2965 results for "愛の戦士完全破壊2-re-imagination-version-鞭首絞め・電撃・ヘソか"

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• chr5_allsamples_imputated

  Chromosome 5 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002536

• chr10_allsamples_imputated

  Chromosome 10 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002530

• chr21_allsamples_imputated

  Chromosome 21 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002532

• chr16_allsamples_imputated

  Chromosome 16 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002534

• chr20_allsamples_imputated

  Chromosome 20 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002537

• Transcriptome of (peripheral blood), from donor Sample A, replicate 2 Freezing replicates

  Freezing replicates

  Dataset EGAD00001010085

• Transcriptome of (peripheral blood), from donor Sample A, replicate 2 Technical replicates

  Technical replicates

  Dataset EGAD00001010083

• chr9_allsamples_imputated

  Chromosome 9 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002535

• chr12_allsamples_imputated

  Chromosome 12 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002517

• chr22_allsamples_imputated

  Chromosome 22 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002518

• chr8_allsamples_imputated

  Chromosome 8 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002519

• chr18_allsamples_imputated

  Chromosome 18 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002520

• chr3_allsamples_imputated

  Chromosome 3 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002521

• chr19_allsamples_imputated

  Chromosome 19 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002524

• chr1_allsamples_imputated

  Chromosome 1 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002525

• sc-RNA-Seq soft tissue sarcoma tumoroid biobank

  Dataset EGAD00001009002

• HipSci - Embryonic Stem Cells - Exome Sequencing - April 2016

  HipSci - Embryonic Stem Cells - Exome Sequencing - April 2016

  Dataset EGAD00001001999

• Illumina_WXS_CONT

  Whole-exome sequencing on Illumina HiSeq2000/2500 of normal colon control tissue

  Dataset EGAD00001002094

• Genetic Factors associated with Conversion from Active Surveillance to Treatment for Prostate Cancer

  This is a genome-wide association study (GWAS) of conversion from active surveillance (AS) for prostate cancer (PC) to treatment. AS is the monitoring with screening tests and/or biopsies over time of men usually with low-risk, early-stage PC to avoid harmful side effects of PC treatment. "Conversion" refers to patients who withdraw from AS (switch to active treatment) due to changes in their cancer development, anxiety about delayed treatment, or other reasons. Patients who converted because of anxiety alone were censored in the GWAS analysis. The overall goal of this study is to develop an easily measurable, germline-based panel of genetic variants to aid in selecting PC patients appropriate for AS and/or the intensity of monitoring. In total, 6,324 PC patients from 28 collaborating institutions are included in this study. Patients are those with PC who are either on AS or who have previously converted from AS to treatment. In this study, 70% of all subjects were diagnosed with very low-risk, low-risk, or low-volume intermediate-risk PC (Kryvenko and Epstein, 2016; PMID: 26709152) and have been enrolled in an AS program. DNA was extracted from blood, saliva, or formalin-fixed, paraffin-embedded (FFPE) tissue collected from the study subjects at the participating sites. Genotyping, quality control processes, and imputation were performed at the Center for Inherited Disease Research (CIDR) at Johns Hopkins University. CIDR used the Illumina Infinium Multi-Ethnic Global Array (MEGA) version 1, which included genome-wide tag SNPs and 10,000 SNPs selected with custom content. Quality control and assurance (QC/QA) checks on genotyped data involved four domains: sample-level checks, batch effects, SNP-level checks, and preliminary association tests. Samples and SNPs passing the QC/QA checks were imputed to appropriate reference panels (HRC r1.1 for patients of European ancestry) using the Michigan Imputation Server (PMID: 27571263). Unmeasured genetic variants were imputed using the Trans-Omics for Precision Medicine (TOPMed) Imputation Server, with 97,256 reference samples and 308,107,085 SNPs. Variants with imputation quality (INFO) score < 0.3 were excluded, leaving a total of 22,691,641 SNPs successfully imputed. After QC steps, a total of 5,936 samples genotyped at CIDR remained for inclusion in the GWAS.

  Study phs002056

• The Effect of the Menstrual Cycle on DNA Expression in the Normal Human Breast Epithelium

  The Susan G. Komen for the Cure® Tissue Bank at the IU Simon Cancer Center [www.komentissuebank.iu.edu] (KTB) was established expressly for the prospective collection of normal, healthy breast tissue from volunteer donors. Blood is also obtained from donors at the time of donation and is processed for serum, plasma and peripheral blood leukocyte DNA. Specimens are annotated and data includes age, race, ethnicity, personal health history, family cancer history, medication usage at the time of donation, and breast cancer risk factors. Premenopausal donors to the KTB were identified by a query of the Bank's database. Hematoxylin and eosin stained sections of the formalin-fixed paraffin-embedded tissue of the identified donors were reviewed and tissue was graded on the basis of the abundance of epithelium within the section. Only tissue containing abundant epithelium was considered for this study. Based on dates, the specimens of nine women in the follicular phase of the menstrual cycle and five in the luteal phase were chosen. Six donors using hormonal contraception at the time of donation were also included. Whole blood obtained from 19 of the 20 donors at the time of tissue donation was available and it was processed for serum. Estradiol, estriol, luteinizing hormone and progesterone concentrations were determined by the Indiana University Health Pathology Laboratory using a Beckman Unicel DxI 800 Immunoassay System. The phase of the menstrual cycle was verified by serum progesterone concentration. The epithelium of these 20 specimens was microdissected from multiple 8 micron frozen tissue sections. Total RNA extracted from the tissue was subsequently depleted of rRNA via locked nucleic acid probes. This enabled profiling of both poly-A and non-poly-A RNA species. Barcoded cDNA libraries from the 20 normal breast epithelia were prepared and sequenced on an Applied Biosystems (AB) SOLiD3 or SOLiD 4 platform. RNA-seq reads for each sample were then mapped to the human genome (hg19) using the LifeScope software version 2.5.1 (Life Technologies, Foster City, CA) and BAM (Binary Alignment/Map) files generated. Read counts for each gene were derived from the output BAM files using the RefSeq database (UCSC Genome Brower) as the gene model.

  Study phs000644

• Kids First: The Genomic Basis of Structural Birth Defects Associated with Chromosome 18 Copy Number Changes

  Chromosome abnormalities are a common cause of structural birth defects. Both childhood cancers and structural birth defects are critical and costly conditions associated with substantial morbidity and mortality. Elucidating the underlying genetic etiology of these diseases has the potential to profoundly improve preventative measures, diagnostics, and therapeutic interventions.However, because these genomic copy number changes involve multiple genes and because most of these conditions are individually rare, defining the specific causative genes behind specific phenotypes has been a challenge. We met that challenge by spending the last 28 years enrolling and evaluating anyone with a chromosome 18 abnormality. The cohort now includes over 700 individuals with a wide variety of chromosome 18 copy number changes (CNV), as well as their parents. Because the vast majority of these participants have individually unique CNVs, we have been able to perform extensive genotype-phenotype correlations resulting in 58 publications. Of the 263 genes on chromosome 18, 28 are linked to specific hemizygous phenotypes. However, most have low penetrance. Genomic sequence data could identify variants in the extant allele that are hypermorphic and compensate for hemizygosity, or are hypomorphic and exacerbate hemizygosity. Additionally, there are phenotypes within this cohort that are rare, or an extreme version of one of the more common phenotypes. Genomic sequence data could help discover new biallelic conditions by revealing functional sequence variants of the extant allele. In both of these cases, there could also be variants in other genes on other chromosomes that may be associated with the phenotype that confer susceptibility or resilience. Inclusion of this unique cohort in the Gabriella Miller Kids First Pediatric Research Program can advance the goals of the program in several ways. First, by adding known susceptibility loci to the existing Kids First cohorts for the structural birth defects that are also found in our cohort. Second, for those structural birth defects known to be polygenetic, this cohort has a single defined risk factor which can simplify the search for secondary factors. Third, these studies can also bring clarity to people with chromosome 18 conditions and help to make the genotype more accurately predict phenotype. This approach could be a model for understanding the many other rare chromosome abnormalities which collectively are a common cause of structural birth defects.

  Study phs002627

• All you need to know about the new Submitter Portal

  In September we launched new services for all EGA users, including a new version of the former Submitter Portal. Our main objective with this Portal is to transform the metadata submission in a simplified and user-friendly method. Although it is possible to find documentation about the Submitter Portal on our website, we wanted to highlight the new features in this article. Enjoy! Credentials: who needs to create a new user to access the Submitter Portal and who doesn't? Users with an old submission account (ega-box) can already access the new Submitter Portal. If it is the first time that you want to access, it is time to request a submitter role linked to the EGA User. In the form, it is mandatory to explain which type of data you want to deposit, the size of your submission, add any comment you’d like us to read, and manage the EGA Data Processing Agreement (DPA) document. This document should be checked by the legal department of your institution and signed by a person with authority to sign contracts on behalf of the institution. Note that we provide a DPA template for new Users; it can be found and downloaded in the Submitter Request section. With the DPA signed and the Submitter Request sent, it is time to wait for our Helpdesk team to validate the request. Metadata submission now can be collaborative It is possible to add collaborator(s) to your submissions. Only registered EGA Users will appear on the search bar. When adding a collaborator, it is mandatory to define the permission granted: Read only: collaborators with this permission can check if the information of the submission is correct but cannot modify anything. Read & write: collaborators with this label can register new objects and modify the registered ones. We wanted to save time for submitters that usually rely on the same collaborators. For that, we have implemented the possibility of adding the same collaborators from previous submissions; all you need to do is to search the submission, and the exact set of EGA users will have access to your submission. You can check the collaborators and their permissions at any point of the submission by going to the collaborators section. Even adding more people to complete the submission is allowed by following the same steps: looking for the EGA User and defining the permission granted (please, note that during the submission it is not possible to add collaborators from previous submissions). Use external links to complement the study information When filling the information about the study we recommend users to enrich the submission with complementary fields such as PubMeds IDs, custom tags and external links. External links are used to connect a study to an external repository. We have implemented an external link to Euro-Bioimaging for a European Project (EuCanImage). Now, we are looking forward to keeping increasing the list of external repositories! Choose the expected release date for your metadata to be searchable on our website Once all the metadata objects are registered it is time to finalise the submission so that the Helpdesk team can approve it. In the new version of the Submitter Portal users can choose the expected release date for the metadata to be searchable on the EGA website. Please, note that the selected date will have a minimum of one week margin from the day of finalisation. This time is necessary for your files to be archived and for the submission to be validated by the Helpdesk Team. Are you looking for the Array submission within the Submitter Portal? Our Submitter Portal does not support array submissions. The submission of Array-based metadata must be done using the EGA programmatic submission. You will find our Array-based format template and all the necessary information on our website, and you can find all the steps in our submission quickguide! More information about the submission process at the EGA Our Team has developed an extended documentation related to metadata submission. A good first step is to take a closer look at the Submission FAQ. To have a general overview of all the metadata submission process we recommend taking a glimpse of our take-the-tour or watching the tutorial available on our YouTube Channel. Don't miss our section on EGA Schemas, to learn more about how the EGA is built. It can be useful to fully ensure that your submissions comply with the EGA's standards and contribute to a valuable and accessible genomic resource.

  Blog new-submitter-portal

• Study2_FiberBlend

  SOMAscan plasma proteome datasets generated from participants consuming the fiber blend snack prototype (study 2)

  Dataset EGAD00010002132

• Dataset that contains analyses of submission 6

  This is the description of analyses dataset of submission 6

  Dataset EGAD50000001107

• RCIDIBAPS001

  DAC created by the Research Computing platform of IDIBAPS for the submission of 2 WES (Whole exome sequencing) samples provided by Sergi Castellví-Bel

  Dac EGAC50000000466