Gene Symphony: Two-Layered Musification of Gene Expression Clusters with Functional Backgrounds

Musification, the process of converting data into music, has found applications in scientific research as a novel medium for data representation and communication. However, in biology, its application has largely been limited to DNA and protein sequences or structural data. Existing approaches that attempt to musify gene expression data often do so at the level of the entire gene set or its low-dimensional representations, lacking finer biological context. In our work, we bridge this gap by representing time-series gene expression data through clusters of genes that exhibit similar expression patterns across time points. We then embed contextual biological information onto these clusters to create layered sonifications: a foreground layer capturing expression dynamics across time points through pitch variations, and a background layer reflecting biologically meaningful context, such as Gene Ontology-derived semantic similarity, conveyed through smoothed audio modulated by intensity and echo effects. This musification approach offers an alternative way to analyze traditional visualizations like gene expression heatmaps and dendrograms, providing auditory insight into both temporal and functional trends. Preliminary user survey results indicate that 72.5% of participants were able to correctly interpret gene expression patterns from the foreground melody, and 88.9% perceived differences in background layers, highlighting the effectiveness of the layered musical representation in conveying biologically relevant information. This two-layered design is broadly applicable and can be extended to other forms of musification, allowing different types of information to be conveyed simultaneously through distinct musical components.