ValidSpliceMut

ValidSpliceMut predicts the impact of genomic variants on mRNA splicing using information theory-based methods to identify changes in natural and cryptic splice site strength and resultant splice isoform outcomes.

Key Features:

• Prediction Accuracy: Uses information theory-based methods to predict changes in natural and cryptic splice site strength caused by single nucleotide polymorphisms (SNPs).
• Comprehensive Analysis: Integrates RNA sequencing (RNAseq) data and genomic resources including The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC).
• Validation with Experimental Data: Predictions have been corroborated by gene expression microarrays, custom q-RT-PCR, and TaqMan assays in HapMap lymphoblastoid cell lines.
• Identification of Splicing Outcomes: Identifies exon inclusion, cryptic splice site usage, intron retention, and allele-specific alternative splicing events.
• Complementary Resolution of Rare Isoforms: Notes that q-RT-PCR can resolve rare splice isoforms that may have low read abundance in RNAseq analyses.

Scientific Applications:

• Variant interpretation in genetic disease: Assesses how SNPs and other genomic variants alter splice site strength and contribute to molecular mechanisms of common and rare diseases.
• Cancer genomics: Analyzes splice-altering variants identified in TCGA and ICGC datasets to link somatic and germline variants to altered splice isoform profiles in cancer.
• Functional genomics of splicing: Enables investigation of allele-specific alternative splicing and its effects on gene expression and transcript structure.

Methodology:

Applies information theory-based models to predict splice site strength changes and integrates TCGA and ICGC RNAseq datasets for analysis of splice isoform abundance.