Novel implementation of conditional co-regulation by graph theory to derive co-expressed genes from microarray data

Background: Most existing transcriptional databases like Comprehensive Systems-Biology Database (CSB.DB) and Arabidopsis Microarray Database and Analysis Toolbox (GENEVESTIGATOR) help to seek a shared biological role (similar pathways and biosynthetic cycles) based on correlation. These utilize conventional methods like Pearson correlation and Spearman rank correlation to calculate correlation among genes. However, not all are genes expressed in all the conditions and this leads to their exclusion in these transcriptional databases that consist of experiments performed in varied conditions. This leads to incomplete studies of coregulation among groups of genes that might be linked to the same or related biosynthetic pathway. Results: We have implemented an alternate method based on graph theory that takes into consideration the biological assumption

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Arun Rawat, Georg J. Seifert, Youping Deng
• Keywords: BMCBI 2008, Databases, Pearson Correlation, Transcriptional Databases