Human Gene Correlation Analysis (HGCA): a Tool for the Identification of Transcriptionally Co-expressed Genes

Overview

Journal BMC Res Notes

Publisher Biomed Central

Specialties Biology
General Medicine

Date 2012 Jun 8

PMID 22672625

Citations 19

Authors

Ioannis Michalopoulos

Georgios A Pavlopoulos

Apostolos Malatras

Alexandros Karelas

Myrto-Areti Kostadima

Reinhard Schneider

Sophia Kossida

Affiliations

Soon will be listed here.

Abstract

Background: Bioinformatics and high-throughput technologies such as microarray studies allow the measure of the expression levels of large numbers of genes simultaneously, thus helping us to understand the molecular mechanisms of various biological processes in a cell.

Findings: We calculate the Pearson Correlation Coefficient (r-value) between probe set signal values from Affymetrix Human Genome Microarray samples and cluster the human genes according to the r-value correlation matrix using the Neighbour Joining (NJ) clustering method. A hyper-geometric distribution is applied on the text annotations of the probe sets to quantify the term overrepresentations. The aim of the tool is the identification of closely correlated genes for a given gene of interest and/or the prediction of its biological function, which is based on the annotations of the respective gene cluster.

Conclusion: Human Gene Correlation Analysis (HGCA) is a tool to classify human genes according to their coexpression levels and to identify overrepresented annotation terms in correlated gene groups. It is available at: http://biobank-informatics.bioacademy.gr/coexpression/.

Citing Articles

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Density-based detection of cell transition states to construct disparate and bifurcating trajectories.

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Approaches in Gene Coexpression Analysis in Eukaryotes.

Zogopoulos V, Saxami G, Malatras A, Papadopoulos K, Tsotra I, Iconomidou V Biology (Basel). 2022; 11(7).

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Analysis of ROQUIN, Tristetraprolin (TTP), and BDNF/miR-16/TTP regulatory axis in late onset Alzheimer's disease.

Asadi M, Talebi M, Gharesouran J, Sabaie H, Jalaiei A, Arsang-Jang S Front Aging Neurosci. 2022; 14:933019.

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Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review.

Baltoumas F, Zafeiropoulou S, Karatzas E, Koutrouli M, Thanati F, Voutsadaki K Biomolecules. 2021; 11(8).

PMID: 34439912 PMC: 8391349. DOI: 10.3390/biom11081245.

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