MeSHer: identifying biological concepts in microarray assays based on PubMed references and MeSH terms

Area of Science:

• Bioinformatics
• Gene Expression Analysis
• Systems Biology

Background:

• Interpreting large gene sets from microarray data is challenging.
• Identifying overrepresented biological concepts can provide functional insights.
• Medical Subject Headings (MeSH) offer a standardized vocabulary for biological concepts.

Purpose of the Study:

• To introduce MeSHer, a novel statistical approach for identifying biological concepts in gene sets.
• To demonstrate MeSHer's utility in interpreting gene expression data from microarray experiments.
• To highlight MeSHer's ability to find significantly overrepresented MeSH terms within a gene set.

Main Methods:

• MeSHer employs a statistical method to detect overrepresented MeSH terms in a gene set compared to a background gene collection.
• The approach utilizes gene lists from the PubMed database and associated MeSH terms.
• A published study on angiotensin II (Ang II) treatment effects on cardiac gene expression was used for demonstration.

Main Results:

• MeSHer successfully identified significantly overrepresented MeSH terms in the Ang II study gene set.
• The identified terms provided biological context and aided in the interpretation of the gene expression changes.
• The approach demonstrated its capability to highlight key biological pathways affected by the treatment.

Conclusions:

• MeSHer is an effective tool for identifying and interpreting biological concepts within gene sets.
• The statistical approach aids researchers in understanding the functional implications of significant gene expression changes.
• MeSHer facilitates the interpretation of microarray data by linking gene lists to established biological knowledge.