15075 results for "cancer studies using rna-seq"

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• WCDT deep RNA-seq

  This dataset includes fastq files for total RNAseq of 104 patient biopsies with metastatic castration resistant prostate cancer. The RNAseq libraries were rRNA depleted and sequenced at 150bp paired-end on Illumina Novaseq.

  Dataset EGAD00001008799

• Bulk-tissue RNA-sequencing paired nuclear and cytoplasmic fractions of anterior prefrontal cortex, cerebellar cortex and putamen tissues from post-mortem neuropathologically-confirmed control individuals.

  Gaining insight into the genetic regulation of gene expression in human brain is key to the interpretation of genome-wide association studies for major neurological and neuropsychiatric diseases. Expression quantitative trait loci (eQTL) analyses have largely been used to achieve this, providing valuable insights into the genetic regulation of steady-state RNA in human brain, but not distinguishing between molecular processes regulating transcription and stability. RNA quantification within cellular fractions can disentangle these processes in cell types and tissues which are challenging to model in vitro. We investigated the underlying molecular processes driving the genetic regulation of gene expression specific to a cellular fraction using allele-specific expression (ASE). Applying ASE analysis to genomic and transcriptomic data from paired nuclear and cytoplasmic fractions of anterior prefrontal cortex, cerebellar cortex and putamen tissues from 4 post-mortem neuropathologically-confirmed control human brains, we demonstrate that a significant proportion of genetic regulation of gene expression occurs post-transcriptionally in the cytoplasm, with genes undergoing this form of regulation more likely to be synaptic. These findings have implications for understanding the structure of gene expression regulation in human brain, and importantly the interpretation of rapidly growing single-nucleus brain RNA-sequencing and eQTL datasets, where cytoplasm-specific regulatory events could be missed.

  Study EGAS00001006380

• Whole exome sequencing in multiplex cleft families from a consortium

  Building upon our previous linkage and association studies, we will use whole exome sequencing studies of second and third degree affected relatives drawn from multiplex families. Our specific aims are: 1) To conduct whole exome sequencing on affected members (2° and 3° relatives) drawn from multiplex cleft families from a consortium to identify novel genes causing non-syndromic oral clefts; 2) To confirm and test the role of rare variants in these novel genes through confirmatory Sanger sequencing, plus linkage information using additional members available in these multiplex families. This whole exome sequencing approach will combine evidence from linkage studies and large scale sequencing to identify novel genes causing oral clefts in multiplex cleft families.

  Study phs000459

• CRUK Accelerator: oesophageal adenocarcinoma whole exome and RNA-seq raw sequencing data

  Whole exome and RNASeq raw sequencing data for a cohort of 7 male patients with oesophageal adenocarcinoma. Median age at diagnosis was 68. Tumour tissue and PBMCs were used for whole exome sequencing and RNA sequencing. This data was generated as part of a study funded by a Cancer Research UK Centres Network Accelerator Award Grant (A21998).

  Dataset EGAD00001008489

• Transcriptomic hallmarks and RNA isoform diversity in human neurodegenerative disease

  Single-cell RNA-sequencing (scRNA-seq) has enabled high-throughput RNA analysis at an unprecedented resolution. One staple analysis in scRNA-seq is identifying differentially expressed genes (DEGs) between two populations. However, the mRNA isoforms have often been overlooked in these transcriptomic analyses due to technical limitations. In order to investigate the mRNA isoforms at the single-cell level, we used a targeted single-nucleus long-read RNA-sequencing method combining single-nucleus short-read RNA-seq (snRNA-seq), target gene enrichment, and Iso-Seq. We performed snRNA-seq and targeted single-nucleus Iso-Seq (snIso-Seq) on 25 human frontal cortices from individuals with AD, DLB, or PD, and age-matched controls. Our short-read transcriptome analysis revealed that DLB samples had more overlapping DEGs with AD than PD despite sharing pathological hallmarks with PD. We observed vast RNA isoform diversity in the 50 genes targeted, including genes that were not differentially expressed in our short-read data. We also identified several novel isoforms that are not included in the reference annotation. Our data collectively characterize the transcriptome of various neurodegenerative diseases and illuminate the mRNA isoform diversity.

  Study EGAS50000000132

• RNAseq of GEPARSIXTO

  294 formalin-fixed paraffin-embedded (FFPE) tissue samples were sent to the UNC Lineberger Comprehensive Cancer Center Translational Genomics Lab (TGL) for RNA isolation using the Maxwell 16 MDx Instrument (Promega AS3000) and the Maxwell 16 LEV RNA FFPE Kit (Promega AS1260) following the manufacturer’s protocol (Promega 9FB167). 279 total RNA sequencing libraries were prepared at TGL using a Bravo Automated Liquid-Handling Platform (Agilent G5562A) and the TruSeq Stranded Total RNA Library Prep Gold Kit (Illumina 20020599) following the manufacturer’s protocol (Illumina 1000000040499). RNAseq library quality and quantity were measured using a TapeStation 4200 (Agilent G2991AA) and Qubit 3.0 fluorometer (Life Technologies Q33216), pooled at equal molar ratios, and denatured following the manufacturer’s protocol (Illumina 1000000106351). 271 total RNA sequencing libraries were sequenced at TGL on NovaSeq 6000 (Illumina 20012850) S4 flow cells (Illumina 20028313) following the manufacturer’s protocol (Illumina 1000000019358) using a 2x50 bp paired-end configuration and pool sizes of 91 libraries to target a read depth of 110 million clusters per library on average.

  Dataset EGAD00001010201

• Functional and genomic heterogeneity of long-term self-renewing compartment as the origin of treatment resistance in pancreatic tumors

  Adaptive drug-resistance mechanisms allow human tumors to evade treatment through selection and expansion of treatment-resistant clones. Here, studying clonal evolution of tumor cells derived from human pancreatic tumors, we demonstrate that in vitro cultures and in vivo tumors are maintained by a common set of tumorigenic cells that can be used to establish clonal replica tumors (CRTs), large cohorts of animals bearing human tumors with identical clonal composition. Using CRTs to conduct quantitative assessments of adaptive responses to therapeutics, we uncovered a multitude of functionally heterogeneous subpopulations of cells with differential degrees of drug sensitivity. High-throughput isolation and deep characterization of unique clonal lineages showed genetic and transcriptomic diversity underlying functionally diverse subpopulations. Molecular annotation of gemcitabine-naïve clonal lineages with distinct responses to treatment in the context of CRTs generated signatures that can predict the response to chemotherapy, representing a potential biomarker to stratify patients with pancreatic cancer. RNA-Seq data on these clones are available on EBI ENA, under accession number: PRJEB27735.

  Study EGAS00001003442

• NCI Expanded Genome-Wide Association Study of Renal Cell Carcinoma

  The National Cancer Institute (NCI) expanded genome-wide association study (GWAS) of renal cell carcinoma (RCC) was a 2nd GWAS of this cancer performed by NCI to further investigate common genetic variants associated with RCC risk. The GWAS includes 2,781 cases and 2,940 controls of European background from 13 studies conducted in the US and Finland (10 cohorts, 2 case series, 1 hospital biorepository), scanned using the Illumina OmniExpress and Omni2.5 SNP arrays. SNP-level results from this GWAS were combined by meta-analysis with results from the earlier NCI RCC GWAS (phs000351.v1.p1; 1,311 cases and 3,424 controls) and others conducted by the International Agency for Research on Cancer (scan 1: 2,438 cases and 5,071 controls; scan 2: 2,781 cases and 2,940 controls), The University of Texas MD Anderson Cancer Center (893 cases and 556 controls) and the Institute for Cancer Research, UK (944 cases and 4,024 controls). Only data from the second NCI scan are included in this dbGaP submission. Data from 239 cases and 229 controls from this scan will be deposited in dbGaP separately under the Women's Health Initiative (phs000200).

  Study phs001736

• MutWP4__CRUK_Grand_Challenge_Mutographs_of_Cancer__Pancreatic_Organoids

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, Kings College London will characterise the mutational signatures induced by putative human carcinogens in order to identify the origins of mutational signatures found in human cancers. To achieve this human organoid cell cultures will be exposed to a representative catalogue of known or suspected human carcinogens and mutagens and, using whole genome sequencing, the patterns of mutations induced by them will be determined. Somatic mutational signatures will be subsequently extracted by non-negative matrix factorisation methods and correlated with exposure data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development.

  Study EGAS00001003520

• Characterization of Structural Variants in Acute Myeloid Leukemia Patients

  Acute myeloid leukemia (AML) is the most common form of acute leukemia in adults and is expected to increase in frequency as the population ages. Most subtypes of AML are difficult to treat, in part due to a lack of understanding of possible molecular targets. Furthermore, the peak incidence of AML is in the sixth decade, and patients over the age of 65 do not tolerate chemotherapeutic regimens well. Moreover, AML has been highly studied as a model for other cancers. An RNA Biology Program is established aiming to develop comprehensive datasets through the profiling of all different classes of RNA expression, RNA editing, alternative splicing, and non-coding RNAs in CD34+ normal hematopoietic stem cells (HSC), their differentiated progeny, and AML clinical samples that could be utilized to understand leukemia. These datasets will allow us to computationally identify and characterize various RNA species and processes to enable the development of effective therapeutics to AML, leukemia and other types of cancers. Epigenetic factors such as methylation and histone modifications will also be characterized from these clinical samples allowing for integrated dataset analysis to look at the cross-talk between RNA and DNA/epigenomics in leukemia. The following is a subsidiary study of the above study description where whole genome sequencing was performed on two collected AML patients' samples using Illumina and Oxford Nanopore sequencing technologies. The aim is to characterize the roles of structural variants (SV) in RNA biology which are potentially important for AML development using our own established long-read SV characterization tool, NanoVar. The advent of Oxford Nanopore sequencing technologies has opened a new avenue for better SV characterization by having longer sequencing reads. NanoVar is an accurate and rapid SV characterization tool that utilizes low-depth Nanopore sequencing data. NanoVar makes use of split-reads and hard-clipped reads for SV discovery and employs a simulation-trained neural network classifier for true positive enrichment. NanoVar exhibited higher accuracy and faster computational speed amongst other long-read SV detection tools in simulated data. In AML patient data, NanoVar displayed excellent accuracy in SV characterization (16/16 SVs validated in two patients) by using only 4x depth sequencing data. In summary, NanoVar improves the accuracy and speed of SV characterization at a lower sequencing cost, an approach compatible with clinical studies.

  Study phs001847