TIN

TIN analyzes exon-level microarray expression to quantify transcriptome instability by estimating aberrant exon usage and relating it to splicing factor expression.

Key Features:

• Exon-Level Analysis: Utilizes exon-level microarray expression profiles to estimate aberrant exon usage across samples.
• Splicing Factor Correlation: Computes correlations between transcriptome instability measures and splicing factor expression levels.
• Comparative Statistical Assessment: Compares observed alternative splicing scores and expression patterns to random permutations of alternative splicing scores and to expressions from random gene sets.
• Novel Isoform and Aberrant Splicing Detection: Leverages high-throughput expression data to detect novel mRNA isoforms and genome-wide aberrant splicing patterns.

Scientific Applications:

• Cancer Research: Investigates transcriptome instability as a genome-wide characteristic in certain solid cancers, analogous to chromosomal and microsatellite instabilities, to study splicing-related genomic features.
• Disease Mechanisms: Identifies genome-scale aberrant splicing patterns and novel mRNA isoforms to support studies of diseases involving disrupted pre-mRNA splicing.

Methodology:

Implemented as an R package, TIN uses exon-level microarray expression and high-throughput expression data to estimate aberrant exon usage, detect novel mRNA isoforms and aberrant splicing patterns, compute correlations with splicing factor expression, and compare results to random permutations of alternative splicing scores and to expressions from random gene sets.