15178 results for "cancer studies using rna-seq"

in 18.78 milliseconds.

• Breakpoint detection using long insert whole genome sequencing

  In this study, we hypothesize that shallow long insert whole genome sequencing (LI-WGS) increases our power for detecting breakpoints compared to shallow short insert WGS. We performed a priori analyses to demonstrate the benefits of LI-WGS, developed a long insert library preparation protocol based off Illumina's protocol, and compared LI-WGS against short insert WGS on test samples. We then used long insert WGS to identify translocations and copy number changes in tumor and germline samples collected from cancer patients with different malignancies.

  Study phs000646

• Genome-Wide Predictors of Treatment-Related Toxicities in SWOG S0221 Trial

  When undergoing treatment for breast cancer, many women experience severe side effects, some of which result in treatment-related death and some that can persist for years. Little is understood regarding factors that may predict drug toxicities. Pharmacogenetics, the investigation of variants in candidate genes in drug metabolism pathways, has been used to determine susceptibility to treatment-related toxicities, as well as to cancer recurrence. Although there have been some strong and important findings using this approach, such as identification of single nucleotide polymorphisms (SNPs) that predispose to side effects associated with thiopurines and irinotecan, there has been less progress in assessment of genetic variants that predispose to toxicities resulting from the multi-drug regimen commonly used to treat breast cancer, anthracyclines (A), cyclophosphamide (C), and taxanes (T). These difficulties in identification of key gene variants may be due to the complex metabolic pathways of these drugs, the lack of rate limiting enzymes in the processes, or the limitations of single SNP analysis, rather than capturing all of the variability across the genes. In addition to drug metabolism pathways, however, there may be a number of constitutional factors or other processes that affect damage to normal tissues in the course of chemotherapy, some known or hypothesized, such as DNA repair and oxidative stress pathways, and others not yet discovered. However, there have been no clear candidate genes that account for a large part of the variation in drug or treatment response, and there are likely important genes that influence sensitivity of cells to chemotherapy through unknown pathways which have not yet been identified or hypothesized. The present technological capabilities to screen the entire genome for variants that discriminate populations allows the opportunity to identify these as yet unknown pathways, and open the doors to exciting new avenues of research into mechanisms that had not been previously considered. We conducted a genome-wide scan (GWAS) in a clinical trial (S0221), in which women with high risk breast cancer were treated with different dosing schedules of C, A and T. Blood specimens were collected and banked, and DNA extracted for genotyping on the Illumina OMNI 1M platform. The GWAS data were used to examine genetic variants significantly associated with grades 3 and 4 toxicities, including peripheral neuropathy recorded during the T segment. In S0221, toxicities were graded according to the NCI Common Toxicity Criteria for Adverse Events (CTCAE). The neurotoxicity is predominantly a distal sensory neuropathy, which is characterized by pain, numbness, tingling, and reduced functional capacity in the extremities. Other symptoms include parasthesias, ataxia, impaired vibration and joint position sense, and loss of tendon reflexes. By using a GWAS approach, it is likely that important pathways not previously considered can be revealed as important in susceptibility to treatment-related toxicities, identifying those at greatest risk for alternate drugs or dose reduction, and opening new areas of research for prevention of taxanes-related neuropathy among patients receiving chemotherapy for breast cancer.

  Study phs001428

• RNA-sequencing

  RNA-sequencing for a cohort of B-ALL samples : 5 Down Syndrome (DS), 16 Hyperdiploid (HeH), 6 iAMP21, 9 other. RNA-sequencing for B-cell progenitors from 3 healthy. It also contains RNA-sequencing datasets of Patient-Derived Xenografts (X) developed from the B-ALL samples : 4 Down Syndrome (DS), 4 Hyperdiploid (HeH), 1 iAMP21, 3 other.

  Dataset EGAD00001005426

• scRNA-seq data of human nuclei collected from the temporal cortex of 17 individuals.

  Transcriptome analysis of single cells has tremendously increased our understanding of cellular heterogeneity in complex tissues. However, whole-cell transcriptomics cannot be applied to frozen tissue due to poor cell recovery after freezing. Human tissue is usually limited to tissue banks containing frozen material, and since single nuclei can be readily isolated from frozen tissues, human cell nuclei can substitute whole-cell analysis to extract single cell transcriptomes from human cells. Despite the importance of human tissue analysis, single-nucleus RNA-sequencing protocols are less developed than those for whole cells. SMARTSeq, either in form of custom-made SMARTSeq2 or as part of the Fluidigm C1 platform, provides a high resolution view of the transcriptome, has proven effective for whole-cell analysis and has recently been adapted to nuclei. Here, we show that the standard SMARTSeq2 protocol applied to single nuclei biases the amplification of single-nucleus transcriptomes due to unspecific capturing of transcripts, which can comprise the majority of reads in single-nucleus RNA-sequencing libraries. We demonstrate that modification of the SMARTSeq2 protocol with respect to template switching and cDNA amplification conditions generates sequencing libraries of higher complexity, allows for detection of more genes and reduces biases of the amplified transcriptome. In addition, modification of SMARTSeq2 chemistry allows for gene detection at shallower sequencing depth, thus increasing efficiency and decreasing cost.

  Study EGAS00001002882

• Reference epigenome SMC07 mRNA-Seq data generated from KEP study

  SMC07 mRNA-Seq paired end data

  Dataset EGAD00001007174

• Reference epigenome IPS01_N_Fibroblast_smRNA-Seq data generated from KEP study

  A IPS01_N_Fibroblast_smRNA-Seq single end data for iPSC(Oct4)

  Dataset EGAD00001003501

• Reference epigenome SMC09 mRNA-Seq data generated from KEP study

  SMC09 mRNA-Seq paired end data

  Dataset EGAD00001007176

• Reference epigenome SMC05 mRNA-Seq data generated from KEP study

  SMC05 mRNA-Seq paired end data

  Dataset EGAD00001007173

• Reference epigenome SMC08 mRNA-Seq data generated from KEP study

  SMC08 mRNA-Seq paired end data

  Dataset EGAD00001007175

• Reference epigenome ADMSC05 mRNA-Seq data generated from KEP study

  ADMSC05 mRNA-Seq paired end data

  Dataset EGAD00001007153