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• Spatially resolved antigen receptor and gene expression data from human tonsil tissue

  The dataset includes spatially-resolved gene expression and antigen receptor data from two Tonsil samples (1 and 2). Tissue sections from the tonsil samples were used for spatial transcriptomics (Visium, 10x genomics). Tonsil 2 tissue sections were analyzed by a new method (Spatial VDJ) to spatially resolve antigen receptor sequences (target capture), which was developed in our publication. Nearby or adjacent tissue sections (from Tonsil2) were also analyzed by a bulk antigen receptor sequencing approach (amplicon sequencing), by a method also newly developed by us in the same publication (Bulk SS3 VDJ). For Visium, the data were anonymized (all SNPs removed) using Bamboozle (Ziegenhain and Sandberg, Nature Communications 2021). The deposited data is in the form of fastq files. All remaining data, metadata, micrographs of the tissue sections (of those used for spatial transcriptomics), and scripts used for the analysis are available at Zenodo (DOI: 10.5281/zenodo.7961605). Final libraries were sequenced on NextSeq2000 (Illumina) or NovaSeq6000 (Illumina) and analyzed with Seurat, Space Ranger, and STutility pipelines.

  Dataset EGAD00001011062

• Integrative genome profiling in AML

  AML emerges as a consequence of accumulating independent genetic aberrations that direct regulation and/or dysfunction of genes resulting in aberrant activation of signalling pathways, resistance to apoptosis and uncontrolled proliferation. Given the significant heterogeneity of AML genomes, AML patients demonstrate a highly variable response rate and poor median survival in response to current chemotherapy regimens. For the past 4 years we have conducted gene expression profiling on purified bone marrow populations equating to normal haematopoietic stem and progenitor cells from healthy subjects and patients with de novo AML in order to identify AML signatures of aberrantly expressed genes in cancer versus normal. We are now applying a series of bioinformatic methodologies combined with clinical and conventional diagnostic data to establish novel genomics strategies for improved prognostication of AML. Additionally, we use our AML signatures to unravel oncogenic signalling pathway activities in AML patients and test inhibitory drugs for these pathways inn preclinical therapeutic programmes. We consider that superimposing GEP and clinical data for our AML patient cohort with additional data on their mutational status will significantly improve the prognostic power of the study as well as unravel yet unknown mutations associated with aberrant signalling activities of oncogenic pathways.

  Dataset EGAD00001001873

• DAC for study Exon resequencing in patients with Brugada syndrome

  Dac EGAC00001000006

• DAC for the "Study on the proliferation history of colorectal adenomas"

  Dac EGAC00001000209

• DAC for study Population Structure and Genetic Diversity in Argentinean populations

  Dac EGAC00001000426

• DAC for genome-wide association study of prognosis in Crohn's disease

  Dac EGAC00001000568

• DAC for study Treg in breast cancer and healthy individuals

  Dac EGAC00001000638

• DAC for NKTL study. National Cancer Centre Singapore.

  Dac EGAC00001000640

• JKU and MUI Data Access Committee for targeted NGS panel data

  Dac EGAC00001000669

• DAC for the family-based genome-wide association study on CRSwNP

  Dac EGAC00001000742

• Data Access Committee (DAC) for NGS-based T-PLL data

  Dac EGAC00001000784

• Stanford Data Access Committee for Breast Cancer Tumor Heterogeneity through Treatment

  Dac EGAC00001000993

• Data Access Committee for Rare Coding Variants in Lupus Risk Genes

  Dac EGAC00001001157

• DAC for TCR-seq analysis of human intestinal Trm cells

  Dac EGAC00001001219

• DAC for Sex-biased patterns shaped the genetic history of Roma

  Dac EGAC00001001477

• Data Access Commitee for Translational Neurodegeneration Group - Technical University of Munich

  Dac EGAC00001001831

• Data Access Commitee for Translational Neurodegeneration Group Technical University of Munich

  Dac EGAC00001001916

• Data Access Committee for LUAD m6A profiling

  Dac EGAC00001002257

• University of Melbourne Centre for Cancer Research (UMCCR) Data Access Committee

  Dac EGAC00001002442

• EGAD00010000622

  SNP array data for gastric cancer cell lines

  Dataset EGAD00010000622

• DAC for "Integrated genetic analysis of primary CNS lymphoma"

  Dac EGAC00001003233

• Tapestri_h5

  H5 files generated for each sample with Tapestri Pipeline

  Dataset EGAD00010002559

• Tapestri_loom

  Loom files generated for each sample with Tapestri Pipeline

  Dataset EGAD00010002561

• DAC UMR Inserm U1236

  DAC for UMR Inserm U1236 laboratory

  Dac EGAC50000000217

• Institute of Pathology for Oncology Department at CHUV

  Prof Solange Peters

  Dac EGAC50000000008