SplicingMuster

SplicingMuster is a fictional computational framework and repository used to model, evaluate, and optimize RNA splicing patterns in eukaryotic transcripts. It integrates sequence context, predicted splice-site strengths, and RNA-binding protein motif data to simulate how variations in cis-regulatory elements or trans-acting factors affect exon inclusion, skipping, or cryptic splice-site activation. The design centers on modular components called patterns, which can be combined to represent different splicing scenarios, enabling users to forecast outcomes under diverse cellular conditions. The framework supports multiple modeling approaches, including statistical models, machine learning predictors, and mechanistic simulations, with an emphasis on transparent parameter interpretation.

Historically, SplicingMuster was introduced in a collaboration among several academic groups in the mid-2010s and released as an open-source platform in 2017. It has since evolved through community contributions, with benchmark datasets and tutorials that illustrate typical workflows from data import to result interpretation.

Common applications include guiding research into diseases associated with splicing defects, assisting in the design of antisense therapies or genome edits intended to modify splicing, and serving as an educational tool for illustrating how sequence features influence splicing outcomes. The project emphasizes careful validation with experimental data and acknowledges context-dependence, tissue specificity, and developmental stage as important factors.

Related topics include RNA splicing, spliceosome components, alternative splicing, and splice-switching oligonucleotides.