DOI: 10.57760/sciencedb.01826
title: Analysis of differentially expressed genes related to the development of diabetic cardiomyopathy in the myocardium of type-2 diabetic rats
title: Analysis of differentially expressed genes related to the development of diabetic cardiomyopathy in the myocardium of type-2 diabetic rats

description: Rats with different course of diabetes cardiomyopathy were modeled, and the gene expression profiles of four heart samples from different course groups (8w-t2dm, 16w-t2dm) and control groups (8W control, 16W control) were sequenced. A total of 121.73m clean reads were obtained. The clean reads of each sample were sequenced with the reference genome, and the comparison efficiency reached 92.71% or more. Based on the comparison results, the gene expression was analyzed. The differentially expressed genes were analyzed by pattern clustering, functional annotation and enrichment analysis. 1) Gene expression data set: take fpkm as an indicator to measure the level of transcripts or gene expression, and the result file of sample gene expression is: gene expression (including fpkm value, gene location, positive and negative values and normalized readings). 2) Differential expression analysis data set (DEG final): in the process of differential expression gene detection, fold change ≥ 2 and fdr&lt;0.01 are used as screening criteria. Fold change refers to the ratio of expression amount between two samples (groups). False discovery rate (FDR): it is obtained by correcting the difference significance p-value. 3) Go enrichment of differentially expressed genes: use the Benjamin Hochberg correction method to correct the significance p-value obtained from the original hypothesis test, and finally use FDR as the key index for screening differentially expressed genes to obtain the go classification statistical results of differentially expressed genes. The data are expressed as: \go_ classify1，GO_ classify2， All Unigene，DEG Unigene。 4) Differential expression analysis gene annotation: compare the differentially expressed genes with NR, Swiss prot GO,eggNOG , KOG，Pfam，KEGG Database for sequence alignment, kobas2.0 was used to obtain KEGG ontology results of differential genes, and Hmmer software was used to compare with Pfam database to obtain annotation information of differential genes. The data is expressed as: FDR, log2 fold change, regulation, go annotation, Swissprot_ annotation, eggNOG_ class_ annotation,nr_ annotation and description. 
description: Rats with different course of diabetes cardiomyopathy were modeled, and the gene expression profiles of four heart samples from different course groups (8w-t2dm, 16w-t2dm) and control groups (8W control, 16W control) were sequenced. A total of 121.73m clean reads were obtained. The clean reads of each sample were sequenced with the reference genome, and the comparison efficiency reached 92.71% or more. Based on the comparison results, the gene expression was analyzed. The differentially expressed genes were analyzed by pattern clustering, functional annotation and enrichment analysis. 1) Gene expression data set: take fpkm as an indicator to measure the level of transcripts or gene expression, and the result file of sample gene expression is: gene expression (including fpkm value, gene location, positive and negative values and normalized readings). 2) Differential expression analysis data set (DEG final): in the process of differential expression gene detection, fold change ≥ 2 and fdr&lt;0.01 are used as screening criteria. Fold change refers to the ratio of expression amount between two samples (groups). False discovery rate (FDR): it is obtained by correcting the difference significance p-value. 3) Go enrichment of differentially expressed genes: use the Benjamin Hochberg correction method to correct the significance p-value obtained from the original hypothesis test, and finally use FDR as the key index for screening differentially expressed genes to obtain the go classification statistical results of differentially expressed genes. The data are expressed as: \go_ classify1，GO_ classify2， All Unigene，DEG Unigene。 4) Differential expression analysis gene annotation: compare the differentially expressed genes with NR, Swiss prot GO,eggNOG , KOG，Pfam，KEGG Database for sequence alignment, kobas2.0 was used to obtain KEGG ontology results of differential genes, and Hmmer software was used to compare with Pfam database to obtain annotation information of differential genes. The data is expressed as: FDR, log2 fold change, regulation, go annotation, Swissprot_ annotation, eggNOG_ class_ annotation,nr_ annotation and description.