CanIsoNet

CanIsoNet provides a database integrating isoform expression with STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) protein–protein interaction networks and ClinVar annotations to analyze dominant isoform switching events and their potential impacts on protein interaction networks in diseases, particularly cancer.

Key Features:

• Integration of data sources: Combines isoform expression data with STRING protein–protein interaction networks and ClinVar annotations for joint analysis of splicing and clinical variation.
• Comprehensive dataset: Contains information on 11,811 isoforms and 14,357 unique isoform switching events across 31 disease types.
• Network density scoring: Provides a network density score for each disease-specific isoform to quantify its interaction context.
• PFAM domain annotations: Maps PFAM domain IDs to disrupted interactions to indicate domain-level interaction changes.
• Transcript domain visualizations: Includes domain structure visualizations of transcripts to display location of affected domains.
• Per-sample expression data: Supplies expression measurements for switched isoforms in each sample to assess splicing dynamics.
• ClinVar annotation in networks: Highlights genes annotated in ClinVar within interaction networks to link isoform switches to clinical variants.

Scientific Applications:

• Alternative splicing research: Enables analysis of dominant isoform switching events to investigate alternative splicing alterations in disease contexts.
• Protein interaction disruption studies: Facilitates identification of protein–protein interactions potentially disrupted by isoform switching via STRING integration and PFAM mapping.
• Clinical variant prioritization: Supports prioritization of clinically relevant splicing alterations by integrating ClinVar annotations with isoform and network data.

Methodology:

Integration of isoform expression data with STRING protein–protein interaction networks and ClinVar annotations, computation of network density scores, mapping of PFAM domain IDs to disrupted interactions, and generation of transcript domain structure visualizations along with per-sample expression data.