McSplicer

McSplicer estimates splice site usage from RNA-seq data using a probabilistic model to quantify individual splice site usage and characterize alternative splicing and effects of splicing mutations.

Key Features:

• Probabilistic model: Employs a simplified probabilistic model that focuses on individual splice site usage rather than inferring full-length transcripts or predefined local splicing events.
• Parameter estimation from reads: Estimates model parameters using comprehensive RNA-seq read data.
• Generation of complex patterns: Reconstructs complex splicing patterns by combining estimated usage probabilities of individual splice sites.
• Compact interpretable parameters: Describes multiple effects of splicing mutations using a limited number of straightforwardly interpretable parameters.
• Application to disease RNA-seq: Demonstrated on RNA-seq data from patients with autism spectrum disorder to illustrate impacts of splicing mutations.
• Implementation: Implemented in Python.

Scientific Applications:

• Splice site quantification: Quantifying usage of individual splice sites from RNA-seq experiments.
• Mutation impact analysis: Interpreting and characterizing the effects of splicing mutations on gene expression.
• Alternative splicing studies: Studying alternative splicing complexity across conditions and developmental stages.
• Patient cohort analysis: Analyzing patient RNA-seq datasets, exemplified by autism spectrum disorder studies.

Methodology:

Estimate parameters of a probabilistic model from comprehensive RNA-seq read data and use the resulting splice site usage probabilities to generate complex splicing patterns.