14989 results for "cancer studies using rna-seq"

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• Intraductal transplantation models of human pancreatic ductal adenocarcinoma reveal progressive transition of molecular subtypes

  Pancreatic ductal adenocarcinoma (PDAC) is the most lethal common malignancy, with little improvement in patient outcomes over the past few decades. Recently, subtypes of pancreatic cancer with different prognoses have been elaborated. However, the inability to model these subtypes has precluded mechanistic investigation of their origins. Here, we present an orthotopic xenotransplantation model of human patient-derived PDAC organoids in which organoids are grafted via direct injection into the pancreatic duct of immunocompromised (NSG) mice. Tumors that arise from these intraductally grafted organoids (IGOs) enable the distinction of the two main PDAC subtypes: intraepithelial neoplasms from this model progress in an indolent or invasive manner representing the classical or basal-like subtypes of PDAC, respectively, depending on which organoid they originate from. RNA-sequencing was used to characterize the tumors that resulted from IGO xenografts, and to compare the fast- and slow-progressing IGO tumors. Tumors generated using a traditional orthotopic xenograft model, in which organoids are injected into the parenchyma of the pancreas (i.e. orthotopically grafted organoids, or OGOs), were also sequenced for comparison with the IGO tumors. To further characterize the model, laser-capture microdissection followed by RNA-sequencing was used to compare intraductal to invasive lesions resulting from IGOs. To better understand the connection between KRAS signaling and the fast-progressing phenotype, RNA-sequencing was used to compare cultured organoids in which oncogenic KRAS (GeneID: 3845) was hyperactivated to control organoids, and to compare laser-capture microdissected lesions derived from IGOs of KRAS-hyperactivated organoids to lesions derived from control organoids. To ascertain copy number, low-coverage, whole genome DNA-sequencing was performed on organoids cultured in vitro, and on laser-capture microdissected lesions derived from IGO tumors.

  Study phs002045

• RaScALL: Rapid (Ra) screening (Sc) of RNA-seq data for prognostically significant genomic alterations in acute lymphoblastic leukaemia (ALL)

  RNA-sequencing (RNA-seq) efforts in acute lymphoblastic leukaemia (ALL) have identified numerous prognostically significant genomic alterations which can guide diagnostic risk stratification and treatment choices when detected early. However, a full RNA-seq Bioinformatics workflow is time-consuming and costly in a clinical setting where rapid detection and accurate reporting of clinically relevant alterations are essential. To accelerate the identification of ALL-specific alterations (including gene fusions, single nucleotide variants and focal gene deletions), we developed the rapid screening tool RaScALL, capable of identifying more than 100 prognostically significant lesions directly from raw sequencing reads. RaScALL uses the k-mer based targeted detection tool km and known ALL variant information to achieve a high degree of accuracy for reporting subtype defining genomic alterations compared to standard alignment-based pipelines. Gene fusions, including difficult to detect fusions involving EPOR and DUX4, were accurately identified in 98% (164 samples) of reported cases in a 180-patient Australian study cohort and 95% (n=63) of samples in a North American validation cohort. Pathogenic sequence variants were correctly identified in 75% of tested samples, including all cases involving subtype defining variants PAX5 p.P80R (n=12) and IKZF1 p.N159Y (n=4). Accurate detection of intragenic IKZF1 deletions resulting in aberrant transcript isoforms was also detectable with 98% accuracy. Importantly, the median analysis time for detection of all targeted alterations averaged 22 minutes per sample, significantly shorter than standard alignment-based approaches, ensuring accelerated risk-stratification and therapeutic triage.

  Dataset EGAD00001009087

• Dataset for WGS and RNA samples of acute myeloid leukemia with MNX1 expression (non-hipo)

  This dataset contains 115 samples of WGS, ATAC, 4C and RNAseq samples of patients with acute myeloid leukemia. The sequencing was performed on Illumina HiSeq 4000, 2000 and HiSeq X using Illumina TruSeq Nano DNA and Agilent Strand Specific RNA Kits. The sequencing was always paired.

  Dataset EGAD50000001097

• Comparison clinical recommendation MASTER and panel sequencing: RNA data

  This dataset contains 19 case/control samples of RNA sequencing data. Sequencing was performed on Illumina HiSeq 4000 and NovaSeq 6000 using TruSeq Stranded mRNA Kit. The sequencing was always paired.

  Dataset EGAD50000000625

• RNAseq

  Libraries for sixty-eight cases (23 EATL and 45 MEITL) were prepared starting from 1 µg of total RNA using the TruSeq Stranded total RNA Gold kit from Illumina (San Diego, California, USA) and were sequenced in three batches on a HiSeq 4000 machine from Illumina with an average output of 63 million reads per sample (range 49-126).

  Dataset EGAD50000001619

• RNAseq of human intestinal epithelial cell layers cultured in OGM, ENR, and ENRRT

  This dataset contains RNA sequencing data in fastq and bam format from 24 samples of human jejunum organoids grown in 3D configuration or as 2D monolayers on anodisc imaging chambers (AICs) in medium formulations OGM, ENR, and ENRRT. The RNA sequencing samples were sequenced using paired-end sequencing (2x150 bp) on an Illumina NovaSeq 6000 instrument at the SciLifeLab National Genomics Infrastructure (NGI) in Uppsala.

  Dataset EGAD50000001766

• TSGKO_RNASeq

  Libraries were prepared from RNA-extracted cell lines using Illumina RNA library prep kit. Samples were sequenced on Illumina HiSeq 4000 or HiSeq 2500.

  Dataset EGAD00001008559

• Peripheral blood RNA-sequencing in 4,732 participants of the INTERVAL cohort

  We performed bulk RNA-sequencing on peripheral blood collected from 4,732 blood donors recruited as part of the INTERVAL study. Using these data, we mapped gene expression and splicing quantitative trait loci (QTLs). Then, we integrated these data with protein, metabolite and lipid QTLs in the same individuals. The study aimed to identify the shared genetic etiology across transcriptional phenotypes, molecular traits and health outcomes in humans.

  Dataset EGAD00001008015

• Circular RNA characterization in functionally distinct brain regions

  RNA extracted from middle temporal gyrus (MG) brain region of healthy elderly controls. Three pairs of samples were generated, each pair consisting of one sample that was enriched for circular RNAs using RNase R, and a second sample that was not enriched (total N=6). Remaining samples in the study from other functionally distinct brain regions are currently under process and will be released soon.

  Dataset EGAD00001004211

• RNAseq_pituitary_adenoma

  Total RNA was extracted using RNAble (Eurobio), cleaned-up with RNeasy columns (Qiagen) and sequenced. The libraries were prepared at the Genomics Platform of the Cochin Institute, following the TruSeq Stranded mRNA protocol (Illumina), starting from 1 µg of high quality total RNA. Paired end (2 × 75 bp) sequencing was performed on a Nextseq 500 platform (Illumina). FASTQ sequences were aligned on hg19 (GRCh37) human reference genome with STAR (v.2.5.2a)

  Dataset EGAD00001004996

• oncogenic gene fusions in primary colon cancers

  This dataset represents RNA-sequencing data from 278 primary colon cancers obtained from fresh-frozen tumor sections. RNA-sequencing was performed using TruSeq library preparation and samples were sequenced on Illumina NextSeq and HiSeq. The data are available as Illumina NextSeq and HiSeq fastq files (_R1.fastq and _R2.fastq for each tumor sample, 556 files in total).

  Dataset EGAD00001003291

• A comprehensive assessment of somatic mutation detection in cancer using whole genome sequencing

  As whole genome sequencing for cancer genome analysis becomes a clinical tool, a full understanding of the variables affecting sequencing analysis output is required. Using tumor-normal sample pairs from two different types of cancer, chronic lymphocytic leukemia and medulloblastoma, we conducted a benchmarking exercise within the context of the International Cancer Genome Consortium. We compare sequencing methods, analysis pipelines, and even validation methods. Here we show that using PCR-free methods and increasing sequencing depth to ~100x showed benefits, as long as the tumor:control coverage ratio remains balanced. We observe widely varying mutation call rates and low concordance among analysis pipelines, reflecting the artifact-prone nature of the raw data and lack of standards for dealing with the artifacts. However, armed with the benchmark mutation set we have created, we show that many issues are in fact easy to remedy and have an immediate positive impact on mutation detection accuracy.

  Study EGAS00001001539

• Genome_Diversity_in_Africa_Project__Uganda

  Globally, human populations show structured genetic diversity as a result of geographical dispersion, selection and drift. Understanding this genetic variation can provide insights into the evolutionary processes that shape both human adaptation and variation in disease. Populations from SSA have the highest levels of genetic diversity. This characteristic, in addition to historical genetic admixture, can lead to complexities in the design of studies assessing the genetic determinants of disease and human variation. However, such studies of African populations are also likely to provide new opportunities to discover novel disease susceptibility loci and variants and refine gene–disease association signals. A systematic assessment of genetic diversity within SSA would facilitate genomic epidemiological studies in the region. The Genome Diversity in Africa Project (GDAP) aims to produce a comprehensive catalogue of human genetic variation in SSA, including single nucleotide polymorphisms (SNPs), structural variants, and haplotypes. This resource will make a substantial contribution to understanding patterns of genetic diversity within and among populations in SSA, as well as providing a global resource to help design, implement and interpret genomic studies in SSA populations and studies comprising globally diverse populations, complementing existing genomic resources. Specifically, we plan to carry out high depth whole genome sequencing of up to 2000 individuals across Africa (25 individuals from each ethnolinguistic group). Our scientific objectives are to: 1) develop a resource that provides a comprehensive catalogue of genetic variation in populations from SSA accessible to the global scientific community; 2) characterise population genetic diversity, structure, gene flow and admixture across SSA; 3) develop a cost-efficient, next-generation genotype array for diverse populations across SSA; and 4) facilitate whole genome-sequencing association studies of complex traits and diseases by developing a reference panel for imputation and resource for enhancing fine-mapping disease susceptibility loci. These scientific objectives will be supported by cross-cutting operational activities, including network and management of the consortium, research ethics, and research capacity building in statistical genetics and bioinformatics

  Study EGAS00001002523

• Genome Wide Association Study of Chronic TMD: Discovery Phase

  The OPPERA project is investigating effects of genetic, physiological, psychological and clinical factors on the risk of developing painful temporomandibular disorder (TMD). This case-control study is a discovery-phase genome wide association study that aims to identify commonly-occurring genetic variants associated with chronic TMD. It represents the first step in a series of studies that seek to identify the genetic background of TMD. For related phenotype and targeted genotype data, please see additional studies from the OPPERA project at dbGaP phs000761).

  Study phs000796

• Genome-Wide Association Analysis of Biomarkers in the InCHIANTI and BLSA

  Version 1 The aim of this study was to identify gene(s) associated with serum iron concentrations. This dataset contains the results from a meta-analysis of 1919 subjects from the InCHIANTI and Baltimore Longitudinal Study of Aging (BLSA). Both studies are population based prospective studies that evaluate the contributors of normal aging. Version 2 We performed genome wide analysis of 93 circulating biomarkers in the InCHIANTI study of 1210 individuals, after imputation from the 1000 genomes project.

  Study phs000215