Differentially expressed genes

Gene expression profile analysis of testis and ovary of oriental river prawn, Macrobrachium nipponense, reveals candidate reproduction-related genes

H. Qiao, Xiong, Y. W., Jiang, S. F., Fu, H. T., Sun, S. M., Jin, S. B., Gong, Y. S., and Zhang, W. Y., “Gene expression profile analysis of testis and ovary of oriental river prawn, Macrobrachium nipponense, reveals candidate reproduction-related genes”, vol. 14, pp. 2041-2054, 2015.

This study utilized high-throughput RNA sequencing technology to identify reproduction- and development-related genes of Macrobrachium nipponense by analyzing gene expression profiles of testis and ovary. More than 20 million 1 x 51-bp reads were obtained by Illumina sequencing, generating more than 7.7 and 11.7 million clean reads in the testis and ovary library, respectively. As a result, 10,018 unitags were supposed to be differentially expressed genes (DEGs) between ovary and testis.

Protein-protein interaction network and mechanism analysis of hepatitis C

Y. Tang, Tang, Q., Dong, C., Li, X., Zhang, Z., and An, F., “Protein-protein interaction network and mechanism analysis of hepatitis C”, vol. 14, pp. 2069-2079, 2015.

We predicted potential genes and identified pathways associated with hepatitis C. The gene expression profiles of GSE40184 from blood samples and GSE38597 from liver biopsy samples were downloaded from the GEO database. Differentially expressed genes (DEGs) were recognized using the Limma Package. The Pearson correlation test was used to construct the co-expression network of DEGs. Gene set enrichment analysis was used to define significant functions and pathways for DEGs. A total of 165 DEGs in blood samples and 523 DEGs in liver biopsy samples were identified.

Meta-analysis of differentially expressed genes in autism based on gene expression data

L. F. Ning, Yu, Y. Q., GuoJi, E. T., Kou, C. G., Wu, Y. H., Shi, J. P., Ai, L. Z., and Yu, Q., “Meta-analysis of differentially expressed genes in autism based on gene expression data”, vol. 14, pp. 2146-2155, 2015.

The purpose of this study was to identify differentially expressed (DE) genes and biological processes associated with changes in gene expression in autism. We performed a meta-analysis using new publicly available Gene Expression Omnibus (GEO) datasets of autism. We performed Gene Ontology (GO) enrichment analyses and pathway analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG). Ten GEO datasets, including 364 cases and 248 controls, were available for the meta-analysis.

Identification of differentially expressed genes associated with flower color in peach using genome-wide transcriptional analysis

Y. Zhou, Wu, X. X., Zhang, Z., and Gao, Z. H., “Identification of differentially expressed genes associated with flower color in peach using genome-wide transcriptional analysis”, vol. 14, pp. 4724-4739, 2015.

Flower color is an important trait of the ornamental peach (Prunus persica L.). However, the mechanism responsible for the different colors that appear in the same genotype remains unclear. In this study, red samples showed higher anthocyanins content (0.122 ± 0.009 mg/g), which was significantly different from that in white samples (0.066 ± 0.010 mg/g). Similarly to carotenoids content, red extract (0.058 ± 0.004 mg/L) was significantly higher in white extract (0.015 ± 0.004 mg/L).

Integrated miRNA-mRNA analysis of Epstein-Barr virus-positive nasopharyngeal carcinoma

L. H. Zhu, Miao, X. T., and Wang, N. Y., “Integrated miRNA-mRNA analysis of Epstein-Barr virus-positive nasopharyngeal carcinoma”, vol. 14, pp. 6028-6036, 2015.

This study aims to identify the crucial miRNAs in Epstein-Barr virus-positive nasopharyngeal carcinoma (NPC) and their target genes. Gene expression profile data (GSE12452) that included 31 NPC and 10 normal nasopharyngeal tissue specimens were downloaded. Differentially expressed genes (DEGs) were identified using significance analysis of microarrays. The underlying function of DEGs was predicted via Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses.

Differential gene expression in pre-laying and laying period ovaries of Sichuan White geese (Anser cygnoides)

N. Ding, Han, Q., Zhao, X. Z., Li, Q., Li, J., Zhang, H. F., Gao, G. L., Luo, Y., Xie, Y. H., Su, J., and Wang, Q. G., “Differential gene expression in pre-laying and laying period ovaries of Sichuan White geese (Anser cygnoides)”, vol. 14, pp. 6773-6785, 2015.

Geese are an economically important poultry species worldwide. Their superior meat production performance and meat qual­ity make them a popular food. However, they are not bred worldwide because their poor laying capacity increases farming costs. To gain a global view of the genes that are differentially expressed between pre-laying (P) and laying (L) periods and to develop a database for further studies, we performed large-scale transcriptome sequencing of ovarian tissue collected from Anser cygnoides.

Bioinformatic analysis of endothelial progenitor cells exposed to folic acid in type 1 diabetes mellitus

D. N. Fang, He, X. D., Li, X. H., Jia, H., Li, P. Y., Lu, Q., Quan, Z., and Wang, Q. L., “Bioinformatic analysis of endothelial progenitor cells exposed to folic acid in type 1 diabetes mellitus”, vol. 13, pp. 1-10, 2014.

We investigated the effects of type 1 diabetes mellitus (T1DM) on endothelial progenitor cells (EPCs) at the molecular level and assessed the therapeutic potential of folic acid (FA) in DM. We downloaded the gene expression profile of the EPCs from T1DM patients before and after treatment with FA and from healthy controls. We identified the differentially expressed genes (DEGs) in the EPCs from T1DM patients before and after a four-week period of FA treatment and compared them with those obtained from the healthy subjects by using limma package in R language.

Identifying differentially expressed genes and pathways in two types of non-small cell lung cancer: adenocarcinoma and squamous cell carcinoma

J. Liu, Yang, X. Y., and Shi, W. J., “Identifying differentially expressed genes and pathways in two types of non-small cell lung cancer: adenocarcinoma and squamous cell carcinoma”, vol. 13, pp. 95-102, 2014.

Non-small cell lung carcinoma, NSCLC, accounts for 80-85% of lung cancers. NSCLC can be mainly divided into two types: adenocarcinoma (ADC) and squamous cell carcinoma (SCC). The purpose of our study was to identify and differentiate the pathogenesis of ADC and SCC at the molecular level. The gene expression profiles of ADC and SCC were downloaded from Gene Expression Omnibus under accession No. GSE10245. Accordingly, differentially expressed genes (DEGs) were identified by the limma package in R language.

Screening of differentially expressed genes between multiple trauma patients with and without sepsis

S. C. Ji, Pan, Y. T., Lu, Q. Y., Sun, Z. Y., and Liu, Y. Z., “Screening of differentially expressed genes between multiple trauma patients with and without sepsis”, vol. 13, pp. 1855-1864, 2014.

The purpose of this study was to identify critical genes associated with septic multiple trauma by comparing peripheral whole blood samples from multiple trauma patients with and without sepsis. A microarray data set was downloaded from the Gene Expression Omnibus (GEO) database. This data set included 70 samples, 36 from multiple trauma patients with sepsis and 34 from multiple trauma patients without sepsis (as a control set). The data were preprocessed, and differentially expressed genes (DEGs) were then screened for using packages of the R language.

Screening and functional microarray analysis of differentially expressed genes related to osteoporosis

Y. Chen and Xia, R. G., “Screening and functional microarray analysis of differentially expressed genes related to osteoporosis”, vol. 13, pp. 3228-3236, 2014.

We searched for key genes that could accurately predict bone mineral density. The gene expression profile GSE7429 was downloaded from the Gene Expression Omnibus database, which includes 20 samples, 10 with high and 10 with low bone mineral density. The differentially expressed genes (DEGs) were identified with packages in R language. Further, BLASTX was used to obtain COG function classifications of all the DEGs. The GOTM software was used to find DEGs enriched modules. The functions of genes in the modules was also predicted with the software GENECODIS.

Pages

Subscribe to Differentially expressed genes