14978 results for "cancer studies using rna-seq"

in 15.36 milliseconds.

• BLUEPRINT release January 2015, RNA-Seq for megakaryocyte-erythroid progenitor cell

  RNA-Seq data for 4 megakaryocyte-erythroid progenitor cell sample(s). 4 run(s), 4 experiment(s), 4 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001140

• BLUEPRINT release January 2015, RNA-Seq for hematopoietic multipotent progenitor cell

  RNA-Seq data for 3 hematopoietic multipotent progenitor cell sample(s). 9 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001186

• BLUEPRINT release August 2014, RNA-Seq for hematopoietic multipotent progenitor cell

  RNA-Seq data for 3 hematopoietic multipotent progenitor cell sample(s). 9 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001000919

• BLUEPRINT release January 2015, RNA-Seq for granulocyte monocyte progenitor cell

  RNA-Seq data for 3 granulocyte monocyte progenitor cell sample(s). 3 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001146

• BLUEPRINT release August 2014, RNA-Seq for hematopoietic stem cell

  RNA-Seq data for 3 hematopoietic stem cell sample(s). 8 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001000939

• BLUEPRINT release January 2015, RNA-Seq for hematopoietic stem cell

  RNA-Seq data for 6 hematopoietic stem cell sample(s). 13 run(s), 6 experiment(s), 6 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001156

• BLUEPRINT release January 2015, RNA-Seq for common lymphoid progenitor

  RNA-Seq data for 5 common lymphoid progenitor sample(s). 20 run(s), 5 experiment(s), 5 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001184

• Influence of the Microbiome on Epigenetic Mechanisms in IBD

  Live CD4 T cells were sorted from inflamed and non-inflamed tissue samples of IBD patients or from healthy and IBD blood samples. ATAC-Seq libraries were generated from live CD4 T cells sorted from i) inflamed and non-inflamed tissue samples, ii) healthy and IBD blood samples, or from iii) CD4 T cell subsets polarised from healthy blood samples. After isolating crude nuclei, live CD4+ T cells were treated with Tagment DNA buffer and Tagment DNA Enzyme (Nextera DNA Library Prep Kit, Illumina), and then the DNA was purified by MinElute PCR Purification Kit (Qiagen). Transposed DNA fragments were amplified using specific adapters followed by purification with MinElute PCR Purification Kit (Qiagen). Fragments from 240-360pb were selected in the PippinHT system (Sage Science). The quality of the library and its DNA concentration were assessed by Bioanalyzer instruments (Agilent Technologies) and ultimately submitted for sequencing using Illumina HiSeq 2500 sequencer, V4 chemistry. On the other hand, single cell RNA-Seq libraries were generated exclusively from inflamed and non-inflamed tissue samples of Crohn’s disease patients. Briefly, live CD4 T cells were captured and encapsulated before cDNA amplification using the 10X Genomics Chromium Platform. Samples were prepared as outlined by 10x genomics Single Cell 3’ Reagent Kits v2 user guide. Samples were sequenced on a HiSeq 2500 with the following run parameters: Read 1 – 26 cycles, read 2 – 98 cycles, index 1 – 8 cycles.

  Dataset EGAD00001011066

• Single-cell RNA and DNA sequencing data obtained after genome-and-transcriptome separation.

  This dataset contains single-cell RNA and DNA sequencing data (fastq, n=928) obtained after genome-and-transcriptome separation. The RNA-seq data was obtained by Smart-seq2 amplification and Nextera XT library preparation. The DNA-seq data was obtained either by Gtag library preparation and amplification or by PicoPlex whole-genome amplification followed by Nextera XT library preparation. The single cells originate from a human PDX melanoma model and from HCC38 and HCC38 BL cell lines. The sequencing libraries were sequenced with Illumina instruments.

  Dataset EGAD00001010096

• Collaborative Study of Genes, Nutrients and Metabolites (CSGNM)

  While many genetic and metabolic studies focus on understanding disease states, this genome wide association study (GWAS) was designed to understand the influence of genetic variants on circulating metabolites and factors in normal, healthy individuals. Over 2500 healthy, ethnically Irish college students attending Trinity College Dublin completed a health and diet questionnaire and provided a non-fasting blood sample to be used for genetic and metabolic analysis. The questionnaire was designed to collect information on age, sex, height, weight, medical conditions, smoking, dietary habits, and consumption of alcohol, fortified foods, and supplements. Extracted DNA was genotyped using the Illumina HumanOmni1-Quad Beadchip. Metabolites were measured using a number of methods and focused on those relevant to the folate/vitamin B12 pathway.

  Study phs000789

• MRC 60 snRNA-seq

  This dataset includes snRNA-seq generated from post-mortem brain and genotype from 60 individuals. The snRNA-seq data was generated using the 10X Single Cell Next GEM Chip targeting a minimum 5,000 nuclei per sample and libraries prepared using the Chromium Single Cell 3′ Library and Gel Bead v3 kit according to manufacturer’s instructions. cDNA libraries were sequenced using the Illumina NovaSeq 6000 system at a minimum sequencing depth of 30,000 paired-end reads per nucleus. Samples were pooled (max 4 per pool) and sequenced over 4 lanes. Pool-specific genotype files (containing genotype information at individual level) were used to demultiplex mapped pools into individual-level single-cell data. Donor DNA from samples processed at Imperial College were genotyped using the Illumina Infinium Global Screening Array v2.0.

  Dataset EGAD50000000965

• MOSAIC Window DLBCL Data

  This dataset will include Spatial Transcriptomics from the 10x Genomics Visium SD technology, Single-Cell RNA-Seq from the 10x Genomics Chromium Flex protocol, Bulk RNA-Seq (sequenced on Illumina), WES (sequenced on Illumina), H&E, and Clinical data from 10 DLBCL patients. For Visium and Chromium data we are sharing the outputs of respectively Space Ranger and Cell Ranger. For Bulk RNA-Seq we are including processed files such as counts.tsv, FPKM.tsv, fusion_genes.tsv, TPM count matrix.tsv, DESeq2 count matrix.tsv, multiqc_report. For WES, we are sharing the presence or absence of single nucleotide variants (SNVs), small insertions and deletions (indels), and copy number variants (CNVs).* Researchers from private or public institutions outside the MOSAIC Consortium will be able to apply to access this data and, pending approval, use the data for their research.

  Dataset EGAD50000001702

• MOSAIC Window Ovarian Data

  This dataset will include Spatial Transcriptomics from the 10x Genomics Visium SD technology, Single-Cell RNA-Seq from the 10x Genomics Chromium Flex protocol, Bulk RNA-Seq (sequenced on Illumina), WES (sequenced on Illumina), H&E, and Clinical data from 15 Ovarian patients. For Visium and Chromium data we are sharing the outputs of respectively Space Ranger and Cell Ranger. For Bulk RNA-Seq we are including processed files such as counts.tsv, FPKM.tsv, fusion_genes.tsv, TPM count matrix.tsv, DESeq2 count matrix.tsv, multiqc_report. For WES, we are sharing the presence or absence of single nucleotide variants (SNVs), small insertions and deletions (indels), and copy number variants (CNVs).* Researchers from private or public institutions outside the MOSAIC Consortium will be able to apply to access this data and, pending approval, use the data for their research.

  Dataset EGAD50000001704

• MOSAIC Window Mesothelioma Data

  This dataset will include Spatial Transcriptomics from the 10x Genomics Visium SD technology, Single-Cell RNA-Seq from the 10x Genomics Chromium Flex protocol, Bulk RNA-Seq (sequenced on Illumina), WES (sequenced on Illumina), H&E, and Clinical data from 10 Mesothelioma patients. For Visium and Chromium data we are sharing the outputs of respectively Space Ranger and Cell Ranger. For Bulk RNA-Seq we are including processed files such as counts.tsv, FPKM.tsv, fusion_genes.tsv, TPM count matrix.tsv, DESeq2 count matrix.tsv, multiqc_report. For WES, we are sharing the presence or absence of single nucleotide variants (SNVs), small insertions and deletions (indels), and copy number variants (CNVs).* Researchers from private or public institutions outside the MOSAIC Consortium will be able to apply to access this data and, pending approval, use the data for their research.

  Dataset EGAD50000001706

• RNA-seq data from the EUROBATS Project in four tissues: Fat, LCL, Skin and whole Blood.

  EUROBATS consists of RNA-seq data for female twins in lisphoblastoide cell lines (LCL), abdominal adipose tissue (Fat), abdominal skin tissue (Skin) and whole blood (Blood). The samples are a combination of MZ twins, DZ twins and singletons. The goal of the project is to find biomarkers related with the ageing process and cellular senescence.

  Study EGAS00001000805

• single-cell RNA-seq Case-Control study of children progressing to Type1 diabetes

  It was a single-cell RNA sequencing study on the PBMC samples from four Finnish children at risk of developing Type 1 diabetes and their gender age and HLA matched control children. All four Case children were positive for multiple islet specific autoantibodies and two of them also progressed to clinical disease during the follow up whereas the control children remain negative for all autoantibodies. Single-cell analysis confirmed some of the signatures obtained from the bulk data. It identified that high IL32 in case samples in the bulk RNA-seq was contributed mainly by activated T cells and NK cells. Trajectory analysis of the scRNA-seq data suggested that IL32 expression increased as the T cells moved towards activated state.

  Dataset EGAD00001005768

• Luminal progenitor cell line iHBEC(CD117)

  Basal-like breast cancer originates in luminal progenitors, frequently with an altered PI3K pathway, and focally in close association with genetically altered myoepithelial cells at the site of tumor initiation. The exact trajectory behind this bi-lineage phenomenon remains poorly understood. Here we used a breast cancer relevant transduction protocol including hTERT, shp16, shp53, and PIK3CA(H1047R) to immortalize FACS isolated luminal cells, and we identified a candidate multipotent progenitor. We found that the apparent luminal phenotype of these oncogene transduced progenitors was metastable giving rise to basal-like cells dependent on culture conditions. After culturing the cells for more than 60 passages, cells were subjected to scRNA-seq as well as bulk RNA sequencing of two subpopulations (CD271+ and CD271-).

  Dataset EGAD50000000723

• scRaCH-seq data targeting BTK and SF3B1

  Data set for scRaCH-seq manuscript. We base call the fast5 using guppy.

  Dataset EGAD50000000235

• International Cancer Proteogenome Consortium (ICPC): Proteogenomics of Oral Squamous Cell Carcinoma in Taiwan

  Oral cavity squamous cell carcinoma (OSCC) is the major subtype (~90%) of oral cancer, which is leading cancer with high mortality worldwide. The environmental risk factors associated with OSCC are quite different in Taiwan as compared to other populations. In most non-Taiwanese populations, OSCC is often associated with tobacco chewing, cigarette smoking, alcohol drinking, or human papillomavirus (HPV) infection. In Taiwan, in contrast, the OSCC occurs mainly in males aged 30 years or older who have habitual betel nut chewing, cigarette smoking, and alcohol drinking. To uncover molecular mechanisms and identify new therapeutic strategies of oral squamous cell carcinoma, this study recruited 112 treatment-naïve OSCC patients and generated multi-omics data, including WES, RNA-Seq, TMT-labeled proteomes, and phosphoproteomes from paired normal/tumor tissues. We have investigated our multi-omics data, covering molecular characterizations such as mutation signatures, impacts of mutations on RNA/protein/phosphoprotein, RNA-protein correlation, somatic copy number alteration, and the relationship with RNA & protein, proteome, and phosphoproteome analysis (T vs NAT), novel peptides (somatic variants, neoantigens, CT antigens, T cell receptors, etc.), as well as molecular subtyping (& pathway analysis). Notably, we have discovered a higher frequency of APOBEC3B deletion polymorphism in the Taiwanese population, and APOBEC3A is a potential prognostic biomarker. Our proteogenomic data enable us to further stratify patients into four groups based on APOBE3A expression level, which is found inversely correlated with EGFR expression. Therefore, the patients with high EGFR and low APOBEC3A expression may benefit from anti-EGFR therapy, while those with low EGFR and high APOBEC3A expression may benefit from immunotherapy. To clarify if there is an interaction between genetic polymorphisms and betel nut chewing habit in Taiwanese OSCC, we plan to concurrently evaluate the effect of habitual betel nut chewing on the gene mutation pattern, the dysregulated protein/phosphoproteins as novel biomarkers and/or therapeutic targets, and the molecular subtyping of patients for effective treatments. 

  Study phs002580