ArticleScientific reports2015
A short treatise concerning a musical approach for the interpretation of gene expression data.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Integrated Analysis of Gene Expression, Protein Synthesis, and Epigenetic Modifications in Alcanivorax borkumensis SK2 Under Iron Limitation.Environmental microbiology reports · 2025Article
- Article
- Chemical composition: Hearing insect defensive volatiles.Patterns (New York, N.Y.) · 2021Article
- Real-time audio and visual display of the Coronavirus genome.BMC bioinformatics · 2020Article
- Diffusions of sound frequencies designed to target dehydrins induce hydric stress tolerance inHeliyon · 2020Article
- An auditory display tool for DNA sequence analysis.BMC bioinformatics · 2017Article
- Polyphonic sonification of electrocardiography signals for diagnosis of cardiac pathologies.Scientific reports · 2017Article
- Melody discrimination and protein fold classification.Heliyon · 2016Article
- Science and Culture: Musical genes.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Gene Expression Music Algorithm-Based Characterization of the Ewing Sarcoma Stem Cell Signature.Stem cells international · 2016Article
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Abstract
Recent technical developments allow the genome-wide and near-complete analysis of gene expression in a given sample, e.g. by usage of high-density DNA microarrays or next generation sequencing. The generated data structure is usually multi-dimensional and requires extensive processing not only for analysis but also for presentation of the results. Today, such data are usually presented graphically, e.g. in the form of heat maps. In the present paper, we propose an alternative form of analysis and presentation which is based on the transformation of gene expression data into sounds that are characterized by their frequency (pitch) and tone duration. Using DNA microarray data from a panel of neuroblastoma and Ewing sarcoma cell lines as well as from Hodgkin's lymphoma cell lines and normal B cells, we demonstrate that this Gene Expression Music Algorithm (GEMusicA) can be used for discrimination between samples with different biology and for the characterization of differentially expressed genes.
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