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• NanoSeq of buccal swab samples

  16 buccal swab samples were analyzed by Nanoseq. S3 in the file names indicates 28 mm^2 swab size, and S5 indicates 254 mm^2 swab size. We analyzed 28 mm^2 swab samples using paired 254 mm^2 swab samples as controls. The files were generated by the Nanoseq pipeline using GRCh37 as the reference genome.

  Dataset EGAD50000000999

• WGS Data from 42 Multi-Region Sampled IPMN-PDACs and 12 Matched Normal Samples

  This dataset contains whole genome sequencing (WGS) data from 42 IPMN-PDAC and 12 normal samples, with tumours collected from multiple regions within each tumour to capture intra-tumour heterogeneity. Tumour and matched normal samples were sequenced to study somatic mutations, structural variants, copy number alterations, mutational signatures and clonal evolution during IPMN-PDAC progression. The sequencing was performed using Illumina NovaSeq instrument with 150 bp paired-end reads.

  Dataset EGAD50000001687

• Single-cell RNA sequencing of Small Intestinal Neuroendocrine Tumors (SI-NET)

  Single-cell RNA sequencing (scRNA-seq) was performed on cells isolated from intestinal mucosa and small intestinal neuroendocrine tumors (SI-NETs) to compare their transcriptomic profiles. Tumor and adjacent normal tissues were collected from two SI-NET patients, enzymatically dissociated, and processed into single-cell suspensions. Single-cell libraries were generated using the Chromium Single Cell 3′ Reagent Kit v3 and sequenced on an Illumina platform to produce FASTQ files for downstream analysis.

  Dataset EGAD50000002265

• Single‑nucleus multiome data of human fetal livers

  Single‑nucleus multiome data of 53 human fetal liver hematopoiesis spanning 5-18 post-conception-weeks. Nuclei were isolated after tissue dissociation and sorting live CD45+CD235- cells, and processed according to the Chromium Next GEM Single Cell Multiome ATAC + Gene Expression User Manual specifications. Sequencing data was processed with CellRanger Arc v2.0.2 and aligned to GRCh38-2020-A-2.0.0.

  Dataset EGAD50000002337

• A Protein Neddylation Inhibitor MLN4924 Suppresses Patient-Derived Glioblastoma Cells via Inhibition of ERK and AKT Signaling

  To identify what factors cause a different reactivity to MLN4924, 15 cells were categorized into high, intermediate, and low MLN4924 resistance groups based on the half-maximal inhibitory concentration (IC50) of MLN4924. PDC1, PCD2, PDC3, PDC4, and PDC5 showed high MLN4924 sensitivity, whereas PDC12, PDC13, PDC14, and PDC15 showed low MLN4924 sensitivity. Whole-transcriptome sequencing of these 9 patient-derived glioblastoma stem cells was performed.

  Dataset EGAD00001005709

• MM samples for epigenomic translocation of H3K4me3 broad domains following super-enhancer hijacking

  Chromosomal translocations are important drivers of haematological malignancies whereby proto-oncogenes are activated by juxtaposition with super-enhancers, often called enhancer hijacking. To examine this phenomenon we used ChipSeq based on a combination of six histone modifications as follows: H3K4me1, H3K4me3, H3K9me3, H3K27me3, H3K27Ac and H3K36me3. Samples are patient-derived xenografts generated by passaging primary patient CD138+ selected cells through the SCID-rab myeloma mouse model.

  Study EGAS00001005684

• Liver CD14+CD8+ T cells scRNAseq dataset

  Sorted single CD8+T cells expressing CD14 from human liver for SMARTSeq2. Livers processed: Kucykowicz et al STAR Prot 2022:pubmed.ncbi.nlm.nih.gov/35516846/ Published: Pallett et al Nature 2022 Tissue CD14+CD8+T-cells are reprogrammed by myeloid cells and modulated by LPS A modified SMART-seq2 protocol was performed on the single flow cytometry sorted-cells as previously described58. After cDNA generation, libraries were prepared (384 cells per library) using the Illumina Nextera XT kit (Illumina). Each library was sequenced to achieve a minimum depth of 1-2 million raw reads per cell using an Illumina HiSeq 4000 using v. 4 SBS chemistry to generate 75-bp paired-end reads.

  Dataset EGAD00001009831

• Paired-end RNA-Seq Dataset of 72 Brain Organoid Samples: Sequencing and Gene Expression Analysis

  72 fastq sequencing files of 72 brain-organoid paired-end sequenced samples (R1/R2). RNA sequencing libraries were prepared using the NEBNext Ultra II RNA Library Prep Kit for Illumina following the manufacturer’s instructions (New England Biolabs, USA). mRNAs were first enriched with Oligo(dT) beads. The samples were sequenced using a 2x150 Pair-End configuration (ver. 1.5) on an Illumina NovaSeq 6000 The raw sequence reads were aligned to the human reference genome GRCh38 using the STAR aligner (Dobin et al., 2013). Gene expression levels were quantified using the FeatureCounts tool (Liao et al., 2014) (file: "All_Counts_final.xlsx") For more information, see "Study Description"

  Dataset EGAD50000000935

• Whole Genome Sequencing Consortium on Frontotemporal Dementia With Underlying TDP-43 Pathology

  Frontotemporal lobar degeneration (FTLD) is one of the leading causes of dementia in individuals younger than 65 years. The FTLD symptoms of most patients are not yet explained by mutations in the known FTLD genes (C9orf72, GRN, MAPT). Identifying genetic risk associated with the disease is key for developing disease models and new therapeutic avenues. We performed short-read whole-genome sequencing (WGS) on 19 neuropathologically normal individuals and 560 patients with FTLD, including 171 pathologically confirmed FTLD patients, 352 clinically diagnosed FTLD patients, and 37 autosomal dominant FTLD patients. Pathologically confirmed FTLD patients include FTLD with TDP43 inclusions (FTLD-TDP) and FTLD with FUS inclusions (FTLD-FUS), autosomal dominant FTLD patients include GRN (FTLD-TDP GRN) and C9ORF72 (FTLD-TDP C9ORF72) mutation carriers, clinically diagnosed patients include behavioral variant of frontotemporal dementia (bvFTD), frontotemporal dementia with amyotrophic lateral sclerosis (FTD-ALS), frontotemporal dementia with corticobasal syndrome (FTD-CBS), semantic variant of primary progressive aphasia (svPPA), and non-fluent variant of primary progressive aphasia (nfvPPA).

  Study phs003309

• HLA has strongest association with IgA nephropathy in genome-wide analysis

  GWAS study of families with probands with IgA nephropathy from the UK Glomerulonephritis DNA bank

  Study EGAS00000000031