How are alternative splicing analyzed?

Alternative splicing is analyzed in three main steps: detection, statistical comparison, and effect prediction. 

1. The first step is to identify alternative splicing events in RNA sequencing data. This typically involves aligning RNA sequencing reads to a reference genome and identifying regions where the reads do not align perfectly, suggesting the presence of alternative splicing. Various computational tools and algorithms, such as MISO, rMATS, or SUPPA, are commonly used for this purpose. These tools can detect different types of alternative splicing events, including exon skipping, alternative 5' or 3' splice site usage and intron retention.
2. Once the alternative splicing events have been detected, the next step is to compare splicing patterns between different conditions or experimental groups. This can involve comparing splicing patterns between normal and disease samples, different developmental stages, or different treatments. Statistical methods such as Fisher's exact test, chi-square test, or regression models are used to assess the significance of differences in splicing patterns between groups. This step helps identify alternative splicing events that are regulated under different conditions.
3. In the third step, the effects of alternative splicing events need to be predicted. This involves analyzing how changes in splicing patterns affect the structure, function, and regulation of the resulting mRNA transcripts and proteins. Computational tools and databases such as ASpedia, SpliceAid 2, or SplicePort can be used to predict the effects of alternative splicing events on protein domains, protein-protein interactions and localization signals. Experimental validation is often required to confirm the predicted effects of alternative splicing on gene expression and cellular function.