11925 results for "cancer rna-seq"

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• small RNA next generation sequencing in head and neck cancer

  small RNA next generation sequencing in head and neck cancer

  Dataset EGAD00001008402

• cfDNA methylation dataset for colon cancer

  Enzymatic conversion-based methylation sequencing data (EM-seq) for colon cancer used in the MESA study

  Dataset EGAD50000000075

• Kidney_tumour_RNA

  This is a bulk DNA and RNA sequencing study of human renal tumours

  Study EGAS00001002487

• Deciphering RBP - alternative splicing networks in ALS iPSC-MN: TDP-43, NOVA1, NOVA2 and RBFOX2 eCLIP-seq

  We produced enhanced cross-linking immunoprecipitation (eCLIP) sequencing data of the RNA binding proteins (RBP) TDP-43, NOVA1, NOVA2 and RBFOX2 in iPSC motor neurons of two healthy control individuals, respectively.

  Study EGAS00001005880

• Processing of tissue and cfDNA samples of CRC patients using Active-seq

  In this study, we have developed a novel technology called Active-Seq (Azide Click Tagging for In Vitro Epigenomic sequencing), a base conversion-free technology used to profile the DNA methylation status of a sample, based on the isolation of DNA containing unmodified CpG sites using a mutated bacterial methyltransferase enzyme and a synthetically prepared cofactor analogue. We procured samples from colorectal cancer (CRC) patients including tumour tissue as well as normal adjacent tissues. The data enabled us to identify CRC-specific markers that could then be used to track and monitor the presence of tumour DNA in cell-free DNA plasma (cfDNA) samples procured in the same patients. The FASTQ files of all the colorectal cancer tissues, normal adjacent tissues as wells as the cfDNA samples are available in this submission. The data made available in this study have been used to outline an approach for tumour-informed disease profiling in colorectal cancer cfDNA samples using Active-Seq.

  Study EGAS50000001226

• Genetic and Genomic Analysis of Primary Human Chondrocytes

  Osteoarthritis (OA) is a common, degenerative joint disease characterized by the gradual breakdown of the cartilage in the joints. Despite osteoarthritis affecting over 500 million individuals, the lack of a comprehensive mechanistic understanding of the disease has greatly hindered the development of adequate treatment options. To address this, we generated RNA-seq, Hi-C, and ATAC-seq data in primary human chondrocytes treated with either PBS as resting or a fibronectin fragment (FN-f) to model the OA chondrocyte phenotype. We mapped expression quantitative trait loci and splicing quantitative trait loci, and performed colocalization with OA GWAS data to identify putative OA risk genes. Integration of ATAC-seq and Hi-C data provided insights into the regulatory mechanisms. This study revealed both expression-based and splicing-based OA risk genes, several of which had not been previously identified in chondrocyte studies.

  Study phs003581

• Genomic Studies of Bipolar Disorder in a Large Cohort from The Netherlands

  The transcriptomes of whole blood samples of a large cohort of individuals from the Netherlands were assayed using RNA-seq. These whole blood samples included patients diagnosed with schizophrenia or bipolar disorder, family members of patients, and healthy controls. Within the whole blood dataset, about 600 of the samples were sequenced to an average of 13.9 million mapped reads per sample, while about 2,000 were sequenced to an average of 5.9 million mapped reads per sample, in order to assess changes in power to detect eQTLs based on sample size. We find that expression, defined as the log transcripts per million reads (TPMs), is highly correlated when comparing the moderate (13.9M) and lower (5.9M) coverage datasets. Additionally, a subset of about 150 individuals provided fibroblast cell lines which were assessed for the levels of gene expression using RNA-seq at an average of 50 million mapped reads per sample. We use downsampling techniques to generate synthetic datasets derived from this real fibroblast RNA-Seq data to explore the relationship between coverage and number individuals in an eQTL analysis. Corresponding individual-level genotypes for the samples included in the expression matrix are also made available. The genotypes were derived from multiple other project cohorts, each using different genotyping platforms including OmniExpress Exome, Global Screening Array, Illumina550, and PsychChip. A Plink file set of these cohorts is provided here after merging the separate project file sets and extracting only the individuals who have expression data also provided here; quality control for SNP-missingness is recommended. We demonstrate in this study that given a fixed budget, eQTL discovery power can be increased by lowering the sequencing depth per sample to allow for an a greater number of individuals sequenced. 

  Study phs002856

• Significant and pervasive effects of RNA degradation on Nanopore direct RNA sequencing

  Abstract Oxford Nanopore direct RNA sequencing (DRS) is capable of sequencing complete RNA molecules and accurately measuring gene and isoform expression. However, as DRS is designed to profile intact RNA, expression quantification may be more heavily dependent upon RNA integrity than alternate RNA-seq methodologies. It is currently unclear how RNA degradation impacts DRS or if it can be corrected for. To assess the impact of RNA integrity on DRS we performed a degradation time-series using SH-SY5Y neuroblastoma cells. Our results demonstrate that degradation is a significant and pervasive factor that can bias DRS measurements, including a reduction in library complexity resulting in an overrepresentation of short genes and isoforms. Degradation also biases differential expression analyses; however, we find explicit correction can almost fully recover meaningful biological signal. In addition, DRS provided less biased profiling of partially degraded samples than nanopore cDNA-PCR sequencing. Overall, we find samples with RIN > 9.5 can be treated as undegraded and samples with RIN > 7 can be utilised for DRS with appropriate correction. These results establish the suitability of DRS for a wide range of samples, including partially degraded in-vivo clinical and post-mortem samples, whilst limiting the confounding effect of degradation on expression quantification.

  Study EGAS00001006542

• SCLC MeDIP-seq

  Methylation profile of 33 patients with small cell lung cancer (SCLC) for both, tumour and normal lung samples using MeDIP-seq.

  Dataset EGAD50000000724

• Aggregated panel-seq VCF for initial cohort screened for BoB

  Aggregated VCF file from cancer genes panel seq for the initial (n=500) cohort of solid tumors screened for the Basket of Baskets study

  Dataset EGAD00001008437