12215 results for "cancer rna-seq"

in 32.31 milliseconds.

• SF4324

  RNA-exome

  Dataset EGAD00001006766

• SF10127

  RNA-exome

  Dataset EGAD00001006767

• SF4209

  RNA-exome

  Dataset EGAD00001006768

• SF6839

  RNA-exome

  Dataset EGAD00001006769

• SF11587

  RNA-exome

  Dataset EGAD00001006770

• SF9640

  RNA-exome

  Dataset EGAD00001006771

• SF6587

  RNA-exome

  Dataset EGAD00001006772

• SF11082

  RNA-exome

  Dataset EGAD00001006773

• SF9135

  RNA-exome

  Dataset EGAD00001006747

• SF10750

  RNA-exome

  Dataset EGAD00001006748

• SF8642

  RNA-exome

  Dataset EGAD00001006749

• SF10064

  RNA exome

  Dataset EGAD00001006744

• SE4831

  RNA-exome

  Dataset EGAD00001006830

• SE8570

  RNA-exome

  Dataset EGAD00001006831

• SE9037

  RNA-exome

  Dataset EGAD00001006832

• SE6136

  RNA-exome

  Dataset EGAD00001006833

• SE10497

  RNA-exome

  Dataset EGAD00001006834

• SE4987

  RNA-exome

  Dataset EGAD00001006835

• SE4567

  RNA-exome

  Dataset EGAD00001006836

• SE9581

  RNA-exome

  Dataset EGAD00001006837

• Gene expression matrix

  Log2 gene expression count data from RNA sequencing.

  Dataset EGAD00001008793

• scRNA-seq of glioblastoma cells alone or after co-culture with cerebral organoid cells

  Dataset EGAD00001005315

• Complete sequencing dataset of study EGAS00001002795

  Dataset EGAD00001004572

• ChIPseq data

  ChIPseq data for H3K4me1 and H3K9me3 in HT29; H3K4me1, H3K27me3, H3K9me3, H3K36me3 in LoVo.

  Dataset EGAD00001003258

• Dataset of PPB

  Whole exome sequencing and miRNA-seq data of PPB.

  Dataset EGAD00001000822

• ChIPseq data

  ChIPseq data for CLL patients

  Dataset EGAD00001008665

• DAC for "Longitudinal copy number variation analysis from plasma DNA of head and neck cancer patients under re-radiotherapy"

  This is the DAC for "Longitudinal copy number variation analysis from plasma DNA of head and neck cancer patients under re-radiotherapy" with Florian Janke f.janke@dkfz-heidelberg.de German Cancer Research Center, Division of Cancer Genome Research Holger Sültmann h.sueltmann@dkfz-heidelberg.de German Cancer Research Center, Division of Cancer Genome Research Magdalena Görtz magdalena.goertz@dkfz-heidelberg.de German Cancer Research Center, Multiparametric Methods for Early Detection of Prostate Cancer

  Dac EGAC50000000114

• scRNA-seq of HSPC treated with gemcitabine and carbplatin

  The study used cryopreserved hematopoietic stem and progenitor cells (HSPCs) harvested from one patient with chronic myelogenous leukemia (CML). The cells were thawed and acclimatized for 48 hours in StemMACS HSC Expansion Media XF (Miltenyi Biotec) supplemented with StemMACS HSC Expansion Cocktail (Miltenyi Biotec) at 37°C in a humidified atmosphere containing 5% CO2. 4 samples of 2000000 HSPCs in 2 ml media each were then prepared and treated for 24 hours: carboplatin-high: 150 µg/ml carboplatin carboplatin-low: 18.75 µg/ml carboplatin gemcitabine: 25 ng/ml gemcitabine control: no drug Single-cells were subsequently extracted using the ddSEQ™ Single-Cell Isolator (Bio-Rad) and later sequenced using the SureCell™ Whole Transcriptome Analysis 3' Library Prep Kit (Illumina) on the NextSeq 500 System (Illumina) using the NextSeq 500/550 High Output Kit v2.5 150 Cycles (Illumina) all following the manufacturer's instructions. FASTQ-files were then processed following the ddSeeker (Romagnoli, D., et al., BMC Genomics 2018, doi:10.1186/s12864-018-5249-x) and Drop-seq (Macosko, E.Z., et al., Cell 2015, doi:10.1016/j.cell.2015.05.002) protocols for processing scRNA-seq data to yield the final digital gene expression for each cell.

  Study EGAS00001004381

• Single-cell RNA sequencing of a PBMC collected from a male with 45,X/48,XYYY karyotype

  Dac EGAC00001002373

• Single cell RNA sequencing of relapsed/refractory multiple myeloma

  Study EGAS00001004805

• Single cell transcriptional characterization of human megakaryocyte lineage commitment and maturation

  Single cell RNA-sequencing of sternal bone marrow reciding Hematopoietic Stem Cells (HSCs) and Megakaryocytes (MKs) from individuals undergoing elective open heart valve replacement. HSCs were defined as Lineage-, CD34+, CD38-, CD45RA-, CD90+, CD49f+ cells. MKs where CD41a+, CD42b+ and ploidy was determined with Hoechst. A sternal bone marrow scraping was taken directly following median sternotomy using a Volkmann’s spoon. The sample was collected into an EDTA Vacutainer tube containing 1.8mg/ml EDTA. 4mL of Dulbecco’s phosphate buffered saline (PBS, Sigma) containing 10% human serum albumin (HSA, Gemini Bio Products) was added and the whole volume was resuspended by pipetting 2-3 times. The sample was then put on metallic thermal beads (ThermoFisher Scientific) at a temperature between 0-4°C and transported to the University of Cambridge for further processing. For HSC isolation the cells were stained with the following antibody cocktail: PECy5 conjugated anti-lineage specific antibodies: CD2 (BD), CD3 (BD), CD10 (BD), CD11b (BD), CD11c (BD), CD19 (BD), CD20 (BD), CD56 (BD), biotinylated CD42b (Pab5, NHS Blood and Transplant, International Blood Group Reference Laboratory [IBGRL]), biotinylated GP6 (Pab5, NHS Blood and Transplant, International Blood Group Reference Laboratory [IBGRL]) used in combination with PECy5 conjugated streptavidin (Biolegend). Alexa Fluor 700 conjugated anti-CD34 (BD), PerCP-Cy5.5 conjugated anti-CD38 (BD), Pacific Blue conjugated anti-CD45RA (Invitrogen), PECy7 conjugated anti-CD90 (BD),PE conjugated anti-CD49f (BD). After staining cells were kept at 4°C before sorting using a FACS Aria Fusion flow sorter (BD). Single HSCs defined as Lineage-, CD34+, CD38-, CD45RA-, CD90+, CD49f+ cells were sorted by FACS directly into individual wells of a 96-well plate. Index sort data was collected for each single cell. For MK isolation the cells were stained for surface MK markers with mouse anti-human CD41a APC conjugated antibody (BD) and mouse anti-human CD42b PE conjugated antibody (BD) and for ploidy analysis with 1ug/ml Hoechst 33342 (Invitrogen). After incubation at 37°C for 30 minutes, the cells were kept at 4°C before sorting using a FACS Aria Fusion flow sorter (BD). Single cells and MK pools of 20 cells were sorted by FACS according to ploidy level using a 100uM nozzle directly into individual wells of a 96-well plate. cDNA synthesis and poly(A) enrichment was performed following the G&T-seq protocol (Macaulay et al. 2015), a variation of the Smart-seq2 protocol1. ERCC spike-in RNA (Ambion) was added to the lysis buffer in a dilution of 1:4,000,000.

  Dataset EGAD00001006677

• CUT&RUN in G3-MB

  Aligned BAM files from CCUT&RUN sequencing for BRD4 and H3K27ac in Group 3 Medulloblastoma cell models.

  Dataset EGAD50000002301

• Natural genetic variation of the cardiac transcriptome in non-diseased donors and patients with dilated cardiomyopathy

  Background: Genetic variation is an important determinant of RNA transcription and splicing, which in turn contributes to variation in human traits including cardiovascular diseases.Results: Here we report the first in-depth survey of heart transcriptome variation using RNA-sequencing in 149 (97)* patients with dilated cardiomyopathy and 113 (108)* non-diseased controls. We reveal extensive differences of gene expression and splicing between dilated cardiomyopathy patients and controls, affecting known as well as novel dilated cardiomyopathy genes. Moreover, we show a widespread effect of genetic variation on the regulation of transcription, isoform usage and allele specific expression. Systematic annotation of genome wide association SNPs identifies 60 functional candidate genes for heart phenotypes, representing 20% of all published heart genome wide association loci. Focusing on the dilated cardiomyopathy phenotype we found that eQTL variants are also enriched for dilated cardiomyopathy genome wide association signals in two independent cohorts.Conclusions: RNA transcription, splicing and allele specific expression are each important determinants of the DCM phenotype and are controlled by genetic factors. Our results represent a powerful resource for the field of cardiovascular genetics. (*) numbers in parentheses represent samples that the publication is based on.

  Study EGAS00001002454

• Cancer Risk Estimates Related to Susceptibility Genes (CARRIERS)

  The CARRIERS study utilized custom amplicon-based variant screening of known cancer predisposition genes in germline DNA from breast cancer cases and age matched unaffected controls taken from predominantly cohort-based breast cancer case-control studies to establish the risks of breast cancer associated with pathogenic variants in these genes.

  Study phs002820

• Bulk-RNA Sequencing of high-grade pancreatic and non-pancreatic Neuroendocrine Neoplasms

  Therapeutic decisions in oncology depend on a precise pathological classification of individual neoplasms. Recent years have seen an intensification of research activities aimed at the extraction of clinically relevant information from patient-derived 'omics' data based on Machine-Learning models. However, a comprehensive training of Machine-Learning models requires sufficiently large numbers of training samples, which are usually not available for rare cancer types. The problem is worsened when individual tissues segregate into different cancer subtypes, as their discrimination would require even more training samples. Methods: Here, we report on a new data-augmentation technique to support the training of Machine-Learning models on ‘omics’ data from pancreatic neuroendocrine neoplasms (panNEN). PanNENs display all properties described above: Only about 2-3% of all pancreatic neoplasms are neuroendocrine and they fall into different subtypes with distinctly different prognosis, which makes the precise classification of such samples both difficult and important for therapy decisions. The approach reconstructs a given transcriptome based on healthy pancreatic cell type signatures and creates a Machine-Learning model that integrates the observed reconstruction error and predicted cell type proportions as features. Results: A benchmark of the deconvolution model predictions with the ground-truth found that the model could efficiently predict the sample grading, disease-related patient survival time, and differentiate between different subtypes in four panNEN and one mixed panNEN and non-pancreatic NENs dataset. We compared the predictive performance of the deconvolution-trained model to that of a model trained directly on the transcriptomic data, under inclusion of the Ki-67 classification gold standard biomarker, and found the performances to be comparable. Conclusion: Our approach serves as a data-augmentation technique to facilitate the training of Machine Learning models for rare cancer types via utilization of data from healthy origin. Additionally, the deconvolution results were clinically interpretable and further research can render the deconvolution approach an effective complementary asset for the clinical classification of neoplasms.

  Study EGAS00001004861

• Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) - Genome-Wide Association Study Meta-Analysis

  Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) was one of five projects funded in 2010 as part of the NCI's Genetic Associations and Mechanisms in Oncology (GAME-ON) initiative (http://epi.grants.cancer.gov/gameon/). GAME-ON's overall goal was to foster an intra-disciplinary and collaborative approach to the translation of promising research leads deriving from the initial wave of cancer GWAS. Specific goals included replication of previous GWAS findings and identification of new susceptibility loci through meta analyses of existing GWAS data and fine mapping of identified loci to better pinpoint causal variants; and identify germline variants that are associated with risk of multiple cancers. The other four funded GAME-ON projects were: the ColoRectal TransdisciplinaryStudy (CORECT), Elucidating Loci Involved in Prostate Cancer Susceptibility (ELLIPSE), Follow-up of Ovarian Cancer Genetic Association and Interaction Study (FOCI), and Transdisciplinary Research in Cancer of the Lung (TRICL). To identify additional cancer risk loci, improve the precision of fine-mapping, and facilitate cross-cancer analyses, DRIVE investigators performed a meta-analysis of eleven genome-wide association studies of breast cancer: The Australian Breast Cancer Family Study (ABCFS), the British Breast Cancer Study (BBCS), the Breast and Prostate Cancer Cohort Consortium (BPC3), the Breast Cancer Family Registries (BCFR), the Dutch Familial Bilateral Breast Cancer Study (DFBBCS), German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC), the Helsinki breast cancer family Study (HEBCS), the Mammary Carcinoma Risk factor Investigation (MARIE), the Singapore and Sweden Breast Cancer Study (SASBAC), the Triple Negative Breast Cancer Study (TNBC), and the UK2 GWAS. These studies comprised a total of 16,062 cases and 46,157 controls. Imputation to the 1,000 Genomes Project Phase 1 v3 ALL reference panel was performed by study, and summary statistics from each study were combined using fixed-effect meta analysis.

  Study phs001263

• Sequencing of liver cancer cell lines

  Liver cancer is a dreadful disease with limited therapeutic options. How to translate genomic findings into biomarkers and precision treatment is challenging. A large cell line-based platform was established, termed as Liver Cancer Model Repository. Heterogeneities in patients were faithfully recapitulated by 81 liver cancer cell lines at genomic and transcriptomic levels. This platform provides a rich resource and models to study cancer drivers and drug responses in liver cancer.

  Study EGAS00001002237

• Developing therapeutics for ovarian cancer using ovarian cancer organoids

  To identify effective drugs for clear cell ovarian cancer (CCC) and high-grade serous ovarian cancer (HGSC) through high-throughput drug screening using ovarian cancer organoids and to identify novel therapeutic targets based on the biological characteristics of CCC and HGSC through omics analysis.

  Study JGAS000764

• TREM2+ Cells in Human Basal Cell Carcinomas

  To explore the tumor immune microenvironment heterogeneity in sporadic human basal cell carcinoma tumors, we profiled two individual patient tumors by scRNA-Seq. We identified a population of TREM2+ macrophages, which have been implicated in promoting an immunosuppressive tumor microenvironment. We identify that TREM2+ can promote tumor epithelial proliferation in an immunosuppressive independent manner.

  Study phs003242

• Dissecting Autoimmune Cellular and Molecular Networks in Vitiligo

  This study consists of suction blister biopsies of primary human skin samples, collected from healthy patients, or patients with vitiligo. Healthy patients had healthy skin sampled. Vitiligo patients had both unaffected (non-lesional) and affected (lesional) skin sampled. The generated data consists of scRNA-seq samples generated following the inDrops method (Klein et al., 2015; PMID: 26000487).

  Study phs002455

• mt-scATAC-seq data and analysis

  Data and analysis of mt-scATAC-seq assay of Lin- CD34+ sorted live cells from bone marrow tissue of 4 patients with AML (before treatment with decitabine). Data files are paired-end reads in FASTQ format from the NextSeq 2000 sequencer. Analysis files are outputs from Cellranger ATAC: .h5 peak matrix file, atac_fragments.tsv.gz file and its index.

  Dataset EGAD50000001375

• Identification of cell type differences in FOXN1 mutation carriers by scRNA-seq

  scRNA-seq was performed on the BD Rhapsody platform. CD45+ lymphocytes from two FOXN1het patients and one cord blood sample as control were sorted from frozen cryopreserved samples. The samples were split and each sample was stained with 4 Sampletag antibodies before Abseq staining with the BD Immune Discovery panel and 6 additional antibodies was performed to better classify immune cell subsets. The sorted and stained populations were pooled before loading onto the BD Rhapsody cartridge. The manufacturer's protocol was followed for library construction and samples were sequenced on an Illumina NovaSeq 6000.

  Dataset EGAD50000001708

• SUM-seq data for Macrophage polarisation to M1 and M2 phenotypes experiment

  We stimulated iPSC-derived M0 macrophages with LPS and IFN-γ to induce M1 polarization or IL-4 to induce M2 polarization. To discern early and sustained responses at chromatin accessibility and gene expression levels, we collected samples at five time points along the two polarization trajectories; prior to stimulation (M0) and at 1-hour, 6-hour, 10-hour, and 24-hour intervals, each sampled in duplicates totaling 18 samples, and subjected them to SUM-seq library preparation. Sequenced files for both data modalities are provided as demultiplexed fastq files.

  Dataset EGAD50000001206

• SUM-seq data for spontaneous differentiation of iPSCs upon perturbation of GATA2, NR4A2 or SOX17

  We combined SUM-seq with arrayed CRISPR screening, modulating the expression of key lineage transcription factors (TFs) (GATA2 - mesoderm, SOX17 - endoderm, and NR4A2 - neuroectoderm) in hiPSCs via CRISPR interference (CRISPRi) or activation (CRISPRa) over a time course of spontaneous differentiation (days cultured in vitro: 0, 4, 12, and 18). This totalled to 54 samples (4 day/time points x 3 target TFs x 2-3 gRNAs per target x CRISPRi/a). Single-cell chromatin accessibility and gene expression data is provided as demultiplexed fastq files.

  Dataset EGAD50000001205

• HCC cfMeDIP-seq

  This dataset comprises 406 raw fastq files derived from 203 plasma cfDNA samples from 90 patients diagnosed with hepatocellular carcinoma (HCC). The samples include 90 baseline HCC (b-HCC) samples, collected during liver transplant or resection surgeries, and 113 postoperative follow-up (f-HCC) samples. For each sample, 10ng of cfDNA was used for library preparation utilizing cfMeDIP-seq technology based on the 5mC antibody-immunoprecipitation strategy. Libraries were validated via Bioanalyzer trace analysis and sequenced on Illumina NovaSeq 6000 or HiSeq 2500 platform with paired-end 150-bp (NovaSeq) or 125-bp (HiSeq) model for ~100 million reads per sample.

  Dataset EGAD50000000651

• Single-nuclei ATAC sequencing of pheochromocytoma and paraganglioma arising from germline SDHB mutations

  10x genomics single-nuclei ATAC sequencing of 7 SDHB-deficient non-metastatic and metastatic pheochromocytoma and paraganglioma, and 1 normal adrenal medulla sample. snATAC-seq was conducted using the "Van Helsing" protocol (dx.doi.org/10.17504/protocols.io.bw52pg8e). Once processed, snATAC-seq libraries were sequenced on the Illumina NextSeq 500 (Illumina, USA) using 50bp paired-end sequencing. Raw sequencing data in BCL format was demultiplexed using cellranger-atac mkfastq (V2.0.0). This dataset contains sequencing reads in FASTQ format. R1/R3 files contain the forward and reverse sequencing reads, respectively, R2 contains the 10x Barcode.

  Dataset EGAD50000000503

• ICGC MMML-seq Data Freeze July 2013 transcriptome sequencing

  ICGC MMML-seq Data Freeze July 2013 transcriptome sequencing

  Dataset EGAD00001000648

• ICGC MMML-seq Data Freeze November 2012 whole exome sequencing

  ICGC MMML-seq Data Freeze November 2012 whole exome sequencing

  Dataset EGAD00001000279

• ICGC MMML-seq Data Freeze November 2012 whole genome sequencing

  ICGC MMML-seq Data Freeze November 2012 whole genome sequencing

  Dataset EGAD00001000278

• snATAC-seq fragment files from 10x Multiome profiling of human fetal liver hematopoiesis

  This dataset provides snATAC-seq fragment files from a single-nucleus multiome study of 53 human fetal liver samples (5–18 post-conception weeks). Live CD45+CD235- cells were sorted from dissociated tissue, and isolated nuclei were processed using the Chromium Next GEM Single Cell Multiome ATAC + Gene Expression kit. Raw sequencing data were processed and aligned to GRCh38-2020-A-2.0.0 using Cell Ranger ARC v2.0.2.

  Dataset EGAD50000002572