ISOexpresso

ISOexpresso provides isoform-level expression analysis in human cancer by integrating RNA sequencing data and large-scale cancer databases to evaluate alternative splicing, identify predominant and tumor-specific isoforms, and annotate genomic variants.

Key Features:

• Isoform Existence and Expression Information: Detailed isoform presence and expression levels across conditions such as cancer versus normal states, different cancer types, and tissue-specific contexts to identify tumor-specific isoform signatures and assess impacts of genomic mutations.
• Visualization Tools: Isoform Expression View that visualizes RNA/isoform expression patterns for queried genes to determine predominant isoforms and compare expressed isoforms with canonical isoforms.
• User Data Annotation: Genomic variant annotation that assesses plausible consequences (e.g., amino acid changes) and prioritizes transcripts based on relative expression levels to focus mutation effect predictions on the most expressed isoforms.

Scientific Applications:

• Alternative Splicing in Cancer: Analyze the role of alternative splicing in tumor cell transformation and growth by comparing isoform expression between conditions.
• Identification of Tumor-Specific Isoforms: Identify candidate tumor-specific and predominant isoforms based on condition- and tissue-specific expression patterns and comparisons with canonical isoforms.
• Variant Consequence Prioritization: Prioritize transcripts for variant consequence prediction by using relative isoform expression to assess plausible amino acid changes and mutation effects.
• Validation and Hypothesis Generation: Reproduce known isoform expression patterns — including recurrent major isoforms across tissues, tissue-specific differential expression, and isoform switching during tumorigenesis — to support formulation and confirmation of hypotheses.

Methodology:

Integration of RNA sequencing data with large-scale cancer databases to quantify and visualize isoform expression, annotation of genomic variants, and prioritization of transcripts based on relative expression levels.