scholarly journals Bioinformatics Analysis and Characteristics of UL21 Protein from Duck Virus Enteritis

2012 ◽  
Vol 1 ◽  
pp. 124-135
Author(s):  
Yong Shi ◽  
Anchun Cheng ◽  
Mingshu Wang
Keyword(s):  
Bioinformatics Analysis ◽  
Duck Virus Enteritis

Bioinformatics Analysis of the Complete Nucleotide Sequence of the Duck Enteritis Virus (DEV) US2 Gene

2012 ◽  
Vol 424-425 ◽  
pp. 484-487 ◽  
Author(s):  
Jie Gao ◽  
An Chun Cheng ◽  
Ming Shu Wang
Keyword(s):  
Nucleotide Sequence ◽  
Sequence Alignment ◽  
Complete Nucleotide Sequence ◽  
Bioinformatics Analysis ◽  
Duck Enteritis Virus ◽  
Gene Sequences ◽  
Enteritis Virus ◽  
Single Cluster ◽  
Duck Virus Enteritis

It is generally recognised that bioinformatics analysis plays a important role in the study of genes and proteins. Here in this article we intend to report some bioinformation about the nucleotide sequence of DEV(Duck virus enteritis）US2 gene.We used DNAstar7.1 software、Primer5.0 and some online websites (e.g.NCBI、BepiPred)to analysis the potential bioinformation in nucleotide of this gene. These results indicated that DEV should be placed in a single cluster within the subfamily Alphaherpesvirinae,and nucleotide sequence of DEV-CHv US2 have 100% similarities with other six strains of DEV US2 gene sequences with sequence alignment and so on.To date,little information on DEV US2 gene has been found,so we provide some materials information for further research.

Molecular Cloning and Bioinformatics Analysis of K+Transporter Gene (GmKT12) from Soybean (Glycine max[L．] Merri)

2013 ◽  
Vol 39 (9) ◽  
pp. 1701
Author(s):  
Xiao-Ling ZHU ◽  
Hai-Feng CHEN ◽  
Cheng WANG ◽  
Qing-Nan HAO ◽  
Li-Miao CHEN ◽  
...  
Keyword(s):  
Glycine Max ◽  
Molecular Cloning ◽  
Bioinformatics Analysis ◽  
Transporter Gene ◽  
Glycine Max L

Bioinformatics analysis and response to nitrate-cadmium stress of NRT1.5 and NRT1.8 family genes in Brassica napus

2019 ◽  
Vol 45 (3) ◽  
pp. 365 ◽  
Author(s):  
Gui-Hong LIANG ◽  
Ying-Peng HUA ◽  
Ting ZHOU ◽  
Qiong LIAO ◽  
Hai-Xing SONG ◽  
...  
Keyword(s):  
Brassica Napus ◽  
Bioinformatics Analysis ◽  
Cadmium Stress

Cloning and Bioinformatics Analysis of IQM2 cDNA from Arabidopsis

2010 ◽  
Vol 30 (3) ◽  
pp. 353-358
Author(s):  
Yu-Zhong CHEN ◽  
Yu-Ping ZHOU ◽  
Hui YE ◽  
Lin GUI ◽  
Pei-Guo GUO ◽  
...  
Keyword(s):  
Bioinformatics Analysis

Comparative Proteomics and Bioinformatics Analysis of Tissue from Non-Small Cell Lung Cancer Patients

2017 ◽  
Vol 14 (1) ◽  
pp. 58-77
Author(s):  
Sevgi Gezici ◽  
Mehmet Ozaslan ◽  
Gurler Akpinar ◽  
Murat Kasap ◽  
Maruf Sanli ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cancer Patients ◽  
Cell Lung Cancer ◽  
Bioinformatics Analysis ◽  
Comparative Proteomics ◽  
Small Cell ◽  
Small Cell Lung ◽  
Lung Cancer Patients

Bioinformatics Analysis Identifies CPZ as a Tumor Immunology Biomarker For Gastric Cancer

2020 ◽  
Vol 15 ◽  
Author(s):  
Yuan Gu ◽  
Ying Gao ◽  
Xiaodan Tang ◽  
Huizhong Xia ◽  
Kunhe Shi
Keyword(s):  
Gastric Cancer ◽  
T Cells ◽  
Signaling Pathway ◽  
Tumor Immunology ◽  
Bioinformatics Analysis ◽  
Human Cancer ◽  
Disease Free Survival ◽  
Kaplan Meier ◽  
Potential Biomarker ◽  
Pathway Regulation

Background: Gastric cancer (GC) is one of the most common malignancies worldwide. However, the biomarkers for the prognosis and diagnosis of Gastric cancer were still need. Objective: The present study aimed to evaluate whether CPZ could be a potential biomarker for GC. Method: Kaplan-Meier plotter (http://kmplot.com/analysis/) was used to determine the correlation between CPZ expression and overall survival (OS) and disease-free survival (DFS) time in GC [9]. We analyzed CPZ expression in different types of cancer and the correlation of CPZ expression with the abundance of immune infiltrates, including B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells, via gene modules using TIMER Database. Results: The present study identified that CPZ was overexpressed in multiple types of human cancer, including Gastric cancer. We found that overexpression of CPZ correlates to the poor prognosis of patients with STAD. Furthermore, our analyses show that immune infiltration levels and diverse immune marker sets are correlated with levels of CPZ expression in STAD. Bioinformatics analysis revealed that CPZ was involved in regulating multiple pathways, including PI3K-Akt signaling pathway, cGMP-PKG signaling pathway, Rap1 signaling pathway, TGF-beta signaling pathway, regulation of cell adhesion, extracellular matrix organization, collagen fibril organization, collagen catabolic process. Conclusion: This study for the first time provides useful information to understand the potential roles of CPZ in tumor immunology and validate it to be a potential biomarker for GC.

Identification of key mRNAs and lncRNAs involved in the effects of anti-TWEAK on Osteosarcoma

2020 ◽  
Vol 15 ◽  
Author(s):  
Mingxuan Yang ◽  
Liangtao Zhao ◽  
Xuchang Hu ◽  
Haijun Feng ◽  
Xuewen Kang
Keyword(s):  
Dna Replication ◽  
Signaling Pathway ◽  
Bioinformatics Analysis ◽  
Mapk Signaling ◽  
Migration And Invasion ◽  
Type I ◽  
Interferon Signaling ◽  
Nf Kappa B ◽  
Ppi Networks ◽  
Coexpression Networks

Background: Osteosarcoma (OS) is one of the most common primary malignant bone tumors in teenagers. Emerging studies demonstrated TWEAK and Fn14 were involved in regulating cancer cell differentiation, proliferation, apoptosis, migration and invasion. Objective: The present study identified differently expressed mRNAs and lncRNAs after anti-TWEAK treatment in OS cells using GSE41828. Methods: We identified 922 up-regulated mRNAs, 863 downregulated mRNAs, 29 up-regulated lncRNAs, and 58 down-regulated lncRNAs after anti-TWEAK treatment in OS cells. By constructing PPI networks, we identified several key proteins involved in anti-TWEAK treatment in OS cells, including MYC, IL6, CD44, ITGAM, STAT1, CCL5, FN1, PTEN, SPP1, TOP2A, and NCAM1. By constructing lncRNAs coexpression networks, we identified several key lncRNAs, including LINC00623, LINC00944, PSMB8-AS1, LOC101929787. Result: Bioinformatics analysis revealed DEGs after anti-TWEAK treatment in OS were involved in regulating type I interferon signaling pathway, immune response related pathways, telomere organization, chromatin silencing at rDNA, and DNA replication. Bioinformatics analysis revealed differently expressed lncRNAs after antiTWEAK treatment in OS were related to telomere organization, protein heterotetramerization, DNA replication, response to hypoxia, TNF signaling pathway, PI3K-Akt signaling pathway, Focal adhesion, Apoptosis, NF-kappa B signaling pathway, MAPK signaling pathway, FoxO signaling pathway. Conclusion: : This study provided useful information for understanding the mechanisms of TWEAK underlying OS progression and identifying novel therapeutic markers for OS.

Sign in / Sign up

Export Citation Format

Share Document