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bulk whole blood transcriptomics data illumina hiseq2000  (Illumina Inc)


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    Structured Review

    Illumina Inc bulk whole blood transcriptomics data illumina hiseq2000
    A. Superscores plot based on multi-omics (metabolomics, proteomics, and <t>transcriptomics)</t> pathways across four latent variables. B. Omics view importances across latent variables. Values represent mean and SEM across 100 bootstrap samples. C. Top five pathways per omics block. D. Top 15 pathways across omics blocks categorised by Reactome parent pathway. E. kPCA ssPA scores from top 15 pathways used to cluster samples using Euclidean distance and Ward linkage. F. Heatmap showing Spearman correlation between superscores across four latent variables and clinical metadata. Asterisks indicate Bonferroni p-value ≤ 0.05. Definitions of clinical variables are in Table B in .
    Bulk Whole Blood Transcriptomics Data Illumina Hiseq2000, supplied by Illumina 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/bulk+transcriptomics+data/sequencing+platform/pmc10994553-242-19-24
    Average 90 stars, based on 1 article reviews
    bulk whole blood transcriptomics data illumina hiseq2000 - by Bioz Stars, 2026-11
    90/100 stars

    Images

    1) Product Images from "PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration"

    Article Title: PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration

    Journal: PLOS Computational Biology

    doi: 10.1371/journal.pcbi.1011814

    A. Superscores plot based on multi-omics (metabolomics, proteomics, and transcriptomics) pathways across four latent variables. B. Omics view importances across latent variables. Values represent mean and SEM across 100 bootstrap samples. C. Top five pathways per omics block. D. Top 15 pathways across omics blocks categorised by Reactome parent pathway. E. kPCA ssPA scores from top 15 pathways used to cluster samples using Euclidean distance and Ward linkage. F. Heatmap showing Spearman correlation between superscores across four latent variables and clinical metadata. Asterisks indicate Bonferroni p-value ≤ 0.05. Definitions of clinical variables are in Table B in .
    Figure Legend Snippet: A. Superscores plot based on multi-omics (metabolomics, proteomics, and transcriptomics) pathways across four latent variables. B. Omics view importances across latent variables. Values represent mean and SEM across 100 bootstrap samples. C. Top five pathways per omics block. D. Top 15 pathways across omics blocks categorised by Reactome parent pathway. E. kPCA ssPA scores from top 15 pathways used to cluster samples using Euclidean distance and Ward linkage. F. Heatmap showing Spearman correlation between superscores across four latent variables and clinical metadata. Asterisks indicate Bonferroni p-value ≤ 0.05. Definitions of clinical variables are in Table B in .

    Techniques Used: Biomarker Discovery, Blocking Assay

    Number of Reactome/KEGG pathways accessible in COPDgene and COVID-19 multi-omics datasets.
    Figure Legend Snippet: Number of Reactome/KEGG pathways accessible in COPDgene and COVID-19 multi-omics datasets.

    Techniques Used: Biomarker Discovery

    Performance comparison of PathIntegrate Multi-View using pathways versus using the molecular-level COPDgene dataset (mean AUC and 95% CI, as well as the number of latent variables (LV) used). In both pathway and molecular-level scenarios the model was used to predict binary COPD status. The molecular-level model was fit both with all molecules available in the datasets, as well as only those mapping to pathways. AUC values are averaged across 5-times repeated 5-fold cross validation.
    Figure Legend Snippet: Performance comparison of PathIntegrate Multi-View using pathways versus using the molecular-level COPDgene dataset (mean AUC and 95% CI, as well as the number of latent variables (LV) used). In both pathway and molecular-level scenarios the model was used to predict binary COPD status. The molecular-level model was fit both with all molecules available in the datasets, as well as only those mapping to pathways. AUC values are averaged across 5-times repeated 5-fold cross validation.

    Techniques Used: Comparison, Biomarker Discovery

    Related Articles

    Generated:

    Article Title: Intensity extraction for feature values in base calling
    Article Snippet: .. The plot and the Table in FIG. 15 were generated based on sequencing data from a new sequencing platform under development by Illumina, Inc. (San Diego, Calif.). ..

    Sequencing:

    Article Title: Intensity extraction for feature values in base calling
    Article Snippet: .. The plot and the Table in FIG. 15 were generated based on sequencing data from a new sequencing platform under development by Illumina, Inc. (San Diego, Calif.). ..

    Article Title: Double-stranded splint adaptors with universal long splint strands and methods of use
    Article Snippet: For example, the two flanking single-stranded regions (e.g., (320) and (330)) can comprise universal binding sequences (or complementary sequences thereof) for P5 and P7 sequences which bind to surface primers immobilized on a support (e.g., flow cell), where P5 and P7 sequences are typically used to construct library molecules for an Illumina sequencing platform. .. The second splint strand (400) can include at least one new universal adaptor sequence (e.g., a new surface primer sequence) that is not found on an Illumina sequencing platform, thereby permitting use of the covalently closed circular molecule (600) on a non-Illumina sequencing platform. ..

    Article Title: Methods for profiling and quantitating cell-free RNA
    Article Snippet: The cDNA was fragmented using DNase I and labeled with Biotin, following by hybridization to Affymetrix GeneChip ST 1.0 microarrays. .. The Illumina sequencing platform and standard Illumina library preparation protocols were used for sequencing. ..

    other:

    Article Title: An adeno-associated virus gene therapy strategy for anti-obesity treatment by nanocarrier-based delivery systems
    Article Snippet: Sequencing was performed with Illumina (HiSeq 2000) according to the manufacturer’s instructions.

    Article Title: Off-target sequence variations driven by the intrinsic properties of the Cas–sgRNA–DNA complex in genome editing
    Article Snippet: Because the error rate for Illumina sequencing platforms is approximately 0.1% [ ], relatively stringent threshold values were applied so that unexpectedly low-quality sequences would be excluded.

    Article Title: An innovative compound that promotes oral wound healing via mobilizing gingival mesenchymal stem cell homing
    Article Snippet: The purified PCR products were then ligated with sequencing adapters to construct sequencing libraries, and sequencing was performed on an Illumina platform(Illumina, CA, USA).

    Functional Assay:

    Article Title: Systems and methods for nucleic acid preparation
    Article Snippet: .. Although functional segments 364A and 364B are shown as P5/P7 sequences, which can be used for attachment in flow cells of an Illumina Hiseq or Miseq system, other type of sequences can be included in functional segments 364A and 364B as well. ..



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    The expression, functions, and characteristics of the FT-elevated transcriptome. (A) Bulk transcriptomics data of FT tissue were used to select genes with different specificity to FT based on RNA categories retrieved from the HPA. In total, 310 genes were elevated in FT and antibodies targeting 133 proteins were identified for further analysis in the current study based on stringent antibody validation criteria. (B) RNA specificity for the 310 genes elevated in FT and the tissue and cell types with the highest expression of each gene are presented in an alluvial plot (C) GO enrichment analysis for all FT-elevated genes. GO term enrichment was assessed using an over-representation test with Benjamini–Hochberg correction for multiple testing. Terms with adjusted p-values < 0.01 were considered significant.

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    Article Title: A high-resolution spatial map of cilia-associated proteins in the human fallopian tube

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    Figure Lengend Snippet: The expression, functions, and characteristics of the FT-elevated transcriptome. (A) Bulk transcriptomics data of FT tissue were used to select genes with different specificity to FT based on RNA categories retrieved from the HPA. In total, 310 genes were elevated in FT and antibodies targeting 133 proteins were identified for further analysis in the current study based on stringent antibody validation criteria. (B) RNA specificity for the 310 genes elevated in FT and the tissue and cell types with the highest expression of each gene are presented in an alluvial plot (C) GO enrichment analysis for all FT-elevated genes. GO term enrichment was assessed using an over-representation test with Benjamini–Hochberg correction for multiple testing. Terms with adjusted p-values < 0.01 were considered significant.

    Article Snippet: Bulk transcriptomics data, derived from the Human Protein Atlas (HPA) resource , identified 310 genes with an elevated expression in human FT compared to other tissues and organs in the human body, of which a majority were found to be associated with the structure and function of motile cilia.

    Techniques: Expressing, Transcriptomics, Biomarker Discovery

    A. Superscores plot based on multi-omics (metabolomics, proteomics, and transcriptomics) pathways across four latent variables. B. Omics view importances across latent variables. Values represent mean and SEM across 100 bootstrap samples. C. Top five pathways per omics block. D. Top 15 pathways across omics blocks categorised by Reactome parent pathway. E. kPCA ssPA scores from top 15 pathways used to cluster samples using Euclidean distance and Ward linkage. F. Heatmap showing Spearman correlation between superscores across four latent variables and clinical metadata. Asterisks indicate Bonferroni p-value ≤ 0.05. Definitions of clinical variables are in Table B in .

    Journal: PLOS Computational Biology

    Article Title: PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration

    doi: 10.1371/journal.pcbi.1011814

    Figure Lengend Snippet: A. Superscores plot based on multi-omics (metabolomics, proteomics, and transcriptomics) pathways across four latent variables. B. Omics view importances across latent variables. Values represent mean and SEM across 100 bootstrap samples. C. Top five pathways per omics block. D. Top 15 pathways across omics blocks categorised by Reactome parent pathway. E. kPCA ssPA scores from top 15 pathways used to cluster samples using Euclidean distance and Ward linkage. F. Heatmap showing Spearman correlation between superscores across four latent variables and clinical metadata. Asterisks indicate Bonferroni p-value ≤ 0.05. Definitions of clinical variables are in Table B in .

    Article Snippet: We integrated COPDgene Phase 2 (~5 years after baseline) plasma metabolomics (Metabolon UHPLC-MS/MS), plasma proteomics (SOMAscan 1.3k assay), and bulk whole blood transcriptomics data (Illumina HiSeq2000) from 522 samples which had data for all three omics.

    Techniques: Biomarker Discovery, Blocking Assay

    Number of Reactome/KEGG pathways accessible in COPDgene and COVID-19 multi-omics datasets.

    Journal: PLOS Computational Biology

    Article Title: PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration

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    Figure Lengend Snippet: Number of Reactome/KEGG pathways accessible in COPDgene and COVID-19 multi-omics datasets.

    Article Snippet: We integrated COPDgene Phase 2 (~5 years after baseline) plasma metabolomics (Metabolon UHPLC-MS/MS), plasma proteomics (SOMAscan 1.3k assay), and bulk whole blood transcriptomics data (Illumina HiSeq2000) from 522 samples which had data for all three omics.

    Techniques: Biomarker Discovery

    Performance comparison of PathIntegrate Multi-View using pathways versus using the molecular-level COPDgene dataset (mean AUC and 95% CI, as well as the number of latent variables (LV) used). In both pathway and molecular-level scenarios the model was used to predict binary COPD status. The molecular-level model was fit both with all molecules available in the datasets, as well as only those mapping to pathways. AUC values are averaged across 5-times repeated 5-fold cross validation.

    Journal: PLOS Computational Biology

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    doi: 10.1371/journal.pcbi.1011814

    Figure Lengend Snippet: Performance comparison of PathIntegrate Multi-View using pathways versus using the molecular-level COPDgene dataset (mean AUC and 95% CI, as well as the number of latent variables (LV) used). In both pathway and molecular-level scenarios the model was used to predict binary COPD status. The molecular-level model was fit both with all molecules available in the datasets, as well as only those mapping to pathways. AUC values are averaged across 5-times repeated 5-fold cross validation.

    Article Snippet: We integrated COPDgene Phase 2 (~5 years after baseline) plasma metabolomics (Metabolon UHPLC-MS/MS), plasma proteomics (SOMAscan 1.3k assay), and bulk whole blood transcriptomics data (Illumina HiSeq2000) from 522 samples which had data for all three omics.

    Techniques: Comparison, Biomarker Discovery