages and susceptibility to secondary infections. Transcriptomes and epigenomes in four primary cell types: monocytes, in vitro differentiated naïve, tolerized and trained macrophages were characterized. Inflammatory and metabolic pathways were modulated in macrophages, including decreased inflammasome activation, and pathways functionally implicated in trained immunity were identified. Strikingly, β-glucan training elicits an exclusive epigenetic signature, revealing a complex network of enhancers and promoters. Analysis of transcription factor motifs in DNase I hypersensitive sites at cell-type specific epigenetic loci unveiled differentiation and treatment specific repertoires. Altogether, this study provides a resource to understand the epigenetic changes that underlie innate immunity in humans.

  Study EGAS00001000954

• BLUEPRINT RNA-seq of Epigenetic programming during monocyte to macrophage differentiation and trained innate immunity

  Monocyte differentiation into macrophages represents a cornerstone process for host defense. Concomitantly, immunological imprinting of either tolerance or trained immunity determines the functional fate of macrophages and susceptibility to secondary infections. Transcriptomes and epigenomes in four primary cell types: monocytes, in vitro differentiated naïve, tolerized and trained macrophages were characterized. Inflammatory and metabolic pathways were modulated in macrophages, including decreased inflammasome activation, and pathways functionally implicated in trained immunity were identified. Strikingly, β-glucan training elicits an exclusive epigenetic signature, revealing a complex network of enhancers and promoters. Analysis of transcription factor motifs in DNase I hypersensitive sites at cell-type specific epigenetic loci unveiled differentiation and treatment specific repertoires. Altogether, this study provides a resource to understand the epigenetic changes that underlie innate immunity in humans.

  Study EGAS00001000953

• BLUEPRINT ChIP-seq of Epigenetic programming during monocyte to macrophage differentiation and trained innate immunity

  Monocyte differentiation into macrophages represents a cornerstone process for host defense. Concomitantly, immunological imprinting of either tolerance or trained immunity determines the functional fate of macrophages and susceptibility to secondary infections. Transcriptomes and epigenomes in four primary cell types: monocytes, in vitro differentiated naïve, tolerized and trained macrophages were characterized. Inflammatory and metabolic pathways were modulated in macrophages, including decreased inflammasome activation, and pathways functionally implicated in trained immunity were identified. Strikingly, β-glucan training elicits an exclusive epigenetic signature, revealing a complex network of enhancers and promoters. Analysis of transcription factor motifs in DNase I hypersensitive sites at cell-type specific epigenetic loci unveiled differentiation and treatment specific repertoires. Altogether, this study provides a resource to understand the epigenetic changes that underlie innate immunity in humans.

  Study EGAS00001000951

• DNA Replication Timing Alterations in Genetic Diseases

  This study contains a collection of lymphoblastoid cell line (LCL) and induced pluripotent stem cell (iPSC) whole genome sequencing from individuals with an without a diagnosed genetic disease. Several types of genetic diseases are included such as translocations, repeat expansions, and heritable or spontaneous mutations of genes. We used the whole genome sequencing to generate DNA replication timing profiles for these individuals and compared the profiles of diseased individuals to presumed healthy samples. We aimed to identify replication timing alteration linked to a specific genetic disease and therefore elucidate trans-acting factors of DNA replication timing. We found that replication timing was highly consistent across almost all diseased samples. Only one genetic disease, a mutation in MCM10, demonstrated an altered profile of replication timing.

  Study phs002597

• 4 - transcriptome (.bam) files, 2 -called somatic mutations (.vcf) files and 2 coverage (.csv) files

  The dataset contains 4 .bam whole transcriptome files that were generated from 2 tumor samples (one .bam file per sample was created using exome-capture (hybrid capture) and the other .bam file was created using ribo-repletion (inverse mRNA selection) library preparation technology). The dataset also contains 2 somatic mutations (.vcf - sample) files (one per sample) which serve as input for Vardetector package (vardetector/vardetector at main · julijselb/vardetector) to produce somatic coverage over those mutations from the .bam file. Therefore, the dataset also contains 4- .csv files (number of reads per somatic mutation that contain the somatic mutation variant and the ones that do not contain the somatic mutation variant) that serve as outputs of the Vardetector package.

  Dataset EGAD50000002055

• Avelumab or M7824 for People with Recurrent Respiratory Papillomatosis

  This submission includes genomic data derived from the study of clinical samples from two different phase II trials. These trials evaluated the clinical efficacy and safety of avelumab or M7824 in the treatment of adults with recurrent respiratory papillomatosis (RRP). Subjects with aggressive RRP were treated with three to six doses of avelumab or M7824. Pre-treatment or post-treatment snap frozen papilloma and normal mucosa samples were subjected to whole exome sequencing and bulk RNA-seq. A total of twenty subjects were enrolled and treated. 

  Study phs002373

• Nala TAS-LRS PGx Study

  Sequencing was performed on the PromethION Flow Cell R10 (M version) using the P2 Solo Sequencer (MinION release 24.02.16). The library was divided into three portions and loaded in a tapered manner across three time points. At the first time point, the initial portion of the library was loaded, and sequencing began in whole-genome mode (without adaptive sampling), to monitor QC (N50 within the expected range). After one hour, adaptive sampling was activated. A custom BED file was used to define the target regions (N=326). Each region was extended by 20 kb upstream and downstream, and overlapping regions were merged into non-redundant intervals using bedtools43, covering a total of 1.3% of the human genome. At the second time point (20–24 hours after sequencing began, or when fewer than 2000 pores remained active), the flow cell was washed following ONT’s Flow Cell Wash Kit protocol, and the second portion of the library was loaded. At the third time point (40–48 hours), the second portion of the library was retrieved, and merged with the third portion. The flow cell was washed and reloaded with the mixed library. POD5 files were basecalled using Dorado v0.8.1 in super-accuracy (SUP) mode with the dna_r10.4.1_e8.2_400bps_SUP@v5.0.0. Reads from the initial 1 h Whole Genome Sequencing (WGS) and subsequent 72 h adaptive sampling were filtered for Q-scores >10. Passing reads were demultiplexed using Dorado’s demux function, then combined per sample and mapped to the GRCh38 reference genome using Minimap2 v2.22. Files were converted to CRAM using samtools.

  Dataset EGAD50000001609

• Whole exome and RNA sequencing of organoid samples derived from TRACERx patients

  Immune checkpoint blockade in non-small cell lung cancer (NSCLC) rarely leads to complete tumour remission, suggesting that tumours harbour subpopulations of cancer cells that can escape immune predation. However, while tumours consist of multiple genetically distinct clones, the extent to which separate tumour subclones differ intrinsically in their functional capacity for immune evasion remains largely unexplored. This has been challenging to address due to an inability to isolate and propagate individual tumour subclones from human cancers. Here, we leverage the multi-region TRACERx lung cancer evolution study to generate a patient-derived platform that allows the functional analysis of immune escape at single clone resolution. We generated libraries of >20 separate NSCLC organoid lines per patient by isolating individual (clonal) organoids established from multiple spatially separated tumour regions. Each organoid subline was co-cultured with autologous tumour infiltrating lymphocytes (TIL) to evaluate their capacity to elicit a T cell response. Organoid lines derived from separate tumour regions, or from single clones within individual regions, differ vastly in their capacity to elicit a T cell response, showing the coexistence of intrinsically immune evading (‘hot’) and non-evading (‘cold’) clones in the same primary tumour. Organoid lines escaping T cell recognition represent genetically and transcriptionally unique subclones with a distinct evolutionary history, showing that evolutionary sculpting of subclonal diversity can have functional consequences for anti-tumour immunity. Notably, the identification of immune-evasive subclones would not have been possible using computational approaches alone, highlighting the unique value of functional co-culture platforms. This patient-derived functional co-culture platform reveals that tumour evolution can give rise to distinct cancer clones with intrinsic differences in immune evasion capacity and provides an approach to prospectively identify, isolate and characterise immune evading subclones from patients with cancer.

  Study EGAS00001008092

• Genomic Analysis of Diffuse Large B Cell Lymphoma

  Tumors from 383 newly diagnosed diffuse large B cell lymphoma were included in this study. Tumor DNA was extracted from formalin-fixed, paraffin embedded (FFPE) tissue sections, germline DNA was extracted from blood, and whole exome sequencing (WES) of all samples (n=340 tumor normal pairs) was performed using the Agilent SureSelect XT AllExon v5 + UTR kit and sequencing was carried out on an Illumina NovaSeq, 100 x 2 paired end reads. Tumor RNA (n=321) was extracted from formalin-fixed, paraffin embedded (FFPE) tissue sections and RNA sequencing was performed using the Illumina TruSeq RNA Exome Kit (Illumina) for library preparation, sequencing platform HiSeq 4000, 100 x 2 paired end reads.

  Study phs003634

• WGS data from COMPASS Trial

  WGS data for pancreatic cancer samples (COMPASS Trial; Knox et al., Journal of Clinical Oncology, 2025). The dataset includes tumour and normal BAM files sequenced on Illumina HiSeq/NovaSeq and aligned against GRCh38 (n=263 tumour/normal pairs).

  Dataset EGAD50000001832

• banfora_20150706_X

  Genotype data (in binary PLINK format) and imputed data (with merged 1000Gp3 and AGVP reference panel in GEMMA BIMBAM dosage format) for 355 individuals from the VAC050 trial performed in Banfora, Burkina Faso - X chromosome.

  Dataset EGAD00010002579

• The Epigenomic Atlas of Early Human Craniofacial Development

  Parallel profiling of six distinct histone modifications via ChIP-Seq in craniofacial tissue samples from individual human embryos was performed. Epigenomic marks imputed with ChromImpute and a 25-state chromatin state model of uniformly processed epigenomic data was produced through ChromHMM. Samples were obtained from the MRC-Wellcome Trust Human Developmental Biology Resource (HDBR) tissue bank. HDBR provided karyotypically and developmentally normal pharyngeal arch or craniofacial tissue from embryos between 4.5 and 10 post-conception weeks. This repository contains raw data (fastq) files from ChIP-seq and RNA-seq experiments associated with this project. Processed ChIP-seq data and imputed data for additional histone marks are available at GEO (Series GSE97752).

  Study phs002008

• Hypertension delays viral clearance and exacerbates airway hyperinflammation in patients with COVID-19

  In Coronavirus Disease 2019 (COVID-19), hypertension and cardiovascular diseases are major risk factors for critical disease progression. However, the underlying reasons and the effect of the main anti-hypertensive therapies—angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs)—remain unclear. Combining clinical data (n = 144) and single-cell sequencing data of airway samples (n = 48) with in vitro experiments, we observed a distinct inflammatory predisposition of immune cells in patients with hypertension that correlated with critical disease progression. ACEI treatment associated with dampened COVID-19-related hyperinflammation and with increased cell intrinsic anti-viral responses, whereas ARB treatment related to enhanced epithelial–immune cell interactions. Macrophages and neutrophils of patients with hypertension, in particular under ARB treatment, exhibited higher expression of the pro-inflammatory cytokines CCL3 and CCL4 and the chemokine receptor CCR1. Although the limited size of our cohort does not allow us to establish clinical efficacy, our data suggest that the clinical benefits of ACEI treatment in patients with COVID-19 who have hypertension warrant further investigation.

  Dataset EGAD00001006828

• stilts-G-1

  Human Core Exome Genotyping for 1471 samples from the STudy Into Lean and Thin Subjects (STILTS) cohort

  Dataset EGAD00010001622

• ProstOmics: spatial and bulk multi-omics of prostate cancer

  In this ERC-funded ProstOmics project we have used spatial and bulk multi-omics on fresh frozen prostate cancer samples to investigate cancer biology and find for biomarkers to improve patient treatment. All tissue material were collected from prostate cancer patients undergoing radical prostatectomy who had not received any prior cancer-specific treatment.Of the 498 prostate tissue samples (114 patients) included in our project, 176 samples (N=37 patients) have been analyzed with bulk transcriptomics (RNA-seq). Some of these 176 samples were also analyzed with spatial transcriptomics (n=32, N=8, Visium 10x RNA-seq) and DNA methylomics (n=96, N=24, array). All datasets include metadata for histopathological evaluation. Patient metadata include information of age at surgery, time (months) until reported relapse, pre-surgery PSA and post-surgery T-stage.

  Study EGAS50000000413

• Spatial and temporal diversity in genomic instability processes define early stage lung cancer evolution.

  Deciphering the complexity of non-small cell lung cancer (NSCLC) evolutionary histories may elucidate the basis for its dismal outcome. We performed multi-region sequencing on 25 regions from seven early-stage NSCLCs. Spatial dissection revealed branched tumor evolution, with driver mutations occurring before and after subclonal diversification. Intra-tumor heterogeneity in DNA copy number alterations, translocations and endogenous APOBEC mutational processes, demonstrates that NSCLC may follow multiple distinct evolutionary trajectories simultaneously. In smokers, despite maintained carcinogen exposure, temporal dissection reveals a proportional decrease in smoking-related mutations during tumor progression, accompanied by an increase in APOBEC-related mutations. We observed genome-doubling events early in tumor development of ex-smokers, suggesting prolonged latency periods before clinical detection. Multi-region sequencing demonstrates the relentless and heterogeneous nature of genomic instability processes in early-stage NSCLCs.

  Study EGAS00001000840

• Target gene sequencing for human normal endometrial glands

  To elucidate the timing and mechanism of the clonal expansion of somatic mutations in cancer-associated genes in the normal endometrium, we conducted target sequencing of 112 genes for 1,298 endometrial glands and matched blood samples from 36 women. By collecting endometrial glands from different parts of the endometrium, we showed that multiple glands with the same somatic mutations occupied substantial areas of the endometrium. The 112 genes are as follows: ABCC1, ACRC, ANK3, ARHGAP35, ARID1A, ARID5B, ATCAY, ATM, ATR, BARD1, BCOR, BRCA1, BRCA2, BRD4, BRIP1, CAMTA1, CDC23, CDYL, CFAP54, CHD4, CHEK1, CHEK2, CTCF, CTNNB1, CUX1, DGKA, DISP2, DYNC2H1, EMSY, FAAP24, FAM135B, FAM175A, FAM65C, FANCA, FANCB, FANCC, FANCD2, FANCE, FANCF, FANCG, FANCI, FANCL, FANCM, FAT1, FAT3, FBN2, FBXW7, FGFR2, FRG1, GPR50, HEATR1, HIST1H4B, HNRNPCL1, HOOK3, KIAA1109, KIF26A, KMT2B, KMT2C, KRAS, LAMA2, LRP1B, MLH1, MON2, MRE11A, MSH2, MSH6, MTOR, NBN, PALB2, PHEX, PIK3CA, PIK3R1, PLXNB2, PLXND1, PMS2, POLE, POLR3B, PPP2R1A, PTEN, PTPN13, RAD50, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54B, RAD54L, RICTOR, SACS, SIGLEC9, SLC19A1, SLX4, SPEG, STT3A, TAF1, TAF2, TAS2R31, TFAP2C, TNC, TONSL, TP53, TTC6, UBA7, VNN1, WT1, XIRP2, ZBED6, ZC3H13, ZFHX3, ZFHX4, ZMYM4.

  Study EGAS00001005914