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• Genomic-Enabled Medicine for Recurrent Glioblastoma

  In this prospective study, we performed genome-wide tumor/normal exome sequencing and tumor RNA-sequencing for recurrent glioblastoma patients. A subset of patients had both enhancing tumor and non-enhancing tissue sequenced to investigate spatial heterogeneity of this disease. Together, this information builds our understanding of the genomic underpinnings of glioblastoma, and contributes toward the knowledge base for identifying and developing more effective treatments for patients with glioblastoma.

  Study phs001460

• Bayesian-Based Noninvasive Prenatal Diagnosis of Single-Gene Disorders

  In the last decade, non-invasive prenatal diagnosis (NIPD) has emerged as an effective procedure for early detection of inherited diseases during pregnancy. This technique is based on using cell-free DNA (cfDNA) and fetal cfDNA (cffDNA) in maternal blood, and hence, has minimal risk for the mother and fetus compared with invasive techniques. NIPD is used today for identifying chromosomal abnormalities (in some instances) and for single-gene disorders (SGDs) of paternal origin. However, for SGDs of maternal origin, sensitivity poses a challenge that limits the testing to one genetic disorder at a time. Here we present a Bayesian method for the NIPD of monogenic diseases that is independent of the mode of inheritance and parental origin. Furthermore, we show that accounting for differences in the fragment length distribution of fetal- and maternal-derived cfDNA results in increased accuracy. Our model is the first to predict inherited insertions-deletions (indels). The method described can serve as a general framework for the NIPD of SGDs; this will facilitate easy integration of further improvements. One such improvement that is presented in the current study is a machine learning model that corrects errors based on patterns found in previously processed data. Overall, we show that next generation sequencing (NGS) can be used for the NIPD of a wide range of monogenic diseases, simultaneously. We believe that our study will lead to the achievement of a comprehensive NIPD for monogenic diseases. (Reprinted from Bayesian-based noninvasive prenatal diagnosis of single-gene disorders, with permission from Genome Research)

  Study phs001659

• Feasibility study of enzymatic methylation sequencing of cell-free DNA from cerebrospinal fluid of pediatric brain tumors for classification

  Array-based DNA methylation profiling is the gold standard for molecular classification of brain tumors. However, it relies on significant amount of input DNA extracted from surgical tissue, thus limiting its use for tumors where biopsy is challenging or limited in quantity. Cell-free tumor DNA (cfDNA) in cerebrospinal fluid (CSF) presents alternative opportunities for brain tumor diagnosis and disease monitoring following treatment. Novel enzymatic DNA methylation sequencing (EM-seq) methods may allow us to overcome input DNA limitations and accurately quantify methylation from cfDNA for tumor classification.

  Study EGAS50000000871

• Comprehensive assay for the molecular profiling of cancer by target enrichment from formalin-fixed paraffin-embedded specimens

  Tumor molecular profiling is becoming a standard of care for patients with cancer, but the optimal platform for cancer sequencing remains undetermined. We established a comprehensive assay, the Todai OncoPanel (TOP), which consists of DNA and RNA hybridization capture-based next-generation sequencing panels. A novel method for target enrichment, named the junction capture method, was developed for the RNA panel to accurately and cost-effectively detect 365 fusion genes as well as aberrantly spliced transcripts. The TOP RNA panel can also measure the expression profiles of an additional 109 genes. The TOP DNA panel was developed to detect single nucleotide variants and insertions/deletions for 464 genes, to calculate tumor mutation burden and microsatellite instability status, and to infer chromosomal copy number. Clinically relevant somatic mutations were identified in 32.2% (59/183) of patients by prospective TOP testing, signifying the clinical utility of TOP for providing personalized medicine to cancer patients.

  Study JGAS000164

• WGS data for MMML for Study EGAS00001002198

  Whole genome sequencing data for MMML (healthy cell_line)

  Dataset EGAD00001003283

• Kidney Two-Hit Mapping

  Germ-line genetic variants identified thus far generally account for only part of the genetic risk predicted for most cancers. A component of the remaining risk may be explained by functionally consequent genetic variants that are individually very rare (or private to that individual's family). While their identification may be highly informative for cancer etiology and biological knowledge, strategies that allow for agnostic (genome-wide) identification of such genes are not straight forward. This is particularly true for rare cancers where sufficient numbers of patients may simply not be available for appropriate statistically powered studies. In this project we have explored a study design based around the concept of "two-hit mapping" using the co-occurrence of germ-line and somatic mutations in the same gene to assist in the identification of susceptibility genes. The dataset includes 54 kidney cancer patients selected for enrichment of genetic disease, that is patients with: early age of onset and/or family history and/or bilateral disease. Germ-line and somatic (kidney cancer) tumor sequencing was carried out. These data may improve our understanding of kidney cancer.

  Study phs001971

• cfDNA dataset for cfDNA cohort

  Cell free DNA sequencing data for 233 samples across multiple cancer types for cfDNA cohort

  Dataset EGAD50000000104

• BLUEPRINT EpiVar ChIP-seq for naive CD4+ T-cells

  BLUEPRINT EpiVar ChIP-seq for naive CD4+ T-cells

  Study EGAS00001000753

• RNASeq files for Roussel-MBPRG

  RNASeq files for Roussel-MPBRG paper titled "Combination of ribociclib and gemcitabine for the treatment of medulloblastoma"

  Dataset EGAD00001008824

• Alcohol Dependence GWAS in European- and African Americans

  This study includes SSADDA (Semi Structured Assessment for Drug Dependence and Alcoholism) assessed subjects (mostly unrelated, but including some affected sibling pairs) recruited in the course of several substance dependence genetics projects. The sample includes 1889 African-American (AA) subjects and 1020 European-American (EA) subjects. Among the AAs, 1397 meet DSM-IV criteria for alcohol dependence and 491 are controls. Among the EAs, 1010 meet the criteria for alcohol dependence and 9 are controls. (One in each population meets criteria for alcohol abuse and not dependence, and is therefore counted in neither category.) Although alcohol dependence is the major focus, the sample is informative also for cocaine, nicotine, and opioid dependence.

  Study phs000425