MyoMiner: explore gene co-expression in normal and pathological muscle

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• High-throughput transcriptomics measures mRNA levels for thousands of genes.
• Gene co-expression analysis identifies genes with correlated expression patterns within biological conditions.
• Existing gene co-expression databases are often generic and lack tissue-specific context.

Purpose of the Study:

• To create a muscle-specific gene co-expression database.
• To enable the study of gene co-expression in both normal and pathological muscle states.
• To facilitate the discovery of novel muscle-related genes and pathways.

Main Methods:

• Acquired and curated publicly available gene expression data for human and mouse striated muscle samples.
• Categorized data based on species, tissue origin, age, gender, anatomic part, and experimental condition.
• Calculated co-expression values to build the MyoMiner database.

Main Results:

• MyoMiner provides a web interface to retrieve co-expressed genes for a selected gene of interest within specific categories.
• Offers measures of correlation strength (adjusted p-value, confidence intervals) and standardized scatterplots for gene pairs.
• Includes a network interface for visualizing gene correlation networks and a comparison tool for analyzing differences in co-expression between conditions.

Conclusions:

• MyoMiner aids in delineating tissue-, cell-, and pathology-specific muscle gene interactions, signaling, and regulation.
• Changes in co-expression between healthy and pathologic states can reveal new disease mechanisms and therapeutic targets.
• MyoMiner serves as a valuable resource for discovering muscle-associated genes and functions through co-expression analysis.