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• RNASeq files for Mullighan_GL_reALL RNASEQ1

  RNASeq files for Mullighan_GL_reALL RNASEQ1 paper titled "Mutational landscape and patterns of clonal evolution in relapsed pediatric acute lymphoblastic leukemia"

  Dataset EGAD00001005511

• T-WGBS for Naive B Cell

  Tagged-WGBS for 3 Naive B Cell samples of the CancerEpiSys-PRECiSe project.

  Dataset EGAD00001005966

• A95654B

  Whole genome sequencing for single cells for library A95654B 1740 cells; filetype=bam

  Dataset EGAD00001006940

• RNAseq files for Mullighan_GL_reALL RNASEQ2

  RNAseq files for Mullighan_GL_reALL RNASEQ2 paper titled "Mutational landscape and patterns of clonal evolution in relapsed pediatric acute lymphoblastic leukemia"

  Dataset EGAD00001005510

• Somatic SNVs and Indels for INSPIRE Tumor WES

  Somatic SNVs and Indels for INSPIRE Tumor WES called using Mutect, Mutect2, Varscan2, Vardict, and Strelka2

  Dataset EGAD00001006569

• ONT Minion reads for a patient with ataxia-pancytopenia syndrome.

  ONT Minion reads to provide 30x coverage for a patient with ataxia-pancytopenia syndrome.

  Dataset EGAD00001005022

• WGS files for Roussel-ATRT-TM

  WGS files for Roussel-ATRT-TM paper titled "Atypical teratoid/ rhabdoid tumoroids reveal subgroup-specific drug vulnerabilities"

  Dataset EGAD00001009163

• RNASeq files for CIC paper data

  RNASeq files for CIC paper titled "Malignant progression of an ancestral bone marrow clone harboring a CIC-NUTM2A fusion in isolated myeloid sarcoma"

  Dataset EGAD00001009788

• Dataset for "Intratumoral Heterogeneity and Clonal Evolution Induced by HPV Integration" (Illumina)

  Dataset for "Intratumoral Heterogeneity and Clonal Evolution Induced by HPV Integration" (Illumina)

  Dataset EGAD00001009630

• WGS files for paper titled "Patient-derived pediatric brain tumor organoids faithfully recapitulate primary tumors"

  WGS files for paper titled "Patient-derived pediatric brain tumor organoids faithfully recapitulate primary tumors"

  Dataset EGAD00001015800

• WXS files for paper titled "Patient-derived pediatric brain tumor organoids faithfully recapitulate primary tumors"

  WXS files for paper titled "Patient-derived pediatric brain tumor organoids faithfully recapitulate primary tumors"

  Dataset EGAD00001015801

• RNASeq files for paper titled "Patient-derived pediatric brain tumor organoids faithfully recapitulate primary tumors"

  RNASeq files for paper titled "Patient-derived pediatric brain tumor organoids faithfully recapitulate primary tumors"

  Dataset EGAD00001015802

• Remaining WGS files for Klco RPAML data

  Remaining WGS files for study titled "Genomics of pediatric myeloid neoplasms"

  Dataset EGAD00001008446

• UKBEC 1st release of Exome data for 65 individuals

  UKBEC 1st release of Exome data for 65 neuropathologically confirmed control individuals of European descent.

  Dataset EGAD00001003100

• RNASeq files for Newman MAP3K8 melanoma

  RNASeq files for MAP3K8 melanoma paper titled "Clinical genome sequencing uncovers potentially targetable truncations and fusions of MAP3K8 in spitzoid and other melanomas"

  Dataset EGAD00001004567

• WES files for CHEN WTPDX WES

  WES files for CHEN WTPDX paper titled "Forty-Five patient-derived xenografts capture the clinical and biological heterogeneity of Wilms tumor"

  Dataset EGAD00001004506

• McGill EMC Release 4 in tissue "venous blood" for cell type "Monocyte"

  McGill EMC Release 4 in tissue "venous blood" for cell type "Monocyte"

  Dataset EGAD00001001282

• McGill EMC Release 4 in tissue "venous blood" for cell type "eosinophil"

  McGill EMC Release 4 in tissue "venous blood" for cell type "eosinophil"

  Dataset EGAD00001001281

• Maternal Plasma RNA Sequencing for Genomewide Transcriptomic Profiling and Identification of Pregnancy-Associated Transcripts

  Maternal Plasma RNA Sequencing for Genomewide Transcriptomic Profiling and Identification of Pregnancy-Associated Transcripts

  Dataset EGAD00001001609

• Single cell whole genome sequencing of 5 ovarian cancer specimens

  Single cell whole genome sequencing BAM files for five ovarian cancer specimens.

  Study EGAS50000001458

• Dataset for soft_tissue_tumor-RNA

  Rare cancer sequencing data of 243 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008859

• Unifying recovery dynamics in heterogeneous diseases exemplified by COVID-19

  Studying the recovery dynamics in different diseases poses many difficulties, as patients often display high heterogeneity in their recovery courses. Moreover, most attempts to study disease dynamics focus on disease progression rather than disease recovery mechanisms. To model recovery dynamics, using severe COVID-19 as the example, we align heterogeneous recovery trajectories via a novel computational scheme applied to longitudinally sampled blood transcriptomes. We thus generate pseudotime trajectories, which we then link to cellular and molecular mechanisms based on cell deconvolution analysis and molecular pathway prediction, thus presenting a unique framework for studying recovery processes over time. Specifically, mature neutrophils displayed a gradual decrease during recovery, allowing superior useability for outcome prediction compared to currently used clinical markers. Further, we discovered a recovery-related regulatory change in gene programs resulting in immune rebalancing between interferon and NFkB activity and the restoration of cell homeostasis. We thus propose regulatory mechanisms governing COVID-19 recovery and suggest mature neutrophils and additional gene markers, as novel clinical biomarkers for disease outcome. Overall, we present a novel clinically relevant computational framework for modeling disease recovery, paving the way for future studies of the recovery dynamics in other diseases and tissues.

  Study EGAS00001005735

• Host pathogen interaction long read transcriptome

  Our incomplete knowledge of the human transcriptome impairs the detection of disease-causing variants, in particular in transcripts only expressed under certain conditions. These transcripts are often lacking from reference transcript sets, such as Ensembl/GENCODE and RefSeq, and could be relevant for establishing genetic diagnoses. We present SUsPECT (Solving Unsolved Patient Exomes/gEnomes using Custom Transcriptomes), a pipeline based on the Ensembl Variant Effect Predictor (VEP) to predict variant impact on custom transcript sets, such as those generated by long-read RNA-sequencing, for downstream prioritization. Our pipeline predicts the functional consequence and likely deleteriousness scores for missense variants in the context of novel open reading frames predicted from any transcriptome. We demonstrate the utility of SUsPECT by uncovering potential mutational mechanisms of pathogenic variants in ClinVar that are predicted to be benign using the reference transcript annotation. In further support of SUsPECT’s utility, we identified an enrichment of immune-related variants predicted to have a more severe molecular consequence when annotating with a newly generated transcriptome from stimulated immune cells instead of the reference transcriptome. Our pipeline outputs crucial information for further prioritization of potentially disease-causing variants for any disease and will become increasingly useful as more long-read RNA sequencing datasets become available.

  Study EGAS00001006779

• Integrative Multi-Omics and Drug Sensitivity Profiling Reveals Potential Predictive Biomarkers in Pediatric Solid Tumors from the INFORM Registry

  Cure rates for childhood malignancies using established therapy protocols have increased to an average of 80% but have reached a plateau. Moreover, survival rates are particularly low for some pediatric tumors (such as high-risk Group3 medulloblastomas, osteosarcomas, Ewing sarcomas, high-risk neuroblastomas and high-grade gliomas) and dismal for patients with relapsed malignancies. A functional drug response profiling platform for pediatric solid and brain tumors has been established within the INFORM program to identify patient-specific vulnerabilities, biomarkers and unravel molecular mechanisms associated with drug response profiles for clinical translation. Here, we perform a multi-omics analysis using drug sensitivity profiles, genomic, and transcriptomic data of 81 pediatric solid tumor samples. The integrative analysis revealed two multi-omics signatures with high drug sensitivity predictivity. One signature highlighted neuroblastoma samples with sensitivity to navitoclax (a BCL-2 family inhibitor). A second signature was significantly specific to a subset of Wilms tumors harboring the SIX1 (Q177R) hotspot mutation, which had high expression of MGAM, PTPN14, STAT4 and KDM2B and high sensitivity to MEK inhibitors. A patient-specific causal interaction network analysis elucidated the potential biological mechanism of action between MEK inhibitors and the SIX1 mutation in n = 6 Wilms tumor samples.

  Study EGAS00001008249

• snRNA-seq analysis in multinucleated myogenic FSHD cells identifies heterogeneous FSHD transcriptome signatures associated with embryonic-like program activation and oxidative stress-induced apoptosis

  The sporadic nature of DUX4 expression in FSHD muscle challenges comparative transcriptome analyses between FSHD and control samples. A variety of DUX4 and FSHD-associated transcriptional changes have been identified, but bulk RNA-seq strategies prohibit comprehensive analysis of their spatiotemporal relation, interdependence and role in the disease process. In this study, we used single-nucleus RNA-sequencing of nuclei isolated from patient- and control-derived multinucleated primary myotubes to investigate the cellular heterogeneity in FSHD. Taking advantage of the increased resolution in snRNA-sequencing of fully differentiated myotubes, two distinct populations of DUX4-affected nuclei could be defined by their transcriptional profiles. Our data provides insights into the differences between these two populations and suggests heterogeneity in two well-known FSHD-associated transcriptional aberrations: increased oxidative stress and inhibition of myogenic differentiation. Additionally, we provide evidence that DUX4-affected nuclei share transcriptome features with early embryonic cells beyond the well-described cleavage stage, progressing into the 8-cell and blastocyst stages. Altogether, our data suggests that the FSHD transcriptional profile is defined by a mixture of individual and sometimes mutually exclusive DUX4-induced responses and cellular state-dependent downstream effects.

  Study EGAS00001007635