DOI: 10.57760/sciencedb.35038
title: The RNA-seq gene expression matrix of liver tissues in C57BL/6 mice under the conditions of Oil and CCl4 induction, as well as CCl4 + Tgfbi knockout
title: The RNA-seq gene expression matrix of liver tissues in C57BL/6 mice under the conditions of Oil and CCl4 induction, as well as CCl4 + Tgfbi knockout

description: 1. Data Background and Objectives This dataset is derived from a study aimed at exploring the mechanism of the Tgfbi gene in liver fibrosis. The data include the transcriptome sequencing results of the liver of C57BL/6 mice under a liver fibrosis model induced by carbon tetrachloride (CCl4), with the aim of comparing the differences between the wild type and the Tgfbi gene knockout type, and screening potential key downstream molecules through bioinformatics analysis.2. Experimental Design and Sample Information Animal model: C57BL/6 mice were used and divided into three experimental groups:Control group (Oil-WT): Wild-type mice, intraperitoneally injected with corn oil solvent.Model group (CCl4-WT): Wild-type mice, intraperitoneally injected with CCl4 (dissolved in corn oil) to induce liver fibrosis.Knockout intervention group (CCl4-Tgfbi KO): Tgfbi gene knockout mice, also exposed to CCl4 induction.Samples and Repetitions: Each group contained 3 independent biological replicates, totaling 9 mouse liver tissue samples.Key Comparisons: "CCl4-WT vs Oil-WT" defined fibrosis-related genes; "CCl4-WT vs CCl4-Tgfbi KO" defined Tgfbi-dependent genes.3. Data Generation and Processing Flow Sequencing: Perform strand-specific library construction for total liver RNA from all 9 samples.Basic analysis: Conduct quality control using FastQC, and align the raw reads to the mouse reference genome (GRCm39) using Hisat2. Calculate the raw counts at the gene level using featureCounts.Advanced analysis: Based on the raw count matrix, perform differential expression analysis (DEGs) using the DESeq2 R package. Conduct Gene Ontology (GO) functional annotation and KEGG pathway enrichment analysis for each of the selected DEGs.4. Description of Data File Content This dataset consists of the following three core files. a. Gene expression matrix, with values representing the counts of original readings. b. Differential expression and enrichment analysis results. The list of DEGs (including log2FC, p-value, etc.) for the two comparisons "CCl4-WT vs Oil-WT" and "CCl4-WT vs CCl4-KO", as well as the corresponding GO and KEGG enrichment analysis results, with filtering conditions of log2FC &gt; 1 and p-value &lt; 0.05. c. List of potential downstream genes dependent on Tgfbi. This file screens for genes that are significantly upregulated in liver fibrosis and that show a significant decrease in expression after Tgfbi knockout.
description: 1. Data Background and Objectives This dataset is derived from a study aimed at exploring the mechanism of the Tgfbi gene in liver fibrosis. The data include the transcriptome sequencing results of the liver of C57BL/6 mice under a liver fibrosis model induced by carbon tetrachloride (CCl4), with the aim of comparing the differences between the wild type and the Tgfbi gene knockout type, and screening potential key downstream molecules through bioinformatics analysis.2. Experimental Design and Sample Information Animal model: C57BL/6 mice were used and divided into three experimental groups:Control group (Oil-WT): Wild-type mice, intraperitoneally injected with corn oil solvent.Model group (CCl4-WT): Wild-type mice, intraperitoneally injected with CCl4 (dissolved in corn oil) to induce liver fibrosis.Knockout intervention group (CCl4-Tgfbi KO): Tgfbi gene knockout mice, also exposed to CCl4 induction.Samples and Repetitions: Each group contained 3 independent biological replicates, totaling 9 mouse liver tissue samples.Key Comparisons: "CCl4-WT vs Oil-WT" defined fibrosis-related genes; "CCl4-WT vs CCl4-Tgfbi KO" defined Tgfbi-dependent genes.3. Data Generation and Processing Flow Sequencing: Perform strand-specific library construction for total liver RNA from all 9 samples.Basic analysis: Conduct quality control using FastQC, and align the raw reads to the mouse reference genome (GRCm39) using Hisat2. Calculate the raw counts at the gene level using featureCounts.Advanced analysis: Based on the raw count matrix, perform differential expression analysis (DEGs) using the DESeq2 R package. Conduct Gene Ontology (GO) functional annotation and KEGG pathway enrichment analysis for each of the selected DEGs.4. Description of Data File Content This dataset consists of the following three core files. a. Gene expression matrix, with values representing the counts of original readings. b. Differential expression and enrichment analysis results. The list of DEGs (including log2FC, p-value, etc.) for the two comparisons "CCl4-WT vs Oil-WT" and "CCl4-WT vs CCl4-KO", as well as the corresponding GO and KEGG enrichment analysis results, with filtering conditions of log2FC &gt; 1 and p-value &lt; 0.05. c. List of potential downstream genes dependent on Tgfbi. This file screens for genes that are significantly upregulated in liver fibrosis and that show a significant decrease in expression after Tgfbi knockout.