scholarly journals Altered Pathway Analyzer: A gene expression dataset analysis tool for identification and prioritization of differentially regulated and network rewired pathways

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Abhinav Kaushik ◽  
Shakir Ali ◽  
Dinesh Gupta
Keyword(s):  
Gene Expression ◽  
Gene Expression Dataset ◽  
Analysis Tool ◽  
Dataset Analysis ◽  
Altered Pathway

scholarly journals Memory Based Cuckoo Search Algorithm for Feature Selection of Gene Expression Dataset

2021 ◽  
pp. 100572
Author(s):  
Malek Alzaqebah ◽  
Khaoula Briki ◽  
Nashat Alrefai ◽  
Sami Brini ◽  
Sana Jawarneh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Gene Expression Dataset ◽  
Selection Of

scholarly journals Automated Multi-Dataset Analysis (AMDA): an on-line database and analysis tool for heliospheric and planetary plasma data

2021 ◽  
pp. 105214
Author(s):  
V. Génot ◽  
E. Budnik ◽  
C. Jacquey ◽  
M. Bouchemit ◽  
B. Renard ◽  
...  
Keyword(s):  
Analysis Tool ◽  
On Line ◽  
Dataset Analysis

Identification of CCNB2 expression in triple-negative breast cancer based on bioinformatics results

2021 ◽  
Author(s):  
jintao cao ◽  
SHUAI SUN ◽  
RAN LI ◽  
RUI MIN ◽  
XINGYU FAN ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Triple Negative Breast Cancer ◽  
Protein Complex ◽  
Triple Negative ◽  
Expression Profiles ◽  
Pathway Enrichment Analysis ◽  
Analysis Tool ◽  
The Core ◽  
Core Genes

Abstract Background The current epidemiology shows that the incidence of breast cancer is increasing year by year and tends to be younger. Triple-negative breast cancer is the most malignant of breast cancer subtypes. The application of bioinformatics in tumor research is becoming more and more extensive. This study provided research ideas and basis for exploring the potential targets of gene therapy for triple-negative breast cancer (TNBC). Methods We analyzed three gene expression profiles (GSE64790、GSE62931、GSE38959) selected from the Gene Expression Omnibus (GEO) database. The GEO2R online analysis tool was used to screen for differentially expressed genes (DEGs) between TNBC and normal tissues. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were applied to identify the pathways and functional annotation of DEGs. Protein–protein interaction network of these DEGs were visualized by the Metascape gene-list analysis tool so that we could find the protein complex containing the core genes. Subsequently, we investigated the transcriptional data of the core genes in patients with breast cancer from the Oncomine database. Moreover, the online Kaplan–Meier plotter survival analysis tool was used to evaluate the prognostic value of core genes expression in TNBC patients. Finally, immunohistochemistry (IHC) was used to evaluated the expression level and subcellular localization of CCNB2 on TNBC tissues. Results A total of 66 DEGs were identified, including 33 up-regulated genes and 33 down-regulated genes. Among them, a potential protein complex containing five core genes was screened out. The high expression of these core genes was correlated to the poor prognosis of patients suffering breast cancer, especially the overexpression of CCNB2. CCNB2 protein positively expressed in the cytoplasm, and its expression in triple-negative breast cancer tissues was significantly higher than that in adjacent tissues. Conclusions CCNB2 may play a crucial role in the development of TNBC and has the potential as a prognostic biomarker of TNBC.

scholarly journals TriPOINT: a software tool to prioritize important genes in pathways and their non-coding regulators

2018 ◽  
Vol 35 (15) ◽  
pp. 2686-2689
Author(s):  
Asa Thibodeau ◽  
Dong-Guk Shin
Keyword(s):  
Gene Expression ◽  
Software Tool ◽  
Supplementary Information ◽  
Analysis Tool ◽  
Graph Representations ◽  
Expression Levels ◽  
Conducting Pathway ◽  
Pathway Analysis Tool ◽  
Pathway Analyses ◽  
Gene Expression Levels

Abstract Summary Current approaches for pathway analyses focus on representing gene expression levels on graph representations of pathways and conducting pathway enrichment among differentially expressed genes. However, gene expression levels by themselves do not reflect the overall picture as non-coding factors play an important role to regulate gene expression. To incorporate these non-coding factors into pathway analyses and to systematically prioritize genes in a pathway we introduce a new software: Triangulation of Perturbation Origins and Identification of Non-Coding Targets. Triangulation of Perturbation Origins and Identification of Non-Coding Targets is a pathway analysis tool, implemented in Java that identifies the significance of a gene under a condition (e.g. a disease phenotype) by studying graph representations of pathways, analyzing upstream and downstream gene interactions and integrating non-coding regions that may be regulating gene expression levels. Availability and implementation The TriPOINT open source software is freely available at https://github.uconn.edu/ajt06004/TriPOINT under the GPL v3.0 license. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Profiling Cellular Processes in Adipose Tissue during Weight Loss Using Time Series Gene Expression

Genes ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 525 ◽  
Author(s):  
Samar Tareen ◽  
Michiel Adriaens ◽  
Ilja Arts ◽  
Theo de Kok ◽  
Roel Vink ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Weight Loss ◽  
Adipose Tissue ◽  
Molecular Mechanisms ◽  
Dietary Intervention ◽  
Network Biology ◽  
Analysis Tool ◽  
Cellular Processes ◽  
Time Series Gene Expression

Obesity is a global epidemic identified as a major risk factor for multiple chronic diseases and, consequently, diet-induced weight loss is used to counter obesity. The adipose tissue is the primary tissue affected in diet-induced weight loss, yet the underlying molecular mechanisms and changes are not completely deciphered. In this study, we present a network biology analysis workflow which enables the profiling of the cellular processes affected by weight loss in the subcutaneous adipose tissue. Time series gene expression data from a dietary intervention dataset with two diets was analysed. Differentially expressed genes were used to generate co-expression networks using a method that capitalises on the repeat measurements in the data and finds correlations between gene expression changes over time. Using the network analysis tool Cytoscape, an overlap network of conserved components in the co-expression networks was constructed, clustered on topology to find densely correlated genes, and analysed using Gene Ontology enrichment analysis. We found five clusters involved in key metabolic processes, but also adipose tissue development and tissue remodelling processes were enriched. In conclusion, we present a flexible network biology workflow for finding important processes and relevant genes associated with weight loss, using a time series co-expression network approach that is robust towards the high inter-individual variation in humans.

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