AStalavista

AStalavista visualizes and analyzes alternative splicing events from transcript annotations and RNA-seq data to quantify transcript isoforms and assess splicing-ratio variability.

Key Features:

• Alternative Splicing Visualization: Retrieves and visualizes all alternative splicing events from generic transcript annotations.
• Quantification of Isoform Ratios: Quantifies individual transcript isoforms and estimates relative splicing ratios within genes using RNA-seq data.
• Statistical Methodology: Measures and compares variability in splicing ratios within and between conditions to identify genes with condition-specific splicing ratios.
• Deconvolution of Variability Sources: Deconvolutes the relative contributions of gene expression variability and splicing-ratio variability to overall transcript abundance variability.
• Population-Specific Analysis: Applies to RNA-seq data from populations (for example, lymphoblastoid cells from Caucasian and Yoruban individuals) to detect population-specific splicing ratios, reported in up to 10% of protein-coding genes.
• Phenotypic Implications: Highlights changes in splicing ratios that occur without overall gene expression changes to indicate potential phenotypic effects of alternative splicing.

Scientific Applications:

• Transcriptome-Wide Analysis: Performs transcriptome-wide studies of gene expression and splicing variability, complementing DNA array technologies and RNA-seq methodologies.
• Comparative Genomics: Facilitates comparative analyses of splicing events between conditions or populations to identify condition-specific splicing.
• Functional Genomics: Dissects contributions of transcriptional and splicing variability to transcript abundance to support studies of gene regulation mechanisms.

Methodology:

AStalavista applies a statistical framework to RNA-seq data to quantify and compare alternative splice forms, measure splicing-ratio variability within conditions, identify genes with distinct splicing patterns between groups, and deconvolute sources of transcript abundance variability.