Non-coding RNAs represent the largest part of transcribed mammalian genomes and prevalently exert regulatory functions. Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) can modulate the activity of each other. Skeletal muscle is the most abundant tissue in mammals. It is composed of different cell types with myofibers that represent the smallest complete contractile system. Considering that lncRNAs and miRNAs are more cell type-specific than coding RNAs, to understand their function it is imperative to evaluate their expression and action within single myofibers. In this database, we collected gene expression data for coding and non-coding genes in single myofibers and used them to produce interaction networks based on expression correlations. Since biological pathways are more informative than networks based on gene expression correlation, to understand how altered genes participate in the studied phenotype, we integrated KEGG pathways with miRNAs and lncRNAs. The database also integrates single nucleus gene expression data on skeletal muscle in different patho-physiological conditions. We demonstrated that these networks can serve as a framework from which to dissect new miRNA and lncRNA functions to experimentally validate. Some interactions included in the database have been previously experimentally validated using high throughput methods. These can be the basis for further functional studies. Using database information, we demonstrate the involvement of miR-149, -214 and let-7e in mitochondria shaping; the ability of the lncRNA Pvt1 to mitigate the action of miR-27a via sponging; and the regulatory activity of miR-214 on Sox6 and Slc16a3. The MyoData is available at https://myodata.bio.unipd.it.
