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• HipSci_RNASEQ_Ataxia

  The HipSci project brings together diverse constituents in genomics, proteomics, cell biology and clinical genetics to create a UK national iPS cell resource and use it to carry out cellular genetic studies. In this sub-study we perform RNAseq on iPS lines from hereditary cerebral ataxia patients.

  Study EGAS00001001992

• HipSci_RNASEQ_BPD

  The HipSci project brings together diverse constituents in genomics, proteomics, cell biology and clinical genetics to create a UK national iPS cell resource and use it to carry out cellular genetic studies. In this sub-study we perform RNAseq on iPS lines from Blood platelet disorders patients.

  Study EGAS00001001993

• HipSci_RNASEQ_Hypertrophic_Cardiomyopathy

  The HipSci project brings together diverse constituents in genomics, proteomics, cell biology and clinical genetics to create a UK national iPS cell resource and use it to carry out cellular genetic studies. In this sub-study we perform RNAseq on iPS lines from Hypertrophic Cardiomyopathy patients.

  Study EGAS00001001994

• HipSci_RNASEQ_Congenital_hyperinsulinia

  The HipSci project brings together diverse constituents in genomics, proteomics, cell biology and clinical genetics to create a UK national iPS cell resource and use it to carry out cellular genetic studies. In this sub-study we perform RNAseq on iPS lines from Congenital hyperinsulinia patients.

  Study EGAS00001001988

• HipSci_RNASEQ_Macular_Dystrophy

  The HipSci project brings together diverse constituents in genomics, proteomics, cell biology and clinical genetics to create a UK national iPS cell resource and use it to carry out cellular genetic studies. In this sub-study we perform RNAseq on iPS lines from Macular Dystrophy patients

  Study EGAS00001001995

• HipSci_RNASEQ_Alport

  The HipSci project brings together diverse constituents in genomics, proteomics, cell biology and clinical genetics to create a UK national iPS cell resource and use it to carry out cellular genetic studies. In this sub-study we perform RNAseq on iPS lines from Alport syndrome patients.

  Study EGAS00001001986

• HipSci_RNASEQ_Spastic_paraplegia

  The HipSci project brings together diverse constituents in genomics, proteomics, cell biology and clinical genetics to create a UK national iPS cell resource and use it to carry out cellular genetic studies. In this sub-study we perform RNAseq on iPS lines from Spastic paraplegia patients

  Study EGAS00001001991

• Genetic landscape of pediatric Low Grade Gliomas & Glioneuronal tumors

  We performed whole-genome sequencing of 42 paired tumor/normal LGG genomes and analyzed somatically acquired single nucleotide variations (SNVs), insertion/deletions, structural variations and copy number variations in the cancer genomes

  Study EGAS00001000255

• Landscape of gene mutations in Down syndrome-related myeloid disorders

  To identify the spectrum of gene mutations in Down syndrome-related myeloid disorders, whole genome sequencing of 4 trio samples from TAM/AMKL/complete remission (CR) phases and whole exome sequencing of 15 TAM and 14 DS-AMKL samples were performed.

  Study EGAS00001000546

• HipSci_RNASEQ_Kabuki

  The HipSci project brings together diverse constituents in genomics, proteomics, cell biology and clinical genetics to create a UK national iPS cell resource and use it to carry out cellular genetic studies. In this sub-study we perform RNAseq on iPS lines from kabuki patients

  Study EGAS00001001989

• HipSci_RNASEQ_Retinitis_Pigmentosa

  The HipSci project brings together diverse constituents in genomics, proteomics, cell biology and clinical genetics to create a UK national iPS cell resource and use it to carry out cellular genetic studies. In this sub-study we perform RNAseq on iPS lines from Retinitis Pigmentosa patients

  Study EGAS00001001996

• RE_NanoSeq___TwinsUK_Buccal

  Utilising restriction enzyme NanoSeq, we hope to explore the somatic mutation burden and mutational signatures in buccal cells from TwinsUK donors. Some donors have paired blood samples and data from this will help contextualise that work. All donors have paired Targeted NanoSeq data from buccal swabs.

  Study EGAS00001007740

• Triple negative breast cancer matched patient and pdx dataset

  PDX aCGH (txt), WES (fastq) and RNASeq (fastq) samples from mice treated with cisplatin. Primary samples and matched pdx samples from multiple passages. Models from the Marie Curie Instute (HBCx1, HBCx4B, HBCx8, HBCx10, HBCx12B, HBCx14, HBCx15, HBCx16, HBCx17, HBCx23, HBCx24, HBCx27, HBCx28, HBCx30, HBCx31, HBCx33, HBCx39, HBCx40, HBCx43, HBCx51, HBCx63, HBCx66, HBCx92, HBCx106) and the NKI (T127, T162, T241, T250, T283, T302, T336).

  Dataset EGAD00001009046

• 10x Visium Spatial Transcriptomics of 61 Human Hepatocellular Carcinoma Samples

  This dataset contains spatial transcriptomics sequencing data from 61 hepatocellular carcinoma (HCC) tissue samples profiled using the 10x Genomics Visium platform. The cohort includes 38 resected tumors and 23 tumors from patients treated with atezolizumab plus bevacizumab. Sequencing data are provided as paired-end FASTQ files. These data support the analysis of intra-tumoral heterogeneity and its association with clinical outcomes.

  Dataset EGAD50000002122

• scRNA seq Data set

  This data set contains the raw FASTq files and processed files of scRNA seq experiment which were conducted on 3 EMD samples. The libraries were generated using 10x genomics Dual index Single Cell 3' v3.1. The processed files are TSV files of features, barcodes, and gene expression matrix

  Dataset EGAD50000000334

• Immunogene panel sequencing dataset

  This dataset contains 241 samples sequenced with immunogene panel (2533 genes). The samples are sorted CD4+ or CD8+ T cells, skin, or fibroblast samples from patients with various hematological disorders (n=90) and healthy blood donors (n=21). The detailed description of sample processing, sequencing, and read alignment can be found in the publication (Somatic mutations associate with clonal expansion of CD8+ T cells, PMID: [will be updated])

  Dataset EGAD50000000352

• Understanding population genetics and patterns of genome-wide heterozygosity in a sample of the Croatian isolated populations (ESGIDalmatians)

  Using high-throughput sequencing technologies and analytical tools, we conduct an exome sequencing study that will help understand the population genetics of a Croatian island isolate, in a sample of 200 subjects from the Adriatic island of Vis who were selected to reflect islanders with at least four known ancestors in grandparental line who are original islanders.

  Dataset EGAD00001001387

• Comprehensive analysis of interaction between human gene expression and environmental metagenomes.

  WGS, 10x scRNA, 10X TCR, 10X BCR and CyTOF analyses were performed using blood samples collected from healthy volunteers aged 18 years or older who worked or lived in the Kashiwa-no-ha area.

  Study JGAS000321

• CAF-S3 subset in human breast and ovarian cancers

  Carcinoma-associated fibroblasts (CAF) are key players in the tumor microenvironment. By combining 6 well-known stromal markers, we identify four CAF subsets (CAF-S1 to CAF-S4) in human breast and ovarian cancers.

  Study EGAS00001003344

• Whole-genome sequencing of paired tumor and blood samples from 65 bladder cancer patients

  65 paired tumor and normal whole-genome sequencing samples from urothelial bladder carcinomas (UBC, the most common type of bladder cancer) are used to uncover the whole-genome mutational landscape of UBC. Recurrent mutations in noncoding regions affecting gene regulatory elements and structural variations leading to gene disruptions are prevalent in this type of cancer.

  Dataset EGAD00001004545

• Acute myeloid leukemia bulk RNA-seq (Diagnosis and Relapse)

  To gain insight into the clonal heterogeneity of diagnosis (Dx) and relapse (Re) pairs, we employed RNA-seq to longitudinally profile two t(8;21) (AML1-ETO = RUNX1-RUNX1T1), and four FLT3-ITD AML cases. All the samples are bone marrow aspirates.

  Dataset EGAD00001008374

• PacBio Rare Disease Study

  This study leverages PacBio HiFi long-read sequencing technology to comprehensively analyze the genomic architecture of rare diseases. By generating highly accurate, long-read sequences, we aim to resolve complex structural variants, repeat expansions, and other genomic features that are often missed by short-read sequencing. Our approach facilitates improved variant detection and interpretation, advancing the understanding of rare disease etiology and enabling more precise genetic diagnoses.

  Dataset EGAD00001015611

• Columbia Alzheimer's sample (white matter)

  The Columbia Alzheimer’s dataset (white matter) consists of 24 white matter individuals (12 controls, 12 cases). A total of 12,000 estimated cells from each sample were loaded on the 10x Single Cell Next GEM G Chip. cDNA libraries were prepared using the Chromium Single Cell 3’ Library and Gel Bead v3 kit according to the manufacturer’s instructions. cDNA libraries were sequenced using the Illumina NovaSeq 6000 System and NovaSeq 6000 S2 Reagent Kit v1.5 (100 cycles), aiming at a sequencing depth of minimum 30K reads/nucleus.

  Dataset EGAD00001009168

• RNAseq data from 112 samples of benign or malignant ovarian tumours

  This dataset contains ~1.2 TB RNA sequencing data in fastq format from 112 samples of fresh-frozen ovarian tumours from a total of 111 women, two samples being replicates. The samples were collected from the U-CAN collection at Uppsala Biobank and include both benign (n = 18) and malignant (n = 94) tumours. The RNA sequencing samples were sequenced using paired-end sequencing (2x150 bp) on an Illumina NovaSeq 6000 instrument at the SciLifeLab National Genomics Infrastructure (NGI) in Uppsala.

  Dataset EGAD50000001521

• Phase II Trial with Correlative Genomic Analysis of Durvalumab plus Pazopanib Combination in Patients with Advanced Soft Tissue Sarcomas

  This study includes an open-label, phase 2 study to determine the activity of the anti-VEGF receptor tyrosine-kinase inhibitor, pazopanib, combined with the anti-PD-L1 immune checkpoint inhibitor, durvalumab, in unselected advanced sarcomas. We conducted whole exome and transcriptomic sequencing with pre-treatment tissue biopsy to correlate clinical outcomes with molecular and genomic biomarkers to identify patients who would most likely benefit from the combination treatment.

  Dataset EGAD50000000114