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online platforms dnanexus; ukbiobank-rap  (DNAnexus Inc)

 
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    DNAnexus Inc online platforms dnanexus; ukbiobank-rap
    Online Platforms Dnanexus; Ukbiobank Rap, supplied by DNAnexus Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/dnanexus%C2%AE+platform/online+platforms+dnanexus++ukbiobank+rap/pm40080676-25-34-33
    Average 90 stars, based on 1 article reviews
    online platforms dnanexus; ukbiobank-rap - by Bioz Stars, 2026-09
    90/100 stars

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    Article Title: Whole genome sequencing for metastatic mutational burden in extraskeletal myxoid chondrosarcoma
    Article Snippet: The above pipeline was run on Linux-based AWS ec2 instances on DNAnexus® platform.



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    A Results of PheWAS for the TR in HRCT1 . Traits are grouped into physiological categories (x-axis) plotted against the −log 10 Bonferroni-corrected p value per trait ( y -axis). Significant fine-mapped associations are individually labeled and shown as filled red points to indicate negative directionality of effect. P values were calculated using linear and logistic regression implemented in REGENIE, applying a Bonferroni correction based on 9531 independent traits and <t>36,085</t> <t>TRs</t> analyzed. B Relative risk of high blood pressure versus average length of the HRCT1 CCA repeat. Odds ratio per allele is shown by the black dot with vertical lines representing the 95% confidence intervals. Odds ratios are based on analysis of 167,533 individuals with both genotype and phenotype data, with each odds ratio per and CI plotted per allele size calculated from at least 54 individuals. The bar plot above shows the relative frequency of averaged TR alleles in the <t>UKB</t> cohort. C Screenshot from the UCSC Genome Browser showing the location of the poly(CCA) motif within HRCT1 . D Length of the HRCT1 CCA repeat (red dot) is the most strongly associated variant in the region with high blood pressure. P -values were calculated using logistic regression implemented in REGENIE. E Results after conditioning the same SNVs as shown in ( D ) based on average genotype of the HRCT1 repeat. The horizontal dashed line indicates the Bonferroni-significance threshold of p < 1.45 × 10 −10 . P values were calculated using logistic regression implemented in REGENIE. F Fine-mapping analysis of variants in the HRCT1 locus with CAVIAR ranks the poly(CCA) motif within HRCT1 (red dot) as the most likely variant underlying risk of high blood pressure.
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    A Results of PheWAS for the TR in HRCT1 . Traits are grouped into physiological categories (x-axis) plotted against the −log 10 Bonferroni-corrected p value per trait ( y -axis). Significant fine-mapped associations are individually labeled and shown as filled red points to indicate negative directionality of effect. P values were calculated using linear and logistic regression implemented in REGENIE, applying a Bonferroni correction based on 9531 independent traits and <t>36,085</t> <t>TRs</t> analyzed. B Relative risk of high blood pressure versus average length of the HRCT1 CCA repeat. Odds ratio per allele is shown by the black dot with vertical lines representing the 95% confidence intervals. Odds ratios are based on analysis of 167,533 individuals with both genotype and phenotype data, with each odds ratio per and CI plotted per allele size calculated from at least 54 individuals. The bar plot above shows the relative frequency of averaged TR alleles in the <t>UKB</t> cohort. C Screenshot from the UCSC Genome Browser showing the location of the poly(CCA) motif within HRCT1 . D Length of the HRCT1 CCA repeat (red dot) is the most strongly associated variant in the region with high blood pressure. P -values were calculated using logistic regression implemented in REGENIE. E Results after conditioning the same SNVs as shown in ( D ) based on average genotype of the HRCT1 repeat. The horizontal dashed line indicates the Bonferroni-significance threshold of p < 1.45 × 10 −10 . P values were calculated using logistic regression implemented in REGENIE. F Fine-mapping analysis of variants in the HRCT1 locus with CAVIAR ranks the poly(CCA) motif within HRCT1 (red dot) as the most likely variant underlying risk of high blood pressure.
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    A Results of PheWAS for the TR in HRCT1 . Traits are grouped into physiological categories (x-axis) plotted against the −log 10 Bonferroni-corrected p value per trait ( y -axis). Significant fine-mapped associations are individually labeled and shown as filled red points to indicate negative directionality of effect. P values were calculated using linear and logistic regression implemented in REGENIE, applying a Bonferroni correction based on 9531 independent traits and <t>36,085</t> <t>TRs</t> analyzed. B Relative risk of high blood pressure versus average length of the HRCT1 CCA repeat. Odds ratio per allele is shown by the black dot with vertical lines representing the 95% confidence intervals. Odds ratios are based on analysis of 167,533 individuals with both genotype and phenotype data, with each odds ratio per and CI plotted per allele size calculated from at least 54 individuals. The bar plot above shows the relative frequency of averaged TR alleles in the <t>UKB</t> cohort. C Screenshot from the UCSC Genome Browser showing the location of the poly(CCA) motif within HRCT1 . D Length of the HRCT1 CCA repeat (red dot) is the most strongly associated variant in the region with high blood pressure. P -values were calculated using logistic regression implemented in REGENIE. E Results after conditioning the same SNVs as shown in ( D ) based on average genotype of the HRCT1 repeat. The horizontal dashed line indicates the Bonferroni-significance threshold of p < 1.45 × 10 −10 . P values were calculated using logistic regression implemented in REGENIE. F Fine-mapping analysis of variants in the HRCT1 locus with CAVIAR ranks the poly(CCA) motif within HRCT1 (red dot) as the most likely variant underlying risk of high blood pressure.
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    A Results of PheWAS for the TR in HRCT1 . Traits are grouped into physiological categories (x-axis) plotted against the −log 10 Bonferroni-corrected p value per trait ( y -axis). Significant fine-mapped associations are individually labeled and shown as filled red points to indicate negative directionality of effect. P values were calculated using linear and logistic regression implemented in REGENIE, applying a Bonferroni correction based on 9531 independent traits and 36,085 TRs analyzed. B Relative risk of high blood pressure versus average length of the HRCT1 CCA repeat. Odds ratio per allele is shown by the black dot with vertical lines representing the 95% confidence intervals. Odds ratios are based on analysis of 167,533 individuals with both genotype and phenotype data, with each odds ratio per and CI plotted per allele size calculated from at least 54 individuals. The bar plot above shows the relative frequency of averaged TR alleles in the UKB cohort. C Screenshot from the UCSC Genome Browser showing the location of the poly(CCA) motif within HRCT1 . D Length of the HRCT1 CCA repeat (red dot) is the most strongly associated variant in the region with high blood pressure. P -values were calculated using logistic regression implemented in REGENIE. E Results after conditioning the same SNVs as shown in ( D ) based on average genotype of the HRCT1 repeat. The horizontal dashed line indicates the Bonferroni-significance threshold of p < 1.45 × 10 −10 . P values were calculated using logistic regression implemented in REGENIE. F Fine-mapping analysis of variants in the HRCT1 locus with CAVIAR ranks the poly(CCA) motif within HRCT1 (red dot) as the most likely variant underlying risk of high blood pressure.

    Journal: Nature Communications

    Article Title: A phenome-wide association study of tandem repeat variation in 168,554 individuals from the UK Biobank

    doi: 10.1038/s41467-024-54678-0

    Figure Lengend Snippet: A Results of PheWAS for the TR in HRCT1 . Traits are grouped into physiological categories (x-axis) plotted against the −log 10 Bonferroni-corrected p value per trait ( y -axis). Significant fine-mapped associations are individually labeled and shown as filled red points to indicate negative directionality of effect. P values were calculated using linear and logistic regression implemented in REGENIE, applying a Bonferroni correction based on 9531 independent traits and 36,085 TRs analyzed. B Relative risk of high blood pressure versus average length of the HRCT1 CCA repeat. Odds ratio per allele is shown by the black dot with vertical lines representing the 95% confidence intervals. Odds ratios are based on analysis of 167,533 individuals with both genotype and phenotype data, with each odds ratio per and CI plotted per allele size calculated from at least 54 individuals. The bar plot above shows the relative frequency of averaged TR alleles in the UKB cohort. C Screenshot from the UCSC Genome Browser showing the location of the poly(CCA) motif within HRCT1 . D Length of the HRCT1 CCA repeat (red dot) is the most strongly associated variant in the region with high blood pressure. P -values were calculated using logistic regression implemented in REGENIE. E Results after conditioning the same SNVs as shown in ( D ) based on average genotype of the HRCT1 repeat. The horizontal dashed line indicates the Bonferroni-significance threshold of p < 1.45 × 10 −10 . P values were calculated using logistic regression implemented in REGENIE. F Fine-mapping analysis of variants in the HRCT1 locus with CAVIAR ranks the poly(CCA) motif within HRCT1 (red dot) as the most likely variant underlying risk of high blood pressure.

    Article Snippet: Using the UKB DNAnexus Research Analysis Platform, we performed genotyping of 36,085 TRs from the GS data with ExpansionHunter (v5) (Supplementary Data ).

    Techniques: Labeling, Variant Assay

    Examples of cloud types, service models, workflows, and platforms for biomedical applications.

    Journal: PLoS Computational Biology

    Article Title: Cloud computing applications for biomedical science: A perspective

    doi: 10.1371/journal.pcbi.1006144

    Figure Lengend Snippet: Examples of cloud types, service models, workflows, and platforms for biomedical applications.

    Article Snippet: Commercial (AWS, Microsoft Azure) cloud-based platforms (e.g., DNAnexus) enables analyses of massive amounts of sequencing data integrated with phenotypic or clinical information [ ].

    Techniques: Sequencing, Next-Generation Sequencing, Software, Diagnostic Assay, Mutagenesis, Variant Assay, Selection