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tcga normalized rna-seq gene expression data from 33 cancer types  (Broad Institute Inc)

 
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    Broad Institute Inc tcga normalized rna-seq gene expression data from 33 cancer types
    Tcga Normalized Rna Seq Gene Expression Data From 33 Cancer Types, supplied by Broad Institute 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/rna+seq+data+analysis/tcga+rna+seq+data/pmc06802116-254-8-14
    Average 90 stars, based on 1 article reviews
    tcga normalized rna-seq gene expression data from 33 cancer types - by Bioz Stars, 2026-10
    90/100 stars

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    Related Articles

    Activation Assay:

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database ( https://gdac.broadinstitute.org/ ) and Genomic Data Commons (GDC) data portal ( https://portal.gdc.cancer.gov/projects/TCGABRCA ).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines
    Article Snippet: Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy.
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database (https://gdac. broadinstitute.org/) and Genomic Data Commons (GDC) data portal (https://portal.gdc.cancer.gov/projects/TCGABRCA).

    Article Title: Identification of DDX60 as a Regulator of MHC-I Class Molecules in Colorectal Cancer.
    Article Snippet: The TCGA mRNA-sequencing data of CRC was obtained from the Broad Institute FireBrowse portal [36].

    Article Title: UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples.
    Article Snippet: We downloaded paired RNA-seq, WES and clinical data of 1,020 RCC tumor and adjacent normal samples in TCGA KIPAN from the Genome Data Analysis Center (GDAC) at Broad Institute.

    Article Title: Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR
    Article Snippet: We downloaded TCGA RNA-seq data (gene read counts) from the BROAD Institute TCGA GDAC at firebrowse.org (March 2019).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines.
    Article Snippet: Signaling pathway activation analysis was adapted from Tan et al.6 Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Cell Extrinsic Synaptogyrin 3 Expression as a Diagnostic and Prognostic Biomarker in Head and Neck Cancer
    Article Snippet: The TCGA RNA-seq datasets used in this study were downloaded from The Broad Institute TCGA GDAC Firehose ( gdac.broadinsitue.org ), which provides TCGA Level 3 data and Level 4 analyses packaged in a form amenable to immediate algorithmic analysis.

    RNA Sequencing:

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database ( https://gdac.broadinstitute.org/ ) and Genomic Data Commons (GDC) data portal ( https://portal.gdc.cancer.gov/projects/TCGABRCA ).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines
    Article Snippet: Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy.
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database (https://gdac. broadinstitute.org/) and Genomic Data Commons (GDC) data portal (https://portal.gdc.cancer.gov/projects/TCGABRCA).

    Article Title: Identification of DDX60 as a Regulator of MHC-I Class Molecules in Colorectal Cancer.
    Article Snippet: The TCGA mRNA-sequencing data of CRC was obtained from the Broad Institute FireBrowse portal [36].

    Article Title: UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples.
    Article Snippet: We downloaded paired RNA-seq, WES and clinical data of 1,020 RCC tumor and adjacent normal samples in TCGA KIPAN from the Genome Data Analysis Center (GDAC) at Broad Institute.

    Article Title: Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR
    Article Snippet: We downloaded TCGA RNA-seq data (gene read counts) from the BROAD Institute TCGA GDAC at firebrowse.org (March 2019).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines.
    Article Snippet: Signaling pathway activation analysis was adapted from Tan et al.6 Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Cell Extrinsic Synaptogyrin 3 Expression as a Diagnostic and Prognostic Biomarker in Head and Neck Cancer
    Article Snippet: The TCGA RNA-seq datasets used in this study were downloaded from The Broad Institute TCGA GDAC Firehose ( gdac.broadinsitue.org ), which provides TCGA Level 3 data and Level 4 analyses packaged in a form amenable to immediate algorithmic analysis.

    Activity Assay:

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database ( https://gdac.broadinstitute.org/ ) and Genomic Data Commons (GDC) data portal ( https://portal.gdc.cancer.gov/projects/TCGABRCA ).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines
    Article Snippet: Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy.
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database (https://gdac. broadinstitute.org/) and Genomic Data Commons (GDC) data portal (https://portal.gdc.cancer.gov/projects/TCGABRCA).

    Article Title: Identification of DDX60 as a Regulator of MHC-I Class Molecules in Colorectal Cancer.
    Article Snippet: The TCGA mRNA-sequencing data of CRC was obtained from the Broad Institute FireBrowse portal [36].

    Article Title: UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples.
    Article Snippet: We downloaded paired RNA-seq, WES and clinical data of 1,020 RCC tumor and adjacent normal samples in TCGA KIPAN from the Genome Data Analysis Center (GDAC) at Broad Institute.

    Article Title: Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR
    Article Snippet: We downloaded TCGA RNA-seq data (gene read counts) from the BROAD Institute TCGA GDAC at firebrowse.org (March 2019).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines.
    Article Snippet: Signaling pathway activation analysis was adapted from Tan et al.6 Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Cell Extrinsic Synaptogyrin 3 Expression as a Diagnostic and Prognostic Biomarker in Head and Neck Cancer
    Article Snippet: The TCGA RNA-seq datasets used in this study were downloaded from The Broad Institute TCGA GDAC Firehose ( gdac.broadinsitue.org ), which provides TCGA Level 3 data and Level 4 analyses packaged in a form amenable to immediate algorithmic analysis.

    Microarray:

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database ( https://gdac.broadinstitute.org/ ) and Genomic Data Commons (GDC) data portal ( https://portal.gdc.cancer.gov/projects/TCGABRCA ).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines
    Article Snippet: Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy.
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database (https://gdac. broadinstitute.org/) and Genomic Data Commons (GDC) data portal (https://portal.gdc.cancer.gov/projects/TCGABRCA).

    Article Title: Identification of DDX60 as a Regulator of MHC-I Class Molecules in Colorectal Cancer.
    Article Snippet: The TCGA mRNA-sequencing data of CRC was obtained from the Broad Institute FireBrowse portal [36].

    Article Title: UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples.
    Article Snippet: We downloaded paired RNA-seq, WES and clinical data of 1,020 RCC tumor and adjacent normal samples in TCGA KIPAN from the Genome Data Analysis Center (GDAC) at Broad Institute.

    Article Title: Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR
    Article Snippet: We downloaded TCGA RNA-seq data (gene read counts) from the BROAD Institute TCGA GDAC at firebrowse.org (March 2019).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines.
    Article Snippet: Signaling pathway activation analysis was adapted from Tan et al.6 Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Cell Extrinsic Synaptogyrin 3 Expression as a Diagnostic and Prognostic Biomarker in Head and Neck Cancer
    Article Snippet: The TCGA RNA-seq datasets used in this study were downloaded from The Broad Institute TCGA GDAC Firehose ( gdac.broadinsitue.org ), which provides TCGA Level 3 data and Level 4 analyses packaged in a form amenable to immediate algorithmic analysis.

    Expressing:

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database ( https://gdac.broadinstitute.org/ ) and Genomic Data Commons (GDC) data portal ( https://portal.gdc.cancer.gov/projects/TCGABRCA ).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines
    Article Snippet: Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy.
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database (https://gdac. broadinstitute.org/) and Genomic Data Commons (GDC) data portal (https://portal.gdc.cancer.gov/projects/TCGABRCA).

    Article Title: Identification of DDX60 as a Regulator of MHC-I Class Molecules in Colorectal Cancer.
    Article Snippet: The TCGA mRNA-sequencing data of CRC was obtained from the Broad Institute FireBrowse portal [36].

    Article Title: UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples.
    Article Snippet: We downloaded paired RNA-seq, WES and clinical data of 1,020 RCC tumor and adjacent normal samples in TCGA KIPAN from the Genome Data Analysis Center (GDAC) at Broad Institute.

    Article Title: Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR
    Article Snippet: We downloaded TCGA RNA-seq data (gene read counts) from the BROAD Institute TCGA GDAC at firebrowse.org (March 2019).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines.
    Article Snippet: Signaling pathway activation analysis was adapted from Tan et al.6 Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Cell Extrinsic Synaptogyrin 3 Expression as a Diagnostic and Prognostic Biomarker in Head and Neck Cancer
    Article Snippet: The TCGA RNA-seq datasets used in this study were downloaded from The Broad Institute TCGA GDAC Firehose ( gdac.broadinsitue.org ), which provides TCGA Level 3 data and Level 4 analyses packaged in a form amenable to immediate algorithmic analysis.

    Immunofluorescence:

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database ( https://gdac.broadinstitute.org/ ) and Genomic Data Commons (GDC) data portal ( https://portal.gdc.cancer.gov/projects/TCGABRCA ).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines
    Article Snippet: Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy.
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database (https://gdac. broadinstitute.org/) and Genomic Data Commons (GDC) data portal (https://portal.gdc.cancer.gov/projects/TCGABRCA).

    Article Title: Identification of DDX60 as a Regulator of MHC-I Class Molecules in Colorectal Cancer.
    Article Snippet: The TCGA mRNA-sequencing data of CRC was obtained from the Broad Institute FireBrowse portal [36].

    Article Title: UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples.
    Article Snippet: We downloaded paired RNA-seq, WES and clinical data of 1,020 RCC tumor and adjacent normal samples in TCGA KIPAN from the Genome Data Analysis Center (GDAC) at Broad Institute.

    Article Title: Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR
    Article Snippet: We downloaded TCGA RNA-seq data (gene read counts) from the BROAD Institute TCGA GDAC at firebrowse.org (March 2019).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines.
    Article Snippet: Signaling pathway activation analysis was adapted from Tan et al.6 Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Cell Extrinsic Synaptogyrin 3 Expression as a Diagnostic and Prognostic Biomarker in Head and Neck Cancer
    Article Snippet: The TCGA RNA-seq datasets used in this study were downloaded from The Broad Institute TCGA GDAC Firehose ( gdac.broadinsitue.org ), which provides TCGA Level 3 data and Level 4 analyses packaged in a form amenable to immediate algorithmic analysis.

    Generated:

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database ( https://gdac.broadinstitute.org/ ) and Genomic Data Commons (GDC) data portal ( https://portal.gdc.cancer.gov/projects/TCGABRCA ).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines
    Article Snippet: Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy.
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database (https://gdac. broadinstitute.org/) and Genomic Data Commons (GDC) data portal (https://portal.gdc.cancer.gov/projects/TCGABRCA).

    Article Title: Identification of DDX60 as a Regulator of MHC-I Class Molecules in Colorectal Cancer.
    Article Snippet: The TCGA mRNA-sequencing data of CRC was obtained from the Broad Institute FireBrowse portal [36].

    Article Title: UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples.
    Article Snippet: We downloaded paired RNA-seq, WES and clinical data of 1,020 RCC tumor and adjacent normal samples in TCGA KIPAN from the Genome Data Analysis Center (GDAC) at Broad Institute.

    Article Title: Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR
    Article Snippet: We downloaded TCGA RNA-seq data (gene read counts) from the BROAD Institute TCGA GDAC at firebrowse.org (March 2019).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines.
    Article Snippet: Signaling pathway activation analysis was adapted from Tan et al.6 Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Cell Extrinsic Synaptogyrin 3 Expression as a Diagnostic and Prognostic Biomarker in Head and Neck Cancer
    Article Snippet: The TCGA RNA-seq datasets used in this study were downloaded from The Broad Institute TCGA GDAC Firehose ( gdac.broadinsitue.org ), which provides TCGA Level 3 data and Level 4 analyses packaged in a form amenable to immediate algorithmic analysis.

    Construct:

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database ( https://gdac.broadinstitute.org/ ) and Genomic Data Commons (GDC) data portal ( https://portal.gdc.cancer.gov/projects/TCGABRCA ).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines
    Article Snippet: Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy.
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database (https://gdac. broadinstitute.org/) and Genomic Data Commons (GDC) data portal (https://portal.gdc.cancer.gov/projects/TCGABRCA).

    Article Title: Identification of DDX60 as a Regulator of MHC-I Class Molecules in Colorectal Cancer.
    Article Snippet: The TCGA mRNA-sequencing data of CRC was obtained from the Broad Institute FireBrowse portal [36].

    Article Title: UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples.
    Article Snippet: We downloaded paired RNA-seq, WES and clinical data of 1,020 RCC tumor and adjacent normal samples in TCGA KIPAN from the Genome Data Analysis Center (GDAC) at Broad Institute.

    Article Title: Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR
    Article Snippet: We downloaded TCGA RNA-seq data (gene read counts) from the BROAD Institute TCGA GDAC at firebrowse.org (March 2019).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines.
    Article Snippet: Signaling pathway activation analysis was adapted from Tan et al.6 Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Cell Extrinsic Synaptogyrin 3 Expression as a Diagnostic and Prognostic Biomarker in Head and Neck Cancer
    Article Snippet: The TCGA RNA-seq datasets used in this study were downloaded from The Broad Institute TCGA GDAC Firehose ( gdac.broadinsitue.org ), which provides TCGA Level 3 data and Level 4 analyses packaged in a form amenable to immediate algorithmic analysis.

    Immunopeptidomics:

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database ( https://gdac.broadinstitute.org/ ) and Genomic Data Commons (GDC) data portal ( https://portal.gdc.cancer.gov/projects/TCGABRCA ).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines
    Article Snippet: Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy.
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database (https://gdac. broadinstitute.org/) and Genomic Data Commons (GDC) data portal (https://portal.gdc.cancer.gov/projects/TCGABRCA).

    Article Title: Identification of DDX60 as a Regulator of MHC-I Class Molecules in Colorectal Cancer.
    Article Snippet: The TCGA mRNA-sequencing data of CRC was obtained from the Broad Institute FireBrowse portal [36].

    Article Title: UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples.
    Article Snippet: We downloaded paired RNA-seq, WES and clinical data of 1,020 RCC tumor and adjacent normal samples in TCGA KIPAN from the Genome Data Analysis Center (GDAC) at Broad Institute.

    Article Title: Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR
    Article Snippet: We downloaded TCGA RNA-seq data (gene read counts) from the BROAD Institute TCGA GDAC at firebrowse.org (March 2019).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines.
    Article Snippet: Signaling pathway activation analysis was adapted from Tan et al.6 Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Cell Extrinsic Synaptogyrin 3 Expression as a Diagnostic and Prognostic Biomarker in Head and Neck Cancer
    Article Snippet: The TCGA RNA-seq datasets used in this study were downloaded from The Broad Institute TCGA GDAC Firehose ( gdac.broadinsitue.org ), which provides TCGA Level 3 data and Level 4 analyses packaged in a form amenable to immediate algorithmic analysis.

    Comparison:

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database ( https://gdac.broadinstitute.org/ ) and Genomic Data Commons (GDC) data portal ( https://portal.gdc.cancer.gov/projects/TCGABRCA ).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines
    Article Snippet: Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy.
    Article Snippet: TCGA RNA-sequencing and clinical data (n=1,097) were downloaded from the Broad Institute Genome Data Analysis Center Firehose database (https://gdac. broadinstitute.org/) and Genomic Data Commons (GDC) data portal (https://portal.gdc.cancer.gov/projects/TCGABRCA).

    Article Title: Identification of DDX60 as a Regulator of MHC-I Class Molecules in Colorectal Cancer.
    Article Snippet: The TCGA mRNA-sequencing data of CRC was obtained from the Broad Institute FireBrowse portal [36].

    Article Title: UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples.
    Article Snippet: We downloaded paired RNA-seq, WES and clinical data of 1,020 RCC tumor and adjacent normal samples in TCGA KIPAN from the Genome Data Analysis Center (GDAC) at Broad Institute.

    Article Title: Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR
    Article Snippet: We downloaded TCGA RNA-seq data (gene read counts) from the BROAD Institute TCGA GDAC at firebrowse.org (March 2019).

    Article Title: Mutant TP53 switches therapeutic vulnerability during gastric cancer progression within interleukin-6 family cytokines.
    Article Snippet: Signaling pathway activation analysis was adapted from Tan et al.6 Briefly, level 3 TCGA RNA-seq normalized data for 415 gastric cancer samples and 35 normal gastric samples, and their corresponding clinical information, were downloaded from the Broad Institute TCGA Genome Data Analysis Center Firehose.

    Article Title: Tumor Cell Extrinsic Synaptogyrin 3 Expression as a Diagnostic and Prognostic Biomarker in Head and Neck Cancer
    Article Snippet: The TCGA RNA-seq datasets used in this study were downloaded from The Broad Institute TCGA GDAC Firehose ( gdac.broadinsitue.org ), which provides TCGA Level 3 data and Level 4 analyses packaged in a form amenable to immediate algorithmic analysis.



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    cell rna seq data analysis  (Broad Clinical Labs)
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    Minor macroscopic effects of Parp14 deficiency on the severity of salmonellosis. ( A, B ) Schematic representation of the single-animal experiment executed in this study. The blue box refers to mice, which were subjected to statistical comparisons throughout the study. The tissues marked with an asterisk were longitudinally cut into two pieces, one for histology and one for <t>qPCR/RNA-Seq.</t> Images were partially created with BioRender.com. ( C ) Weight change of the mice during the course of the experiment relative to day −1 (medians with interquartile range). No statistically significant differences between the infected wt and Parp14-deficient mice were detected. Statistical significance values are shown in the figure. Weights of the PBS mice were not statistically compared (NA, not applicable; fewer than three animals to compare, see ). ( D ) Colon lengths at day 1 and day 5. ( E ) Spleen weights at day 1 and day 5. ( F ) Liver weights at day 1 and day 5. ( G–L ) Determination of viable bacteria in different tissues at day 1 and day 5. Bars in sub-panels D–L represent medians with interquartile range. All individual data points are shown. Statistical significance values for the differences between the infected wt and Parp14-deficient mice are shown in each D–L sub-panel. Fecal pellets were not obtained from all mice. Parameters of the PBS mice were not statistically compared (NA, not applicable; fewer than three animals to compare, see ).
    Cell Rna Seq Data Analysis, supplied by Broad Clinical Labs, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    rna seq data analysis  (Novogene)
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    Minor macroscopic effects of Parp14 deficiency on the severity of salmonellosis. ( A, B ) Schematic representation of the single-animal experiment executed in this study. The blue box refers to mice, which were subjected to statistical comparisons throughout the study. The tissues marked with an asterisk were longitudinally cut into two pieces, one for histology and one for <t>qPCR/RNA-Seq.</t> Images were partially created with BioRender.com. ( C ) Weight change of the mice during the course of the experiment relative to day −1 (medians with interquartile range). No statistically significant differences between the infected wt and Parp14-deficient mice were detected. Statistical significance values are shown in the figure. Weights of the PBS mice were not statistically compared (NA, not applicable; fewer than three animals to compare, see ). ( D ) Colon lengths at day 1 and day 5. ( E ) Spleen weights at day 1 and day 5. ( F ) Liver weights at day 1 and day 5. ( G–L ) Determination of viable bacteria in different tissues at day 1 and day 5. Bars in sub-panels D–L represent medians with interquartile range. All individual data points are shown. Statistical significance values for the differences between the infected wt and Parp14-deficient mice are shown in each D–L sub-panel. Fecal pellets were not obtained from all mice. Parameters of the PBS mice were not statistically compared (NA, not applicable; fewer than three animals to compare, see ).
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    Image Search Results


    Minor macroscopic effects of Parp14 deficiency on the severity of salmonellosis. ( A, B ) Schematic representation of the single-animal experiment executed in this study. The blue box refers to mice, which were subjected to statistical comparisons throughout the study. The tissues marked with an asterisk were longitudinally cut into two pieces, one for histology and one for qPCR/RNA-Seq. Images were partially created with BioRender.com. ( C ) Weight change of the mice during the course of the experiment relative to day −1 (medians with interquartile range). No statistically significant differences between the infected wt and Parp14-deficient mice were detected. Statistical significance values are shown in the figure. Weights of the PBS mice were not statistically compared (NA, not applicable; fewer than three animals to compare, see ). ( D ) Colon lengths at day 1 and day 5. ( E ) Spleen weights at day 1 and day 5. ( F ) Liver weights at day 1 and day 5. ( G–L ) Determination of viable bacteria in different tissues at day 1 and day 5. Bars in sub-panels D–L represent medians with interquartile range. All individual data points are shown. Statistical significance values for the differences between the infected wt and Parp14-deficient mice are shown in each D–L sub-panel. Fecal pellets were not obtained from all mice. Parameters of the PBS mice were not statistically compared (NA, not applicable; fewer than three animals to compare, see ).

    Journal: Microbiology Spectrum

    Article Title: Exacerbated salmonellosis in poly(ADP-ribose) polymerase 14-deficient mice

    doi: 10.1128/spectrum.02971-25

    Figure Lengend Snippet: Minor macroscopic effects of Parp14 deficiency on the severity of salmonellosis. ( A, B ) Schematic representation of the single-animal experiment executed in this study. The blue box refers to mice, which were subjected to statistical comparisons throughout the study. The tissues marked with an asterisk were longitudinally cut into two pieces, one for histology and one for qPCR/RNA-Seq. Images were partially created with BioRender.com. ( C ) Weight change of the mice during the course of the experiment relative to day −1 (medians with interquartile range). No statistically significant differences between the infected wt and Parp14-deficient mice were detected. Statistical significance values are shown in the figure. Weights of the PBS mice were not statistically compared (NA, not applicable; fewer than three animals to compare, see ). ( D ) Colon lengths at day 1 and day 5. ( E ) Spleen weights at day 1 and day 5. ( F ) Liver weights at day 1 and day 5. ( G–L ) Determination of viable bacteria in different tissues at day 1 and day 5. Bars in sub-panels D–L represent medians with interquartile range. All individual data points are shown. Statistical significance values for the differences between the infected wt and Parp14-deficient mice are shown in each D–L sub-panel. Fecal pellets were not obtained from all mice. Parameters of the PBS mice were not statistically compared (NA, not applicable; fewer than three animals to compare, see ).

    Article Snippet: We used the single-cell RNA-Seq data analysis and visualization interface at the Broad Institute Single Cell Portal ( https://singlecell.broadinstitute.org/single_cell ) in order to analyze Parp1 and Parp14 expression.

    Techniques: RNA Sequencing, Infection, Bacteria

    Quantitation of Parp14 expression in the mouse gastrointestinal tract. ( A ) The QuPath-based quantitation of Parp14 expression. Representative examples of the Parp14 stainings are shown in . The values on the y -axis refer to the means of DAB staining intensity, that is, the mean OD in the QuPath data output. Each dot refers to a single cell. The numbers of analyzed cells (mostly epithelial cells) are indicated on the x -axis (see ). The red lines above the data points refer to the mean values. Statistical analyses were conducted using the two-tailed unpaired t -test (NA, not applicable; fewer than three animals to compare, see ). One Salmonella -infected day 5 mouse was left out from the quantitation due to poor quality of the FFPE tissue block. ( B ) The qPCR data on relative Parp14 expression (means with standard deviation, statistics performed using two-tailed unpaired t -test). Samples were included in the data analysis if they passed the 0.5 standard deviation Ct filter for replicate runs. No statistical analyses were executed against the PBS groups because there were less than three data points/animal to compare (see ). The calibrators in each sub-panel are the mean dCq values of the day 1 Salmonella -infected mice.

    Journal: Microbiology Spectrum

    Article Title: Exacerbated salmonellosis in poly(ADP-ribose) polymerase 14-deficient mice

    doi: 10.1128/spectrum.02971-25

    Figure Lengend Snippet: Quantitation of Parp14 expression in the mouse gastrointestinal tract. ( A ) The QuPath-based quantitation of Parp14 expression. Representative examples of the Parp14 stainings are shown in . The values on the y -axis refer to the means of DAB staining intensity, that is, the mean OD in the QuPath data output. Each dot refers to a single cell. The numbers of analyzed cells (mostly epithelial cells) are indicated on the x -axis (see ). The red lines above the data points refer to the mean values. Statistical analyses were conducted using the two-tailed unpaired t -test (NA, not applicable; fewer than three animals to compare, see ). One Salmonella -infected day 5 mouse was left out from the quantitation due to poor quality of the FFPE tissue block. ( B ) The qPCR data on relative Parp14 expression (means with standard deviation, statistics performed using two-tailed unpaired t -test). Samples were included in the data analysis if they passed the 0.5 standard deviation Ct filter for replicate runs. No statistical analyses were executed against the PBS groups because there were less than three data points/animal to compare (see ). The calibrators in each sub-panel are the mean dCq values of the day 1 Salmonella -infected mice.

    Article Snippet: We used the single-cell RNA-Seq data analysis and visualization interface at the Broad Institute Single Cell Portal ( https://singlecell.broadinstitute.org/single_cell ) in order to analyze Parp1 and Parp14 expression.

    Techniques: Quantitation Assay, Expressing, Staining, Single Cell, Two Tailed Test, Infection, Blocking Assay, Standard Deviation

    Transcriptional signatures uniquely detected in S . Typhimurium-infected wt and Parp14-deficient mice. Data from a triplicate RNA-Seq analysis of mouse large intestine sections 1 day post-infection are shown. ( A ) The Venn diagrams of shared and unique genes that were detected to be expressed in the infected wt and Parp14-deficient mice (FPKM value >1). The integer is the number of genes detected to be expressed in both of the genotypes. ( B ) The pie charts of the numbers of identified GO terms based on the genotype-specific lists of expressed genes (BP, biological process; CC, cellular component; MF, molecular function; ). ( C–E ) Bar graph representation of all the identified GO BP terms with the genotype-specific lists of expressed genes. The BP terms are sorted based on the percentage of GO term gene values (number of detected genes in a particular BP term / number of all genes in particular BP term × 100). FDR refers to the false discovery rate value. An FDR value cut-off of <0.05 was used in the searches. The asterisks in the wt sub-panel ( D ) refer to the seven infection- and inflammation response-related BP terms. The sub-panel E displays the genes of these seven infection- and inflammation response-related BP terms. ( F–H ) Pathway-enrichment dot plot representations of all (KO sub-panel) and the top 10 (wt sub-panel) KEGG pathways identified with the genotype-specific lists of expressed genes. All the identified KEGG pathways with the corresponding gene lists are described in . The KEGG pathways are sorted based on the P -value. The count values refer to the number of genes that were detected in a particular KEGG pathway. The asterisk in the wt sub-panel ( G ) refers to the only KEGG pathway with a <0.05 P adj -value. The sub-panel H displays the genes of this IL-17 signaling pathway.

    Journal: Microbiology Spectrum

    Article Title: Exacerbated salmonellosis in poly(ADP-ribose) polymerase 14-deficient mice

    doi: 10.1128/spectrum.02971-25

    Figure Lengend Snippet: Transcriptional signatures uniquely detected in S . Typhimurium-infected wt and Parp14-deficient mice. Data from a triplicate RNA-Seq analysis of mouse large intestine sections 1 day post-infection are shown. ( A ) The Venn diagrams of shared and unique genes that were detected to be expressed in the infected wt and Parp14-deficient mice (FPKM value >1). The integer is the number of genes detected to be expressed in both of the genotypes. ( B ) The pie charts of the numbers of identified GO terms based on the genotype-specific lists of expressed genes (BP, biological process; CC, cellular component; MF, molecular function; ). ( C–E ) Bar graph representation of all the identified GO BP terms with the genotype-specific lists of expressed genes. The BP terms are sorted based on the percentage of GO term gene values (number of detected genes in a particular BP term / number of all genes in particular BP term × 100). FDR refers to the false discovery rate value. An FDR value cut-off of <0.05 was used in the searches. The asterisks in the wt sub-panel ( D ) refer to the seven infection- and inflammation response-related BP terms. The sub-panel E displays the genes of these seven infection- and inflammation response-related BP terms. ( F–H ) Pathway-enrichment dot plot representations of all (KO sub-panel) and the top 10 (wt sub-panel) KEGG pathways identified with the genotype-specific lists of expressed genes. All the identified KEGG pathways with the corresponding gene lists are described in . The KEGG pathways are sorted based on the P -value. The count values refer to the number of genes that were detected in a particular KEGG pathway. The asterisk in the wt sub-panel ( G ) refers to the only KEGG pathway with a <0.05 P adj -value. The sub-panel H displays the genes of this IL-17 signaling pathway.

    Article Snippet: We used the single-cell RNA-Seq data analysis and visualization interface at the Broad Institute Single Cell Portal ( https://singlecell.broadinstitute.org/single_cell ) in order to analyze Parp1 and Parp14 expression.

    Techniques: Infection, RNA Sequencing

    Hampered expression of four cytokines in the large intestine of S . Typhimurium-infected Parp14-deficient mice. Four hit genes of the large intestine bulk tissue RNA-Seq analysis ( Ccl2 , Ccl7 , Cxcl10 , Il1b ) were analyzed. Five other TaqMan qPCR assays on inflammation-associated genes were run in parallel. The figure illustrates the TaqMan qPCR data on relative gene expression with means and standard deviations. The calibrators in all sub-panels are the mean dCq values of the day 1 infected wt mice. Statistical analyses were done with a two-tailed unpaired t -test. All the statistical significance values of the comparisons between the wt and Parp14-deficient mice are indicated.

    Journal: Microbiology Spectrum

    Article Title: Exacerbated salmonellosis in poly(ADP-ribose) polymerase 14-deficient mice

    doi: 10.1128/spectrum.02971-25

    Figure Lengend Snippet: Hampered expression of four cytokines in the large intestine of S . Typhimurium-infected Parp14-deficient mice. Four hit genes of the large intestine bulk tissue RNA-Seq analysis ( Ccl2 , Ccl7 , Cxcl10 , Il1b ) were analyzed. Five other TaqMan qPCR assays on inflammation-associated genes were run in parallel. The figure illustrates the TaqMan qPCR data on relative gene expression with means and standard deviations. The calibrators in all sub-panels are the mean dCq values of the day 1 infected wt mice. Statistical analyses were done with a two-tailed unpaired t -test. All the statistical significance values of the comparisons between the wt and Parp14-deficient mice are indicated.

    Article Snippet: We used the single-cell RNA-Seq data analysis and visualization interface at the Broad Institute Single Cell Portal ( https://singlecell.broadinstitute.org/single_cell ) in order to analyze Parp1 and Parp14 expression.

    Techniques: Expressing, Infection, RNA Sequencing, Gene Expression, Two Tailed Test

    Transcriptional signature downregulated in S . Typhimurium-infected Parp14-deficient mice. Data from triplicate bulk tissue RNA-Seq analysis of mouse large intestine sections 1 day post-infection are shown. ( A ) Inter-sample correlation heatmap based on the FPKM values of the DEGs in Parp14-deficient vs wt mice comparison. R 2 is the square of Pearson correlation coefficient ( R ). ( B ) Volcano plots of the DEGs. Specific information on the DEGs is given in . The x -axis shows the fold difference in gene expression between different samples, and the y -axis shows the statistical significance of the differences. Red dots represent upregulation genes, and green dots represent downregulation genes. The dashed line indicates the threshold line for statistically significant differential gene expression. The values marked with asterisks refer to the number of DEGs that were used for a stringent downstream data analysis, that is, UP genes, log2(FoldChange) > 0.5 and P adj < 0.05; DOWN genes, log2(FoldChange) < −0.5 and P adj < 0.05 . ( C ) GO term analysis with DEGs in Parp14-deficient vs wt mice comparison (BP, biological process; CC, cellular component; MF, molecular function; ). The GO terms were searched using the canonical Fisher’s test and an FDR value <0.05 filter. ( D ) Bar graph representations of the top 20 identified GO BP terms (all the 107 identified GO BP terms in ) sorted based on the percentage of GO term gene values (number of detected genes in a particular GO term / number of all genes in a particular GO term × 100). The black asterisks in the sub-panel refer to the PB terms with functional relevance to cell adhesion and cytoskeleton remodeling. ( E ) Pathway-enrichment dot plot representations of the top 10 identified KEGG pathways sorted based on the P -value. All the identified KEGG pathways with the corresponding gene lists are described in . The count values refer to the number of genes that were detected in a particular KEGG pathway. The black asterisk in the wt sub-panel refers to the KEGG pathways with a <0.05 P adj -value.

    Journal: Microbiology Spectrum

    Article Title: Exacerbated salmonellosis in poly(ADP-ribose) polymerase 14-deficient mice

    doi: 10.1128/spectrum.02971-25

    Figure Lengend Snippet: Transcriptional signature downregulated in S . Typhimurium-infected Parp14-deficient mice. Data from triplicate bulk tissue RNA-Seq analysis of mouse large intestine sections 1 day post-infection are shown. ( A ) Inter-sample correlation heatmap based on the FPKM values of the DEGs in Parp14-deficient vs wt mice comparison. R 2 is the square of Pearson correlation coefficient ( R ). ( B ) Volcano plots of the DEGs. Specific information on the DEGs is given in . The x -axis shows the fold difference in gene expression between different samples, and the y -axis shows the statistical significance of the differences. Red dots represent upregulation genes, and green dots represent downregulation genes. The dashed line indicates the threshold line for statistically significant differential gene expression. The values marked with asterisks refer to the number of DEGs that were used for a stringent downstream data analysis, that is, UP genes, log2(FoldChange) > 0.5 and P adj < 0.05; DOWN genes, log2(FoldChange) < −0.5 and P adj < 0.05 . ( C ) GO term analysis with DEGs in Parp14-deficient vs wt mice comparison (BP, biological process; CC, cellular component; MF, molecular function; ). The GO terms were searched using the canonical Fisher’s test and an FDR value <0.05 filter. ( D ) Bar graph representations of the top 20 identified GO BP terms (all the 107 identified GO BP terms in ) sorted based on the percentage of GO term gene values (number of detected genes in a particular GO term / number of all genes in a particular GO term × 100). The black asterisks in the sub-panel refer to the PB terms with functional relevance to cell adhesion and cytoskeleton remodeling. ( E ) Pathway-enrichment dot plot representations of the top 10 identified KEGG pathways sorted based on the P -value. All the identified KEGG pathways with the corresponding gene lists are described in . The count values refer to the number of genes that were detected in a particular KEGG pathway. The black asterisk in the wt sub-panel refers to the KEGG pathways with a <0.05 P adj -value.

    Article Snippet: We used the single-cell RNA-Seq data analysis and visualization interface at the Broad Institute Single Cell Portal ( https://singlecell.broadinstitute.org/single_cell ) in order to analyze Parp1 and Parp14 expression.

    Techniques: Infection, RNA Sequencing, Comparison, Gene Expression, Functional Assay

    Epithelial cell-specific transcriptomic signature downregulated in the large intestine of S . Typhimurium-infected Parp14-deficient mice. ( A ) The Venn diagrams of the shared and unique genes in two comparisons, that is (i) genes upregulated by infection in wt mice (single-cell data ) vs genes downregulated by infection in Parp14-deficient mice (bulk tissue data), and (ii) genes downregulated by infection in wt mice (single-cell data ) vs genes upregulated by infection in Parp14-deficient mice (bulk tissue data). ( B ) The key single-cell RNA-Seq differential expression metrics of the shared genes. The numbers behind the gene names indicate the rank numbers, for example, ApoA1 was the third highest upregulated gene in goblet cells. ( C ) The key bulk tissue differential expression metrics of the shared genes. ( D ) TaqMan qPCR validation of the four shared genes with small and large intestine samples at day 1 and day 5. The figure illustrates the TaqMan qPCR data on relative gene expression with means and standard deviations. The calibrators in all sub-panels are the mean dCq values of the infected wt mice. Statistical analyses were done with a two-tailed unpaired t -test. All the statistical significance values of the comparisons between the wt and Parp14-deficient mice are indicated.

    Journal: Microbiology Spectrum

    Article Title: Exacerbated salmonellosis in poly(ADP-ribose) polymerase 14-deficient mice

    doi: 10.1128/spectrum.02971-25

    Figure Lengend Snippet: Epithelial cell-specific transcriptomic signature downregulated in the large intestine of S . Typhimurium-infected Parp14-deficient mice. ( A ) The Venn diagrams of the shared and unique genes in two comparisons, that is (i) genes upregulated by infection in wt mice (single-cell data ) vs genes downregulated by infection in Parp14-deficient mice (bulk tissue data), and (ii) genes downregulated by infection in wt mice (single-cell data ) vs genes upregulated by infection in Parp14-deficient mice (bulk tissue data). ( B ) The key single-cell RNA-Seq differential expression metrics of the shared genes. The numbers behind the gene names indicate the rank numbers, for example, ApoA1 was the third highest upregulated gene in goblet cells. ( C ) The key bulk tissue differential expression metrics of the shared genes. ( D ) TaqMan qPCR validation of the four shared genes with small and large intestine samples at day 1 and day 5. The figure illustrates the TaqMan qPCR data on relative gene expression with means and standard deviations. The calibrators in all sub-panels are the mean dCq values of the infected wt mice. Statistical analyses were done with a two-tailed unpaired t -test. All the statistical significance values of the comparisons between the wt and Parp14-deficient mice are indicated.

    Article Snippet: We used the single-cell RNA-Seq data analysis and visualization interface at the Broad Institute Single Cell Portal ( https://singlecell.broadinstitute.org/single_cell ) in order to analyze Parp1 and Parp14 expression.

    Techniques: Infection, Single Cell, RNA Sequencing, Quantitative Proteomics, Biomarker Discovery, Gene Expression, Two Tailed Test