Systems and methods for analysis of alternative splicing

A computer-implemented system for quantifying functional impact of alternative splicing events on protein structures, protein functions, RNA stability, RNA integrity, or biological pathways comprising:

• a digital processing device comprising:
• a processor, an operating system configured to perform executable instructions, a memory, and a computer program including instructions executable by the digital processing device to create an alternative splicing functional impact analysis application, the application comprising a software module for:
• (a) generating a plurality of features based on information stored in a database, wherein the information comprises metadata obtained from annotations of a plurality of types of alternative splicing based on public RNA-seq data or other biological data;
• (b) obtaining one or more alternative splicing events;
• (c) quantitatively estimating probabilities of the one or more alternative splicing events of damaging the protein structures, protein functions, RNA stability, RNA integrity, or biological pathways based on the plurality of features;
• (d) applying a supervised or semi-supervised machine learning algorithm to predict the functional impact of the one or more alternative splicing events based on the estimated probabilities;
• and
• (e) generating a list of prioritized and biologically relevant alternative splicing events based on prediction of the functional impact of the one or more alternative splicing events.

A computer-implemented method for quantifying a functional impact of alternative splicing events on protein structures, protein functions, RNA stability, RNA integrity, or biological pathways comprising:

• (a) generating a plurality of features based on information stored in a database, wherein the information comprises metadata obtained from annotations of a plurality of types of alternative splicing based on public RNA-seq data or other biological data;
• (b) obtaining one or more alternative splicing events;
• (c) quantitatively estimating probabilities of the one or more alternative splicing events of damaging the protein structures, protein functions, RNA stability, RNA integrity, or biological pathways based on the plurality of features;
• (d) applying a supervised or semi-supervised machine learning algorithm to predict the functional impact of the one or more alternative splicing events based on the estimated probabilities;
• and
• (e) generating a list of prioritized and biologically relevant alternative splicing events based on prediction of the functional impact of the one or more alternative splicing events.

A computer-implemented method for identifying a disease-specific exon duo or exon trio comprising:

• (a) receiving disease associated gene sequencing data from a source;
• (b) differentiating known from novel annotations wherein the frequency, coverage, and source are extracted;
• (c) assigning a reliability score to the disease-specific exon duo or exon trio based on the known annotations;
• (d) sorting the annotations based on inclusion or skipping states;
• (e) outputting a list of predicted exon duos and/or exon trios.