13926 results for "Cheap fc 26 coins Buyfc26coins.com is EA Sports official for FC 26 coins Great price for such fast service..NvmJ"

in 23.25 milliseconds.

• CTCF-dependent enhancer hijacking by the EVI1 oncogene in leukemia

  Chromosomal rearrangements are a frequent cause of oncogene deregulation in human malignancies. Overexpression of EVI1 is found in a subgroup of acute myeloid leukemia (AML) with 3q26 chromosomal rearrangements which are often therapy resistant. In a cohort of primary t(3;8)(q26;q24) AML samples we observed the translocation of a MYC super-enhancer to EVI1. We generated a patient-based t(3;8)(q26;q24) model in vitro using CRISPR-Cas9 technology and demonstrated hyper-activation of EVI1 by the hijacked MYC super-enhancer. One MYC super-enhancer element in particular, which recruits early hematopoietic regulators, is critical for EVI1 expression and enhancer-promoter interaction. This interaction is facilitated by a CTCF-bound motif upstream of the EVI1 promoter that acts as an enhancer-docking site in t(3;8) AML. Genomic analyses of 3q26-rearranged AML samples point to a common mechanism by which EVI1 uses this CTCF-bound enhancer-docking site to hijack early hematopoietic enhancers.

  Study EGAS00001004808

• CSER: South-Seq: DNA Sequencing for Newborn Nurseries in the South

  SouthSeq, part of the Clinical Sequencing Evidence-Generating Research (CSER2) effort, aims to perform genome sequencing for infants in nurseries at hospitals in Alabama, Mississippi, Louisiana, and Kentucky. SouthSeq aims to sequence 600 newborn babies presenting with congenital anomalies, or other signs of a rare genetic disorder. The primary goals of SouthSeq are to provide early diagnoses to children with rare genetic conditions, to demonstrate the utility of genome sequencing as a frontline test with potential to improve intermediate and long-term clinical outcomes, and to equip non-genetics providers to deliver test results in order to facilitate the conduction of genome sequencing outside of major medical centers where patients may not have access to a genetics provider. SouthSeq also aims to ensure that genomic testing is applied across diverse communities; recruitment efforts focus on patients from rural and minority populations that have been historically under-represented in both medicine and genomics research. Sequencing and analysis for SouthSeq are being conducted at the HudsonAlpha Institute for Biotechnology in Huntsville, Alabama (https://www.hudsonalpha.org/). Enrollment of infants with signs of genetic disease is approved at five clinical sites, including nurseries at the University of Alabama at Birmingham, the University of Mississippi Medical Center, Woman's Hospital in Baton Rouge, Children's Hospital in New Orleans, and the University of Louisville. Both biological parents are enrolled when available so that follow-up testing can be performed to determine inheritance. Further, participant families may choose to opt-into return of secondary findings identified in the proband, which focus on pathogenic and likely pathogenic variation identified within ACMG SFv3.0 genes.

  Study phs002307

• 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

• Deep single-cell RNA sequencing data for 12346 T cells from tumour, adjacent normal tissue and peripheral blood of treatment-naive NSCLC patients

  Cancer immunotherapies have shown sustained clinical responses in treating non-small cell lung cancer (NSCLC), but efficacy varies between patients and is believed to depend in part on the amount and properties of tumor infiltrating lymphocytes (TILs). To comprehensively depict and dissect the baseline landscape of the composition, lineage and functional states of TILs in lung cancer, here we generated deep single-cell RNA sequencing data for 12,346 T cells from the primary tumour, adjacent normal tissues and peripheral blood of 14 treatment-naive NSCLC patients. Combined expression and TCR-based lineage tracking revealed a significant proportion of effector T cells with common origins and similar functional states across peripheral blood and tumours pointing towards a highly migratory nature of these T cells. We also observed tumour-infiltrating CD8+ T cells undergoing extensive clonal expansion and exhaustion, with two clusters of cells exhibiting states preceding exhaustion. Survival analysis on independent datasets suggested that high ratio of pre-exhausted to exhausted T cells is associated with better prognosis of lung adenocarcinoma (LUAD). In addition, we observed further heterogeneity within the tumour regulatory T cells (Tregs), characterized by the bimodal distribution of TNFRSF9, an activation marker for antigen-specific Tregs. The gene signature of this group of activated tumour Tregs, which included IL1R2, correlated with poor prognosis in LUAD. The T cell clusters revealed by our single cell analyses provide a new approach for patient stratification, and the accompanying compendium of data will help the research community to gain further insight into the functional states and dynamics of T cell responses in lung cancer.

  Study EGAS00001002430

• A reference map of potential determinants for the human serum metabolome

  The collection of metabolites circulating in the human blood, termed the serum metabolome, contains a plethora of biomarkers and causative agents. Although the origin of specific compounds is known, we have a poor understanding of the key determinants of most metabolites. Here, we measured the levels of 1251 circulating metabolites in serum samples from a healthy human cohort of 491 individuals, and devised machine learning algorithms to predict their levels in held-out subjects based on a comprehensive profile consisting of host genetics, gut microbiome, clinical parameters, diet, lifestyle, anthropometric measurements and medication data. Notably, we obtained statistically significant predictions for over 76% of the profiled metabolites. Despite using the strict out-of-sample prediction metric, which is a lower bound for the explained variance, diet and microbiome each explained hundreds of metabolites, with over 50% of the variance explained in some metabolites. We further validated the robustness of the microbiome related associations by showing a high replication rate in two geographically independent cohorts that were not available to us when developing the algorithms. We also demonstrate that some of these interactions are causal, as some metabolites we predicted to be positively associated with bread increased in level following a randomized clinical trial of bread intervention. Microbiome-explained metabolites were enriched with unnamed metabolites, and we devised an algorithm that accurately predicts their biological pathway, finding that they mainly include food components, aromatic amino acids and secondary bile acid derivatives. Overall, our results unravel potential determinants of over 800 metabolites, paving the way towards mechanistic understanding of alterations in metabolites under different conditions and to designing interventions for manipulating circulating metabolite levels.

  Study EGAS00001004512

• Genotype data for 4607 Greenlandic samples (MEGA array)

  This file fileset has 4607 Greenlanders scored on the Illumina MEGA array (1,622,813 sites), and has been put on the plus strand. The data is in PLINK bed/bim/fam format. The Greenlandic individuals originate from two population surveys, B99 and IHIT.

  Dataset EGAD00010002057

• MethylBERT enables read-level DNA methylation pattern identification and tumour deconvolution using a Transformer-based model

  This is the DAC for the study "MethylBERT enables read-level DNA methylation pattern identification and tumour deconvolution using a Transformer-based model" of Pavlo Lutsik (DKFZ B370/KU Leuven, pavlo.lutsik@kuleuven.be) and Clarissa Gerhauser (DKFZ B370, c.gernauser@dkfz.de)

  Dac EGAC50000000497

• RNA-Seq dataset for malignant pleural and peritoneal mesothelioma

  This dataset contains RNA sequencing data of pleural and peritoneal mesothelioma. The number of samples is 18. Sequencing was performed on Illumina NovaSeq 6000 and Illumina NovaSeqXPlus. The sequencing was always paired.

  Dataset EGAD50000001342

• Blood plasma and paired genomic DNA from neuroblastoma patients

  The dataset is comprised of 3 blood plasma samples (Patient 1-3_plasma_cfDNA) paired with genomic DNA (Patient 1-3_tumor gDNA) from the corresponding primary neuroblastoma from three patients. For all these samples whole-exome sequencing (WES) data have been generated.

  Dataset EGAD00001006012

• Benchmarking CRISPR Whole-genome Drop-out Screen - B&S (2019-08-07)

  This study is a benchmarking exercise to explore potential source of variation between different CRISPR drop out libraries. . This dataset contains all the data available for this study on 2019-08-07.

  Dataset EGAD00001005233