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• RNA data for Glioblastoma (EGAS00001003953, H016)

  RNA data for human glioblastoma patients, EGAS00001003953. 64 human samples, 2 cell lines (LN229, ZH487).

  Dataset EGAD00001006547

• sWGS for genome-wide copy number profiling

  Shallow whole-genome sequencing (sWGS) data for the identification of somatic copy number alterations (SCNA) and the estimation of tumor fractions in plasma DNA of metastatic colorectal cancer patients (mCRC).

  Dataset EGAD00001006384

• RNA-seq data from paired tumour and germline samples from mesothelioma patients for study EGAS00001005196

  RNA-seq data from paired tumour and germline samples from mesothelioma patients for study EGAS00001005196

  Dataset EGAD00001007874

• Hi-C for Junvenile Pilocytic Astrocytomas

  5 Hi-C datasets (4 JPA and 1 LGG). Hi-C data for the remaining 5 JPAs used in our paper as well as all controls have been uploaded to EGA under EGAS00001005476.

  Dataset EGAD00001009044

• Evaluation of somatic mutations in cervicovaginal samples as a non-invasive method for the detection and molecular classification of endometrial cancer

  Evaluation of somatic mutations in cervicovaginal samples as a non-invasive method for the detection and molecular classification of endometrial cancer

  Dataset EGAD00001011123

• Amplicon Sequencing for 'Early evolutionary branching across spatial domains predisposes to clonal replacement under chemotherapy in neuroblastoma'

  BAMs from deep sequencing using a custom panel for the study 'Early evolutionary branching across spatial domains predisposes to clonal replacement under chemotherapy in neuroblastoma'

  Dataset EGAD00001015012

• Dataset for transcriptome sequencing of forebrain organoids from Lissencephaly patients

  This dataset contains transcriptome sequencing for 16 samples of forebrain organoids from Lissencephaly patients. The sequencing was performed on Illumina NextSeq 6000 . The sequencing was always paired.

  Dataset EGAD00001015688

• National Institute of General Medical Sciences (NIGMS) Human Genetic Cell Repository Human Variation Panels including 100 African-Americans (HD100AA), 100 Caucasians (HD100CAU), 100 Han People of Los Angeles (HD100CHI) and 100 Mexican American Community of Los Angeles (HD100MEX)

  The Human Genetic Cell Repository is sponsored by the National Institute of General Medical Sciences (NIGMS) with the mission of supplying scientists with the materials for accelerating disease gene discovery and functional studies. The resources available include highly-characterized, contaminant-free cell cultures and high quality, well-characterized DNA samples derived from these cultures, both subjected to rigorous quality control. The Repository was established in 1972 at Coriell and contains more than 9,500 cell lines, primarily fibroblasts and transformed lymphoblasts. The Repository has a major emphasis on inherited diseases and chromosomally aberrant cell lines. In addition, it contains a large collection dedicated to understanding human variation that includes samples from populations around the world, the CEPH collection, the Polymorphism Discovery Resource, Human Variation and many apparently healthy controls. The Human Variation collection provides cell lines and DNA samples from a variety of populations. The panels of African-Americans (HD100AA) and Caucasians (100CAU) used for this study are comprised of samples present in the Repository that were originally collected over the years from apparently healthy people to be used as "controls", for example, unaffected family members of persons with identified mono-genetic diseases. The samples for the Han people of Los Angeles (HD100CHI) and the Mexican American Community of Los Angeles (HD100MEX), however, were collected relatively recently from volunteers, identified as member of these communities, specifically for use in these panels. The Coriell Genotyping and Microarray Center in conjunction with the NIGMS repository used the Affymetrix Genome-Wide Human SNP 6.0 platform to genotype 400 samples from the NIGMS human variation panels. The populations genotyped included Americans of African, Caucasian, Mexican, and Han Chinese ancestry. The Affymetrix SNP 6.0 array detects approximately 940,000 SNPs and provides copy number information for more than 900,000 additional locations across the genome.

  Study phs000211

• Molecular Etiology of Early-Onset Dystonia

  Primary torsin dystonias (PTD) are a group of movement disorders characterized by twisting muscle contractures, where dystonia is the only clinical sign (except for tremor) and there is no evidence of neuronal degeneration or an acquired cause. Eleven genes have been mapped for primary dystonia including DYT1 (TOR1A), DYT2, DYT4 (TUBB4A), DYT6 (THAP1), DYT7, DYT13,, DYT17, DYT21, CIZ1, DYT24 (ANO3), and DYT25 (GNAL); however, only 3 genes (TOR1A, THAP1, and GNAL) have mutations in early onset dystonia patients. Each is inherited as an autosomal dominant trait but with reduced penetrance, meaning that individuals can carry the mutation but do not show clinical symptoms. The most common form of PTD is adult onset focal accounting for about 90% of all cases of dystonia with a prevalence estimated at 30/100,000 in the general population. A small percentage of focal cases are due to mutations in CIZ1, ANO3, THAP1, TUBB4A, and GNAL, but the vast majority is unaccounted for and is most likely multigenic or multifactorial. In this grant, focusing on early onset PTD, we will uncover genes that influence the penetrance of DYT1 (TOR1A) dystonia through GWAS and exome sequencing studies. We will identify other genes for early onset PTD using exome sequencing and determine whether variants within the identified early onset PTD genes or the pathways associated with these genes contribute to susceptibility in the most prevalent form of PTD, focal dystonia, using a case:control association study. The proposed research will identify novel PTD risk factors and genes, which in turn should reveal shared and intersecting pathways leading to a better understanding of the molecular basis of PTD and provide the underpinnings for developing new treatments. Additionally, insight into the factors that modulate disease penetrance would be a first step in determine whether these could be modified leading to a reduced incidence of the disease.

  Study phs001733

• Prediction of Trastuzumab Benefit in Adjuvant Breast Cancer: Gene Expression Profiling in NSABP B31

  Background National Surgical Adjuvant Breast and Bowel Project (NSABP) trial B-31 suggested the efficacy of adjuvant trastuzumab, even in HER2-negative breast cancer. This finding prompted us to develop a predictive model for degree of benefit from trastuzumab using archived tumor blocks from B-31. Methods Case subjects with tumor blocks were randomly divided into discovery (n = 588) and confirmation cohorts (n = 991). A predictive model was built from the discovery cohort through gene expression profiling of 462 genes with nCounter assay. A predefined cut point for the predictive model was tested in the confirmation cohort. Gene-by-treatment interaction was tested with Cox models, and correlations between variables were assessed with Spearman correlation. Principal component analysis was performed on the final set of selected genes. All statistical tests were two-sided. Results Eight predictive genes associated with HER2 (ERBB2, c17orf37, GRB7) or ER (ESR1, NAT1, GATA3, CA12, IGF1R) were selected for model building. Three-dimensional subset treatment effect pattern plot using two principal components of these genes was used to identify a subset with no benefit from trastuzumab, characterized by intermediate-level ERBB2 and high-level ESR1 mRNA expression. In the confirmation set, the predefined cut points for this model classified patients into three subsets with differential benefit from trastuzumab with hazard ratios of 1.58 (95% confidence interval [CI] = 0.67 to 3.69; P = .29; n = 100), 0.60 (95% CI = 0.41 to 0.89; P = .01; n = 449), and 0.28 (95% CI = 0.20 to 0.41; P < .001; n = 442; P interaction between the model and trastuzumab < .001). Conclusions We developed a gene expression-based predictive model for degree of benefit from trastuzumab and demostrated that HER2-negative tumors belong to the moderate benefit group, thus providing justification for testing trastuzumab in HER2-negative patients (NSABP B-47). ** Reprinted from J Natl Cancer Inst;2013;105:1782-1788 with permission from Oxford University Press.

  Study phs000826