8 results for "*"

in 4.37 milliseconds.

• Synthetic - FEGA Sweden Heilsa synthetic dataset December 2023

  Synthetic - This submission contains a subset of a synthetic dataset derived from the project Heilsa Tryggvedottir - a Nordic collaboration on sharing sensitive human data. Heilsa Tryggvedottir is funded by the Nordic e-Infrastructure Collaboration (NeIC), the ELIXIR nodes of Finland, Norway, and Sweden, Computerome in Denmark, and the Estonian Scientific Computing Infrastructure (ETAIS). In the synthetic data creation process, it was attempted to strike a fine balance between the usability of the datasets (e.g. technical FEGA development, testing, user training, and basic bioinformatics) and compliance with GDPR. File names and file content (e.g. headers in fastq) are anonymized. Moreover, the X, Y, and mitochondrial sequences have been discarded from the original data since these data can be used for maternal, paternal, or ethnic origin tracing. The dataset does not follow natural haplotype distribution (inherent to imputation panels). The only inputs derived from real sequence data are variant distribution density per chromosome and learning sequencing error models. The synthetic dataset consists of two fastq files, a cram file, a vcf file, and two index files.

  Study EGAS50000000086

• A Combined Omics and Tissue Biobank for Paediatric Cancers

  The Swedish Childhood Tumour Biobank (BTB) is a nonprofit national infrastructure for samples and genomic data collection from pediatric patients diagnosed with central nervous system and other solid tumours in Sweden. Fresh frozen tumour tissue and patient’s matched blood are collected from affected patients and genomic data is continuously generated and made available for approved medical research projects. We expect that the accessibility of biological samples and/or data to the research community will contribute to further advances in the understanding of tumour biology, which in turn will impact many aspects of clinical care of children with cancer, by refining diagnosis and/or identifying oncogenic drivers and therapeutic targets.

  Study EGAS50000000209

• Single-cell RNA sequencing of SI-NET

  Small intestinal neuroendocrine tumors (SI-NETs) arise from hormone-producing cells in the gut, yet the molecular changes driving malignant transformation remain poorly understood. Bulk tissue-based analyses make it challenging to distinguish signals derived from normal cells from those of normal hormone-producing cells. In this study, we analyzed SI-NETs at single-cell resolution and directly compared tumor cells with their corresponding normal hormone-producing counterparts using single-cell RNA sequencing. Tissue samples were obtained from two patients undergoing tumor resection surgery. Cells from corresponding normal intestinal mucosa and SI-NET tumors were isolated using specific markers and analyzed for direct comparison.

  Study EGAS50000001584

• SweGen: a whole-genome data resource of genetic variability in a cross-section of the Swedish population

  The SweGen project represents a comprehensive map of genetic variation in the Swedish population. This Study contains four datasets where the first one contains whole-genome sequencing data in CRAM-format for 942 individuals from the Swedish Twin Registry, the second one contains genetic variant frequencies in VCF-format for the same individuals, the third one contains whole-genome sequencing data in CRAM-format for 58 individuals from the Northern Sweden Population Health Study and the fourth one contains genetic variant frequencies in VCF-format for the same individuals. The data, serving as a reference dataset, is a valuable and national resource for genetics research and clinical diagnostics and can be used, for instance, to increase understanding of both rare and complex disorders. It also serves as an international resource for larger studies of human population genetics.

  Study EGAS50000000906

• Total RNA expression in benign ovarian and malignant ovarian tumours

  This study investigates the total RNA expression profiles of fresh-frozen ovarian tumors, providing a comprehensive analysis of 112 samples from 111 women in Sweden, two samples being replicates. The dataset includes 18 benign and 94 malignant tumours, collected as part of the U-CAN initiative from Uppsala Biobank. The sequencing data offers valuable insights into ovarian tumour biology and can be used to enhance understanding of tumour classification and molecular characteristics.

  Study EGAS50000001045

• The cellular state space of AML unveils novel NPM1 subtypes with distinct clinical outcomes and immune evasion properties

  Acute myeloid leukemia (AML) is a genetically and cellularly heterogeneous disease. We characterized 120 AMLs using bulk genomic and transcriptomic analyses (WES, MP-WGS, RNA-seq), and single-cell RNA sequencing (scRNA-seq). Our results reveal an extensive cellular heterogeneity that distorts the bulk transcriptomic profiles. Notably, NPM1 mutated AML could be stratified into two novel, clinically relevant classes (NPM1class I and NPM1class II).

  Study EGAS50000001084

• Aberrant expression of SLAMF6 constitutes a targetable immune escape mechanism in acute myeloid leukemia

  In this study, we demonstrate that aberrant expression of SLAMF6 on acute myeloid leukemia (AML) cells serves as a novel immune escape mechanism that can be inhibited by antibodies against the SLAMF6 dimerization site. This constitutes a novel mechanism of checkpoint inhibition, which paves the way for immunotherapy in AML and other SLAMF6-expressing cancers. This study utilizes single cell sequencing data from the AML cell line HNT-34 co-cultured with normal T cells, with and without antibody targeting SLAMF6, as well as single cell data of 38 primary AML samples.

  Study EGAS50000001085

• Transcriptomic subgroups in soft tissue tumors correlate with morphologic subtype, genomic features, and outcome

  We assessed gene expression (GGE) profiles and gene fusions as detected by transcriptome sequencing (RNA-seq) of 704 soft tissue tumors (STT) representing 56 different tumor types. Comparison with follow-up data identified transcriptional subclusters among sarcomas with complex genomes and among adipocytic tumors that correlated with metastasis-free survival and local recurrences, respectively. Thus, RNA-seq analysis of STT provides clinically important information.

  Study EGAS50000001472