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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• Characterization of Human Transcriptome by Computational and HTS Approaches

  We have deeply sequenced total RNA and whole exome of six different post-mortem human snap-frozen tissues (brain, liver, lung, striated muscle, kidney, heart) from three unrelated healthy Caucasian individuals (males, aged 47-54) with the aim to characterize post-transcriptional events due to alternative splicing and RNA editing. In addition, to facilitate the detection of RNA editing sites we have also resequenced the entire genome of each individual. Data are also used to investigate tissue-specific abundance of mitochondrial DNA from exomes and its correlation with mitochondrial transcription, mass and respiratory activity. Tissues were obtained from Cureline (South San Francisco, CA, USA). DNA and RNA were purified using the DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany) and the RNeasy Plus Mini Kit (Qiagen, Hilden, Germany), respectively. Exome capture was performed using the TruSeq Exome Enrichment Kit (Illumina, San Diego, CA) and the sequencing was carried out at IGA Technology Services in Udine (Italy) on Illumina HiSeq 2000 (generating for each tissue approximately 40 millions of 100bp paired-end reads). Strand-oriented RNA reads (2x100bp) were generated using the TruSeq Stranded Total RNA Sample Prep Kit (Illumina, San Diego, CA, USA) and sequenced at IGA Technology Services in Udine (Italy) on Illumina HiSeq 2000 platform. Whole genome resequencing was performed at Personal Genomics in Italy on an Illumina HiSeq 2000 platform (2x100bp) with a mean coverage of 30X. MiRNA-Seq was instead performed at IBBE Institute (Italy) using the TruSeq Small RNA Sample Prep Kit on Illumina MiSeq platform. Preliminary bioinformatics analyses have been performed to check quality using FASTQC software and inconsistent read regions have been trimmed using trim_galore. All reads were mapped onto the human reference genome (version hg19) using GSNAP.

  Study phs000870

• Discovery and Characterization of Genetic Risk Loci in Sjogren's Syndrome

  The Sjögren's Genetics Network (SGENE) is an international collaboration focused on identifying and understanding the genetic variations that influence Sjögren’s pathology. Collectively, SGENE collaborators from 26 different sites (5 in the United States; 21 in foreign countries) have recruited a large cohort of geographically diverse participants of European ancestry. Recruitment, sample collection, and genotyping were performed at unique SGENE sites in compliance with local institutional review board approval. High-density SNP genotyping data were collated by the Oklahoma Medical Research Foundation (OMRF) and used to perform a GWAS of Sjögren's of European ancestry. Participants underwent extensive medical assessment for Sjögren's Syndrome using internationally accepted American-European Consensus Group (AECG) classification criteria or the American College of Rheumatology/European League Against Rheumatism classification criteria. Data quality control, GWAS imputation using the Haplotype Reference Consortium, panel version 1.1, and dbGaP preparation/posting were performed at the OMRF. To facilitate research focused on understanding the genetics of Sjögren's syndrome, the Genomic Summary Results from this GWAS are being made available herein using a total of n=3232 Sjögren's cases and n=17,481 controls. A total of n=8962 controls were obtained from our SGENE collaborators or from our previous study (Lessard, et al. Nature Genetics 2013. PMID: 24097067). To achieve adequate case-to-control ratios for the GWAS analyses, population-based control GWAS data were also obtained from dbGaP (phs000428.v2.p2; phs000672.v1.p1; phs000196.v3.p1; phs000187.v1.p1). In accordance with IRB approval, subject consents, and data sharing restrictions, we are also making available the individual-level genotyping data for a subset of Sjögren's cases (n=1,199) that were genotyped at the OMRF.

  Study phs002723

• ChiLDReN/BA: Genetic Studies of Biliary Atresia in the Childhood Liver Disease Research Network

  Biliary atresia (BA) is a progressive necroinflammatory process initially involving the extra-hepatic biliary tree. Little is known about the factors that cause BA or the factors that influence disease progression. A variety of genetic, autoimmune, and environmental influences have been hypothesized to be important. Most studies to date have focused on the neonate and young child with BA, yet the older surviving child with BA can provide important information about genetics as well as natural history.The Childhood Liver Disease Research Network (ChiLDReN) conducts two longitudinal and observational studies that include BA patients: Biliary Atresia Study in Infants and Children (BASIC) and A Prospective Database of Infants with Cholestasis (PROBE). Using samples from participants in these two protocols, we have formed the largest known data set of biliary atresia patients in the world. ChiLDReN is performing a series of genomic analyses, with subsequent analyses being released as subsequent versions of this registration, phs003356. Version 1 concerns PKD1L1.About the PKD1L1 Analysis (phs003356.v1): While multiple factors have been implicated in the perinatal biliary injuries that characterize BA, a definitive etiology has not yet been established. This study is part of a larger project to characterize the largest cohort of pediatric BA patients through whole-exome sequencing (WES). In this analysis, we examine the WES of those with various laterality defects to determine whether genetic factors could be identified to explicate the etiopathogenesis of BA in biliary atresia splenic malformation (BASM) syndrome. We specifically explored variations in the PKD1L1 gene, which recently has been identified in mouse models as playing a role in laterality.

  Study phs003356

• Out with the Java, in with the Python - new EGA data download client unveiled

  We will shortly be deprecating the Java-based download client also known as API V2. The Java client began its life at the back end of 2014, and during its time has seen many developments that have served our users well. During its tenure, the Java client enabled distribution of over 6.5 million files totalling nearly 15 petabytes of data to researchers around the globe. Recent increases in genomic and phenotypic data file sizes, the number of datasets archived in the EGA, the number of users downloading data, and the diversity of download locations has meant that at times the Java client struggled to distribute data efficiently. In response, our team of developers have built a new download client with more robust features and better functionality and reliability. Enter stage left our shiny new API V3.1! The new API V3.1 will replace both API V2 and API V3. The download client for the new API V3.1 is written in Python, and the code is openly available on Github. This Python client makes https calls to the EGA AAI (https://ega.ebi.ac.uk:8443/) and to the EGA Data API (https://ega.ebi.ac.uk:8052), both of which must be accessible from the client location. In addition to general updates to improve performance, the API V3.1 has three new features enhancing the EGA distributions services. First, requested data are now downloaded over secure https connections rather than http, meaning that data can be delivered unencrypted. Instead of separately requesting, downloading, and decrypting data using the Java client, data can now be requested and downloaded using a single command without the need for decrypting. The API V3.1 also automatically verifies each file’s unencrypted md5 after download to ensure the file has downloaded correctly. Second, the new API supports segmenting and automatic resuming. To enable quicker download speeds, the API breaks files into up to four segments and downloads them in parallel. With the new resume feature, downloads - both segmented and continuous stream - will automatically resume if they encounter any errors or the connection is interrupted. These features result in both a faster and more reliable download experience. Finally, the new API V3.1 implements the GA4GH-compliant htsget protocol for supporting requests over genomic ranges. This exciting new feature means that for data files with accompanying index files (e.g. .crai for CRAM files) users can download specific regions of interest rather than the entire file saving both time and storage space. The new features of the Python-based API V3.1 represent significant improvements in security, speed, reliability, and flexibility that are sure to provide a better data download experience. To take full advantage of these features, users must update to Python 3.X. Considering the changes in the data structures with the new API, the upgrade to a new version of the Python client is also mandatory. Updating the client to the latest version can be achieved by running the following command: pip3 install pyega3 -U We are excited for users to try out the new features and take advantage of the multifariously growing EGA distribution services for human biomedical omics data. If you have any questions or feedback, please email the EGA Helpdesk team. We'd love to hear from you!

  Blog new-ega-data-download-client

• OICR PANCREATIC CANCER DATASET 2

  OICR PANCREATIC CANCER DATASET 2

  Dataset EGAD00001000276

• CSER: North Carolina Clinical Genomic Evaluation by Next-Gen Exome Sequencing 2

  The North Carolina Clinical Genomic Evaluation by Next-gen Exome Sequencing, 2 (NCGENES 2) study is part of a larger consortium project investigating the clinical utility, or net benefit of an intervention on patient and family well-being as well as diagnostic efficacy, management planning, and medical outcomes. A clinical trial will be implemented to compare (1) first-line exome sequencing to usual care and (2) participant pre-visit preparation to no pre-visit preparation. The study will use a randomized controlled design, with 2x2 factorial design, coupled with patient-reported outcomes and comprehensive clinical data collection addressing key outcomes, to determine the net impact of diagnostic results and secondary findings.

  Study phs002110

• Splicing signature analysis of RNU2-2 samples

  Dominant pathogenic variants in the snRNA RNU2-2 were recently reported as a cause of neurodevelopmental disorders. In our study we identified additional individuals with dominant variants (n.4G>A and n.35A>G) and we also identified biallelic variants as a novel frequent cause of neurodevelopmental disorder. We obtained blood samples and performed short-term lymhocytes cultures for 19 cases (5 n.4; 5 n.35; 9 biallelic). We generated exome-capture RNA-Seq for these cases that we compared to 49 controls (probands affected from a neurodevelopmental disorder but without RNU2-2 variants). We observed that RNU2-2 variants mainly disturb alternative exon skipping, although affecting different exons.

  Study EGAS50000001410

• WTCCC case-control study for Type 2 Diabetes

  WTCCC genome-wide case-control association study for Type 2 Diabetes (T2D) using the 1958 British Birth Cohort and the UK National Blood Service collections as controls.

  Study EGAS00000000016

• RNAseq data for 6 samples from the DEV cell line

  - Six samples from the DEV cell line: 2 controls, 2 transduced with IL4R WT and 2 transduced with IL4R mutant (I242N) - This DEV cell line is not commercially available and was acquired from a colleague in the Netherlands

  Dataset EGAD00001003908

• Pre-activated anti-viral innate immunity in the upper airways controls early SARS-CoV-2 infection in children

  We investigated 10 female and 14 male SARS-CoV-2 positive children (age range: 0.8 to 18 years). Based on the WHO guidelines, 15 patients were classified as having mild COVID-19, while 7 children were classified as moderate COVID-19 cases. Two children were asymptomatic. 8 female and 10 male SARS-CoV-2 negative children were included as controls (age range: 4 to 16). 12 SARS-CoV-2 positive female and 9 male adults were included ( age range: 27 - 76) together with 13 female and 10 male SARS-CoV-2 negative adult controls (age: 24 - 77). 10 adult COVID-19 patients had mild disease, while 12 had moderate COVID-19. We performed single-cell RNA sequencing experiments.

  Dataset EGAD00001007969