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• UROMOL 2020 - RNA-seq data validation

  For this project about non-muscle invasive bladder cancer (NMIBC), we analysed total RNA-seq data from 47 patients used for validation. Sequencing of total RNA was performed using KAPA RNA HyperPrep Kit with RiboErase HMR (Roche). RNA input was 100 to 500 ng. The dataset is composed of 94 fastq files.

  Dataset EGAD00001006967

• RNASeq from PC12 cell lines (derived from PPGL) cultivated under 21% (normoxia) and 1% (hypoxia) oxygen conditions.

  RNASeq fastq files from PC12 cells (derived from rat PPGL) were cultivated in normoxia conditions (37˚C, 5% CO2 and 21% O2 balanced with N2) or in hypoxia conditions in a hypoxic incubator (37˚C, 5% CO2 and 1% O2 balanced with N2), for short time (12h, 24h, and 48h) or prolonged time (36 days). Samples at each timepoint were cultured in triplicate (total 24 paired end fastq files). Sequencing was performed with the Illumina NovaSeq6000.

  Dataset EGAD50000001200

• RIP-SeqRaw data

  This dataset contains the raw count matrix generated in the PTBP1 project. Sequencing data were obtained from bulk directional RNA-seq and RIP-seq experiments performed on a NovaSeq 6000 platform (2×100 bp paired-end reads). Read counts were computed using HTSeq-count, without normalization. The dataset comprises two groups of samples: controls and patients. For each group, both input and immunoprecipitated samples are available. Patient samples correspond to skin biopsies from individuals affected by a neurodevelopmental disorder associated with variable skeletal dysplasia and disproportionate short-limbed short stature.

  Dataset EGAD50000001722

• Visium CytAssist Spatial Gene Expression analysis for glioblastoma

  The glioblastoma spatial transcriptomics dataset was generated from 4 FFPE fixed adult glioblastoma samples, using the 10x Genomics Visium platform, producing raw FASTQ files, spatial feature matrices (.h5, .mtx, .tsv), and paired histology images (.tif). Spot-level transcript counts were aligned to tissue architecture and processed in Seurat, with results stored as .rds objects. Data visualization was performed using SpatialFeaturePlot, and quantitative analyses were supported by custom Matlab scripts. The dataset provides both raw and processed files, offering reproducible spatially resolved transcriptomic profiles of glioblastoma tissue sections

  Dataset EGAD50000001767

• Transcriptome data of WS patients and controls

  Fibroblasts of 4 WS patients and 4 sex matched controls were transformed into iPSCs, which were then differentiated to Neuroepithelial Stem Cells (NESCs), and further differentiated into neurons. RNA sequencing was performed at the NESC stage and after 4 and 8 weeks of neural development for 3 replicates per individual using the Illumina TruSeq stranded library polyA method on the NovaSeq6000 platform at NGI Stockholm. The data was preprocessed using the nf-core/rnaseq (https://nf-co.re/rnaseq) that includes quality control, data cleaning, and read alignment to GRCh38 (STAR, v2.6.1d)

  Dataset EGAD50000001728

• Angiopredict: predicting response for bevacizumab treatment

  In ANGIOPREDICT, academic cancer biologists and industry-based biotechnology researchers worked with clinicians to identify biomarkers to predict whether individual metastatic colorectal cancer patients will respond positively to bevacizumab containing therapy. In this retrospective study, copy number profiles were determined using whole genome shallow sequencing, and progression free survival and treatment data was collected.

  Study EGAS00001002724

• small RNA-sequencing and RNA-sequencing of human brain tissue with temporal lobe epilepsy

  This data set contains the raw .fastq files from two RNA-sequencing experiments and two small RNA-sequencing experiments. Both control brain tissue and tissue from sufferers of mesial temporal lobe epilepsy were sequenced. Two different brain regions were sequenced; the cortex and the hippocampus. For more details please see: Mills, James D., et al. "Coding and non-coding transcriptome of mesial temporal lobe epilepsy: Critical role of small non-coding RNAs." Neurobiology of disease 134 (2020): 104612.

  Dataset EGAD00001005735

• Comprehensive copy number aberration analysis using digital Multiplex Ligation-dependent Probe Amplification (digitalMLPA) in pediatric B-cell precursor acute lymphoblastic leukemia

  In this study a next-generation sequencing based method was applied to comprehensively screen for recurrent, disease-relevant copy number aberrations in a cohort of Hungarian patients. Diagnostic bone marrow samples from 260 children with B-cell acute lymphoblastic leukemia as well as 72 control samples and were investigated by digital multiplex ligation-dependent probe amplification using the disease-specific D007 probemix. Whole chromosome gains and losses, as well as subchromosomal copy number aberrations were simultaneously profiled.

  Dataset EGAD00001010878

• ICGC Prostate Cancer Whole Genome Sequencing

  Whole Genome Sequencing Illumina HiSeq data from 20 men with prostate cancer. 20 samples were taken from primary tissue obtained at prostatectomy (target sequencing depth 50X) with matched blood control (target sequencing depth 30X). These were submitted for use in the ICGC Pan-Cancer Analysis of Whole Genomes project. Same raw data submitted in EGAD00001001116. As of September 2020, some of the studies using these data include: Wedge et al, Nature Genetics 2018 (PMID: 29662167) Pan-Cancer Analysis of Whole Genomes, Nature 2020 (PMID: 32025007)

  Dataset EGAD00001000892

• Deep exome sequencing

  Bam files from deep exome sequencing of blood DNA samples from five TET2 mutation carriers (Ly1, Ly2, Ly9, Ly11, Ly14) and three wild-type family members (Ly8, Ly10, Ly13) extracted at multiple time points. Library preparations were performed with SeqCap EZ Exome v3 (Roche, Switzerland) using six different index primers per sample for which paired-end Illumina sequencing was done with 75bp read length and HiSeq4000 sequencer. After alignment (bwa version 0.7.12), base recalibration (GATK 3.5), realignment around indels (GATK 3.5) and duplicate removal (MarkDuplicates; Picard Tools version 1.79), data from libraries with six different indexes were merged.

  Dataset EGAD00001004780