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• Purified vs unpurified stem cell-islets

  A scRNAseq dataset consisting of stem cell-derived islets treated with gradient separation (HUES8_Pure_Islets) and unpurified control (HUES8_control_islets). scRNAseq was done on cryopreserved stage 7 stem cell (HUES8 cell line)-derived islets. Details about stem cell differentiation and sequencing can be found on the publication by Rajaei et al. (2025, Science Translational Medicine). This dataset correspond to Fig 4 of the publication.

  Dataset EGAD50000001396

• Thirty cutaneous SCC WES tumour samples with matched normal

  Thirty cutaneous SCC WES tumour samples with matched normal include 20 samples from South et al. JID and 10 new samples. These 30 samples has been used to support the findings in the TGFb Nature Communications paper (DOI: 10.1038/ncomms12493). They are also a part of the ongoing study of cSCC genomic landscape of 40 cSCC samples in total.

  Dataset EGAD00001002253

• Sequencing data for ICGC Oesophageal Adenocarcinoma tissue samples - barretts_1

  Samples from Ross Innes et. al 2015 - doi:10.1038/ng.3357

  Dataset EGAD00001001394

• Capture Hi-C on Hodgkin lymphoma

  In situ promoter capture Hi-C on Hodgkin lymphoma cell line L-428 in experimental triplicates. Hi-C libraries were prepared as previously described (Orlando et al., 2018, https://currentprotocols.onlinelibrary.wiley.com/doi/pdf/10.1002/cphg.63). Promoter capture was based on 32,313 biotinylated 120-mer RNA baits (Agilent). Hi-C libraries were sequenced using Illumina HiSeq 2000 technology. The files are in FASTQ format.

  Dataset EGAD00001004321

• 10x genomics RNAseq of an isogenic human iPSC model of SMA

  10x genomics single-cell RNAseq of an isogenic human iPSC model for SMA and control. The transcriptomic analysis was performed at 3 timepoints, day 4, day 20 and day 40. The analysis of this dataset was reported in the manuscript "An isogenic human iPSC model unravels neurodevelopmental abnormalities in SMA" from Grass et al.:

  Dataset EGAD00001011259

• Metagenome for FINRISK2002

  The dataset contains data for n=7211 FINRISK 2002 participants who underwent fecal sampling. Demultiplexed shallow shotgun metagenomic sequences were quality filtered and adapter trimmed using Atropos (Didion et al., 2017), and human filtered using Bowtie2 (Langmead and Salzberg, 2012). The files are in FASTQ format.

  Dataset EGAD00001007035

• Genomic Profiling of Peripheral T-cell Lymphoma

  Peripheral T-cell lymphomas (PTCLs) are malignant and highly aggressive hematologic tumors arising from mature post-thymic T cells. The diagnosis of PTCL includes diverse lymphoma subgroups, altogether accounting for about 15% of all non-Hodgkin lymphomas. Despite recent advances in uncovering the mutational landscape of PTCLs, the prognoses of most PTCLs remain poor. To overcome the limits of exome sequencing and identify new genomic abnormalities in PTCL, we performed RNAseq and whole genome sequencing. From our analyses, we identified a recurrent in-frame deletion (VAV1 Δ778-786) generated by a focal deletion-driven alternative splicing mechanism as well as novel VAV1 gene fusions (VAV1-THAP4, VAV1-MYO1F, and VAV1-S100A7). These genetic lesions result in increased activation of VAV1 catalytic-dependent (MAPK, JNK) and non-catalytic-dependent (nuclear factor of activated T cells, NFAT) VAV1 effector pathways. These results support an oncogenic role for VAV1 signaling in the pathogenesis of PTCL (Abate et al., 2017). Recently, we have also identified a novel chimeric oncogene, FYN-TRAF3IP2, in the RNA-seq and whole genome sequencing analyses. FYN-TRAF3IP2 leads to aberrant NF-κβ activity downstream of T-cell receptor signaling and induces NF-κβ-driven T-cell transformation when expressed in hematopoietic progenitors in mice. This novel gene fusion supports the oncogenic driver role of NF-κβ signaling in the pathogenesis of PTCL (Kim et al., in review).

  Study phs001962

• Genes and Blood Clotting Study (GABC)

  Objectives: Use genome-wide approaches to identify genetic variants that influence common thrombosis and hemostasis factors, as well as selected common human traits. Design/Methods: The GABC study was a prospective sibling cohort design. Siblings were recruited by targeted email to the undergraduate and graduate student email lists at the University of Michigan. Healthy persons between 14 and 35 years old who had healthy siblings within the same age restriction were able to participate. Study participants agreed to an online informed consent and subsequently completed a 52-question online survey describing their specific bleeding traits as well as many common human traits. Fifty milliliters of blood was collected into a citrate-dextrose solution (ACD) from each participant. An aliquot of whole blood was used for an automated complete blood count analysis and the remainder was processed into platelet poor plasma and buffy coat portions. Plasma and buffy coat aliquots were snap frozen and stored in liquid nitrogen for future studies. 1189 individuals representing 507 sibships were collected between 06/26/2006 and 01/30/2009. Phenotyping Survey Details: To characterize individual bruising and bleeding history, the online survey recorded answers to questions based on a modified von Willebrand Disease (VWD) screening questionnaire. To characterize a collection of participant's common human traits, the survey recorded answers to questions about height, weight, presence of skin tags, history of acne, eye color, hair color, hair line characteristics, skin sunburn sensitivity, skin tanning ability, natural skin color, freckling, cheek dimpling, earlobe shape, shoe size, foot arch characteristics, hand fifth digit morphology, history of dyslexia, history of migraine headaches, history of seasonal allergies, history of apthous ulcers, tendency to sneeze while walking into a bright sunny place, history of dental caries, need for corrective eye lenses, handedness and like or dislike of strongly flavored foods. Biochemical phenotyping: Assays for plasma Von Willebrand Factor (VWF) antigen were performed using ELISA and "Alphalisa" techniques. Automated complete blood count analysis was performed on a Bayer Advia 120 on all participants (including WBC differential, RBC indices, and platelet count.) For the dbGaP v2 update, new biochemical phenotypes have been submitted and include von Willebrand Factor, von Willebrand Factor propeptide, plasminogen, gamma prime fibrinogen, ADAMTS 13, antithrombin III, protein C, and protein S. All new phenotypes were obtained using "Alphalisa" techniques. Genotyping Details: SNP genotyping was performed using genomic DNA extracted from peripheral blood at the Broad Institute, (MIT/Harvard). Genotyping was performed on the Illumina Omni-1 quad chip at the Broad Institute. For the dbGaP v2 update, genotyping data from the Illumina Human Exome was deposited. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to blood clotting through large-scale genome-wide association studies of siblings. Genotyping was performed at the Broad Institute of MIT and Harvard, a GENEVA genotyping center. Data cleaning and harmonization was performed by the primary investigators at the University of Michigan, Ann Arbor, and at the GEI-funded GENEVA Coordinating Center at the University of Washington. This study serves as a resource for investigators who are interested in the genetic determinants of specific plasma proteins in a healthy population. The sibling cohort design allows for linkage analysis in addition to association studies. Analysis of thrombosis and hemostasis related traits should help elucidate specific biochemical and genetic networks that maintain hemostasis. We hope to identify specific genetic determinants of VWF levels in order to better understand the factors that influence the development of VWD.

  Study phs000304

• AML Proteogenomic Landscape Whole-transcriptome RNA-Sequencing

  Whole transcriptome RNA-sequencing of purified bone marrow blasts of 136 de novo, treatment naive AML patients. For further details, we refer to the manuscript "The Proteogenomic Landscape of AML" by Jayavelu, Wolf, Buettner et al. mRNA extraction and whole transcriptome sequencing For transcriptome analysis the TruSeq Total Stranded RNA kit was used, starting with 250ng of total RNA, to generate RNA libraries following the manufacturer’s recommendations (Illumina, San Diego, CA, USA). 100bp paired-end reads were sequenced on the NovaSeq 6000 (Illumina) with a median of 57 mio. reads per sample. RNA Data Analysis Data quality control was performed with FastQC v0.11.9. Reads were aligned to the human reference genome (Ensembl GRCh38 release 82) using STAR v2.6.1. Gene count tables were generated while mapping, using Gencode v31 annotations. All downstream analyses were carried out using R v4.0 and BioConductor v3.12 (Huber et al., 2015; R Core Team, 2020). Size-factor based normalization was performed using DESeq2 v1.28.1(Love et al., 2014).

  Dataset EGAD00001008484

• Genomic Advances in Sepsis (GAinS): RNA-seq

  Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. This cohort comprises a subset of patients enrolled in the Genomic Advances in Sepsis (GAinS) study, an established biobank of adult sepsis patients. Patients with sepsis due to community acquired pneumonia or faecal peritonitis were recruited from 34 hospitals across the UK from 2005-2018, with samples for functional genomics and detailed clinical information collected on the first, third and/or fifth day following ICU admission. RNA was extracted from leukocytes isolated at the bedside using LeukoLOCK kits. We have previously identified sepsis response signatures (SRSs), transcriptomic endotypes that are associated with differential early mortality (Davenport et al, Lancet Respir Med, 2016; Burnham et al, AJRCCM, 2017) and response to treatment in a clinical trial (Antcliffe et al, AJRCCM, 2018). We generated RNA sequencing data on 903 samples, including 134 samples repeated from our previously released microarray data. Libraries were prepared using NEB Ultra II Library Prep kits (Illumina) and sequenced on a NovaSeq 6000. Reads were aligned to the reference genome (GRCh38) using STAR and gene counts quantified using featureCounts (annotation Ensembl v99). Counts were TMM-normalised and log-transformed. Following QC, processed data were available on 864 samples from 667 unique patients.

  Dataset EGAD00001008730