14663 results for "PLOS+ONE+Pennsylvania+German+ACS+language+code+pdc+open+access+study"

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• The Genomic Map of Polad in Open Access

  The main goal is to prepare and share WGS/WES of Polish population

  Study EGAS50000000110

• PKD1 capture-by-hybridization resequencing for genetic diagnostics of polycystic kidney disease

  We established and validated a sequence capture based NGS testing approach for PKD1. The presence of six PKD1 pseudogenes and tremendous allelic heterogeneity make molecular genetic testing of PKD1 variants challenging. In the publication accompaying this dataset (An efficient and comprehensive strategy for genetic diagnostics of polycystic kidney disease, Eisenberger et.al., PLoS one), we demonstrate that the applied standard mapping algorithm specifically aligns reads to the PKD1 locus and overcomes the complication of unspecific capture of pseudogenes. This dataset contains the raw PKD1 reads of all patients from the publication.

  Dataset EGAD00001001091

• R code

  R code to run analyses on anonymized data from ABACUS for IMvigor010 ctDNA publication.

  Dataset EGAD00001007652

• ML modeling data & code

  Rmarkdown code, PDF, and Rdata file to recapitulate the paper's primary figures and machine learning model development.

  Dataset EGAD00001009764

• Integrative understanding of human immune system by functional genomics and development of intervention strategies for the prevention of autoimmune diseases

  arious peripheral blood immune cell subsets from 5 rheumatoid (RA) donors were collected ("CD16p_Mono", "CL_Mono???, "DN_B", "Fr_II_eTreg???, "mDC???, "Mem_CD4" , "Naive_B" , "Naive_CD4" , ???Neu", "NK" , "pDC" , "Plasmablast", ???SM_B", "Tfh", "Th1", "Th17", "Th2" , "USM_B"). The patients were analyzed again longitudinally after abatacept treatment. RNA-seq was performed with each immune cell subset samples. After gene expression quantification samples were filtered.

  Study JGAS000602

• Integrative understanding of human immune system by functional genomics and development of intervention strategies for the prevention of autoimmune diseases

  Various peripheral blood immune cell subsets from 39 healthy volunteers and 50 rheumatoid (RA) donors were collected ("CD16p_Mono", "CL_Mono???, "DN_B", "Fr_II_eTreg???, "mDC???, "Mem_CD4" , "Naive_B" , "Naive_CD4" , ???Neu", "NK" , "pDC" , "Plasmablast", ???SM_B", "Tfh", "Th1", "Th17", "Th2" , "USM_B"). 15 RA patients were analyzed longitudinally. RNA-seq was performed with each immune cell subset samples. After gene expression quantification samples were filtered.

  Study JGAS000598

• Combining transcription factor binding affinities with open chromatin data for accurate gene expression prediction

  The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid cost and labour intensive TF ChIP-seq assays.It is important to develop reliable and fast computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices.TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq, using either peaks or footprints as input.In addition to open-chromatin data, also Histone-Marks (HMs) can be used in TEPIC to identify candidate TF binding sites.TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength.Using machine learning techniques, we show that incorporating low affinity binding sites improves our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites.Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance.In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq datasets.Finally, we show that these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.

  Study EGAS00001002073

• R code

  R code to run analyses on anonymized data from IMvigor010 ctDNA publication.

  Dataset EGAD00001007654

• Immunogenetics of BCG Vaccination and Pediatric Tuberculosis

  We examined genetic variation associated with BCG-induced innate immune responses and susceptibility to pediatric tuberculosis (TB) disease. South African study participants were enrolled at the South African Tuberculosis Vaccine Initiative (SATVI) as part of a larger BCG vaccination study. This area has one of the highest rates of TB incidence in the world. Infants were vaccinated with BCG at birth, and a nested case-control study was performed over a 2-year prospective observation period, which resulted in 135 infant TB cases and 521 controls. Whole blood was collected from BCG vaccinated infants at 10 weeks of age. Genotyping was performed with the llumina MegaEx Beadchip, and GWAS was performed against TB disease to assess for associations between genetic variants and susceptibility to TB. In addition, a subset of infants were assessed for BCG-induced innate immune responses (N=198). For these, whole blood was stimulated with BCG or media and flow cytometry was used to measure BCG-induced PDL1, CD40, and cytokine expression in myeloid (mDC) and plasmacytoid (pDC) dendritic cells, monocytes, and neutrophils. Cellular GWAS was then performed to associate genetic variants with BCG-induced immune responses.

  Study phs003406

• Myocardial Applied Genomics Network (MAGNet) Study

  The MAGNet repository includes non-failing samples from organ donors with no history of heart failure and failing samples from explanted hearts of donors receiving a heart transplant. The study protocol was approved by the Institutional Review Board at the University of Pennsylvania, and all patients provided written informed consent to participate.

  Study phs001539