17346 results for "the best way to get coins in fc 26 Navštivte Buyfc26coins.com Společnost známá transparentními transakcemi..CQ2q"

in 16.40 milliseconds.

• Validation_of_AML_Mutational_Screening

  We recently worked-up a pulldown protocol for studying 21 genes recurrently mutated in AML (Study1770). Our manuscript is currently under revision and to address the reviewers' comments we need to validate some mutations by re-sequencing. In this add-on study we will be using PCR followed by MiSeq for this purpose.

  Study EGAS00001000430

• GOSH_Paediatric_Tumour_23P108_WSSS_WGS_Managed_Access

  We have a case of two siblings (twins) who were born with nodular blue rashes over their body. The first twin had a large periorbital subcutaneous mass which was diagnosed as infantile undifferentiated sarcoma. This baby was too systematically unwell for chemotherapy and died at 12 days of age. The second sibling received two cycles of emergency chemotherapy however died due to disease progression and secondary haemorrhage at 2 months of age. Post-mortem normal and tumour samples have been taken. In addition to placental tissue and parental germline DNA, the goal will be to investigate intra-uterine transfer of sarcoma and the phylogenetic relationship of tumours in both twins.

  Study EGAS00001007536

• MorphoITH: A Framework for Deconvolving Intra-Tumor Heterogeneity Using Tissue Morphology

  This study introduces MorphoITH, a framework for deconvolving intra-tumor heterogeneity (ITH) in clear cell renal cell carcinoma (ccRCC) based on tissue morphology from histopathological slides. The study tested MorphoITH's ability to capture different aspects of morphology and to reflect genetic ITH. The data deposited here is derived from a morphology guided multi-regional sequencing dataset used to study the relationship between morphology and genetic evolution. This dataset consists of whole exome sequencing of tumor from multiple sites and adjacent normal samples which were collected from two patients treated in affiliated hospitals of the University of Texas Southwestern Medical Center (UTSW), including Parkland Hospital.

  Study EGAS50000001064

• Submitting array based metadata

  Submitting array based metadata For further information please check our Submission FAQs, submission quickguide as well as submission terms! The submission metadata required for Array-based submission must be submitted using EGA programmatic submission and by completing the Array-based format (AF) template. The guidelines for this workflow are described on this page. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA Array-based-Format (AF) template. You can request a legacy EGA submission account (ega-box-XXXX) by populating this form. Please, allow two business days for our Helpdesk team to contact you after populating this form. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA-Array-based-Format (AF) template. Metadata Model Registering Metadata Use the EGA programmatic submission to register your Study, Samples, Data Access Committee (DAC) and Policy. This online interface enables you to create new and edit existing submissions. Do not use the Submitter Portal to register the metadata objects for array submissions. Please, go to the EGA programmatic submission. Registering Study Go to the EGA programmatic submission Submit an study XML Take a note of your study accession number (EGASXXXXXXXXXXX) To use the study accession number in a publication, we suggest the following format: "Sequence data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX. Further information about EGA can be found on https://ega-archive.org "The European Genome-phenome Archive of human data consented for biomedical research"(https://doi.org/10.1093/nar/gkab1059 ). Registering Samples Go to the EGA programmatic submission Submit a sample XML Save the list of accession numbers. These will be needed for the AF template. Registering Data Access Committee Further information on the role of your DAC. Go to the EGA programmatic submission Submit a DAC XML Take a note of the DAC accession number (EGACXXXXXXXXXXX) Registering Policy Your Data Access Policy provides the terms and conditions of data use. This is also referred to as the Data Access Agreement (DAA). Completion of a DAA by the applicant/s should form part of the application process to the Data Access Committee (DAA) Go to the EGA programmatic submission Submit a Policy XML Take a note of the Policy accession number (EGAPXXXXXXXXXXX) Complete the Array-based format (AF) spreadsheet Once you have completed the registration of your Study, DAC and Policy using the programmatix submission, you must then complete and return the AF spreadsheet The AF spreadsheet consists on four components: Do not use EGA IDs registered using the Submitter Portal. You can easily identify objects registered in the Submitter Portal by their EGA ID pattern: EGA[A-Z]5{10 more digits} (e.g. for a sample EGAN50000002506). For populating an AF spreadsheet, please exclusively use EGA IDs following this specific pattern: EGA[A-Z]0{10 more digits} (e.g. for a sample EGAN00001691542). Tab1) Webin accessions : Provide the accession numbers for your study, DAC and policy. Please, also add your ega-box number. Tab2) Sample & phenotypes: Please, leave this tab blank. Tab3) Dataset: Describe the dataset to be created Tab4) Data files: Define how your data is going to be organised into datasets and packets for distribution (linkage between samples and files). Should further assistance be required after going through the guide below; please do not hesitate to contact the EGA helpdesk Once the AF spreadsheet is populated, please send it to our EGA helpdesk for further validation. AF spreadsheet Should your submission require multiple DAC's or policies, use ' ; ' to separate the accession numbers. Accessions AF spreadsheet: Samples & phenotypes Samples and phenotypes AF spreadsheet: Datasets We suggest that each dataset consists of a common set of data. The example below consists of two datasets, grouped according to shared data type, technology and by case/control. We also like to capture the number of unique samples that make up the dataset and the Data Access Committee (DAC) responsible for providing the named dataset and their policy (EGAP). Datasets AF spreadsheet: Data files What follows is an example of how to map your samples to the array based files added to your upload account (4th tab). Data Files Please, find below some practical examples on how to register the linkage between samples-files Case 1) 1 sample or list of samples in different datasets: Data Files In case you have a list of samples that belong to different datasets, please, repeat the samples accession number/s in the first column and link the sample to the corresponding dataset each time (each row). Each row is one linkage between sample-file-dataset. Case 2) 1 sample links to several files: Data Files In order to add multiple files to one sample you MUST use “ ; “ between filenames. Example: file1.gpg;file2.gpg;file3.gpg In case that you want to add an extra file to the sample (phenotype or .Rdata), please use “Additional files” column. Important note: You MUST upload the encrypted and unencrypted md5sum values of all files uploaded to your submission account using the filename nomenclature (file.gpg, file.md5,file.md5.gpg). Your submission will not be processed without md5values supplied for all files in the CORRECT format. What happens after the submission of a dataset? All datasets affiliated to unreleased studies are automatically placed on hold until the authorised submitted or DAC contact instructs our EGA helpdesk for the study to be released. Finally, your data is archived within our databases and prepared for encrypted distribution upon the request of permitted EGA account holders. We strongly advise you NOT to delete your data until we confirm that your data has been successfully archived.

  Documentation submission/metadata/submission/array

• The Influence of Gut Microbiota on the Speciation and Toxicity of Mercury During Pregnancy

  There are few studies that have characterized maternal gut microbiota and fetal methylmercury exposure, yet microbes likely modulate this relationship. The primary objective of our pilot study was to determine associations between gut microbial taxa and mercury concentrations in multiple biomarkers (stool, hair, and cord blood). Our secondary objective was to determine the contribution of gut microbial mercury methylation to stool methylmercury.

  Study phs000970

• Canadian prostate cancer samples (CPC-GENE) for PanProstate study

  CPC-GENE has sequenced the genomes of 350 intermediate risk prostate cancers to identify genetic signatures that differ in cancers that responded well to treatment compared with those that did not. CPC-GENE also has sequenced multiple regions of prostate cancer from the same gland to determine if and how the genetic make-up of prostate cancer varies within an individual man’s prostate

  Study EGAS00001003037

• ET_Exome

  Agilent whole exome hybridisation capture will be performed on genomic DNA derived from 50 Essential Thrombocythemia Myeloproliferative Disease samples and matched normal DNA from the same patients. Samples will be multiplexed in one lane of Illumina HiSeq. Sequencing to a coverage of at least 30x will be performed and mapped to build 37 of the human reference genome to facilitate the identification of novel cancer genes.

  Study EGAS00001000102

• Tumor-derived cell lines as pharmacogenomic models to predict therapeutic vulnerabilities in hepatocellular carcinoma

  Hepatocellular carcinoma (HCC) is a heterogeneous aggressive malignancy with low efficacy of current therapies at advanced stages. We integrated molecular and pharmacological profiling of a large panel of liver cancer cell lines (LCCL) to assess their clinical relevance as HCC preclinical models and identify new effective therapies and biomarkers of response. We performed multi-omic analysis including whole-exome, RNA and miRNA sequencing and quantification of 126 proteins in a series 34 LCCL coupled with a screening of 29 anti-cancer agents. Molecular profiles of LCCL and primary HCC were compared and we searched for molecular features associated with drug response. Our panel of LCCL faithfully recapitulated the most aggressive molecular "proliferation class" of HCC. Genomic alterations of the RAS-MAPK pathway correlated with trametinib sensitivity that was more potent than FGFR4 inhibitors in tumor cells depending on the FGF19/FGFR4 pathway. Moreover, we showed that FGF19 amplification was only functional in tumor cells that retained a hepatocyte differentiation program with expression of FGFR4, KLB and NR1H4 highlighting the complex interplay between the genomic and transcriptomic context in drug response. We also identified drugs/combination that could target efficiently the transcriptomic "progenitor subclass of HCC", as well as specific genetic contexts such as inactivating mutations in TSC1/TSC2 and TP53 associated with higher sensitivity to the mTOR inhibitor rapamycin and the AURKA inhibitor alisertib, respectively. LCCL represent relevant preclinical models for drug-biomarker discovery in HCC and enabled to identify molecular contexts linked with particular therapeutic vulnerabilities that may be useful to stratify patients in future clinical trials

  Study EGAS00001003536

• A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy

  The accurate identification and prioritization of antigenic peptides is crucial for the development of personalized cancer immunotherapies. Publicly available pipelines to predict clinical neoantigens do not allow direct integration of mass spectrometry immunopeptidomics data, which can uncover antigenic peptides derived from various canonical and non-canonical sources. To address this, we present an end-to-end clinical proteogenomic pipeline, called NeoDisc, that combines state-of-the-art publicly available and in-house software for immunopeptidomics, genomics and transcriptomics with in-silico tools for the identification, prediction, and prioritization of tumor-specific and immunogenic antigens from multiple sources, including neoantigens, viral antigens and high-confidence tumor-specific antigens.

  Study EGAS50000000228

• Premalignant SOX2 overexpression in the fallopian tubes of ovarian cancer patients: Discovery and validation studies

  To identify early driver mutations in ovarian cancer cells, we used dense whole genome sequencing of micrometastases and microscopic residual disease collected at three time points from a single patient during treatment for high-grade serous ovarian cancer (HGSOC). Laser capture microdissection of tumour islets was performed on samples prior to chemotherapy, after three cycles of chemotherapy and at recurrence. Long frament read whole genome sequencing was performed and mutations were identified. To our knowledge, this is the first longitudinal study that attempts to investigate spatial and temporal micro-heterogeneity in cancer.

  Study EGAS00001001909