DBASS

DBASS catalogs aberrant 5'- and 3'-splice sites induced by pathogenic mutations in human disease genes to support analysis of mutation-induced exon boundaries.

Key Features:

• Scope: Approximately 900 records documenting cryptic and de novo splice sites generated by over a thousand distinct mutations across roughly 360 genes.
• Aberrant splice-site types: Catalogues both cryptic splice sites (activated by mutations within consensus exon–intron junctions) and de novo splice sites (created by mutations outside consensus sequences).
• Exon-boundary annotation: Records new exon boundaries and the positions of aberrant splice sites relative to authentic counterparts.
• Reference sequences: Provides reference sequences for both aberrant and authentic splice sites together with associated mutation sequences.
• Mutation annotation: Stores specific mutation details including location within introns or exons and proximity to authentic splice sites.
• Indexed parameters: Data are indexed by disease phenotype, gene name, mutation details, intron/exon location, proximity to authentic sites, and bibliographic references.
• Splice-site strength: Includes splice-site strength estimates produced by multiple prediction algorithms.
• Bibliographic support: Links each record to literature references describing the underlying pathogenic mutations and splicing events.

Scientific Applications:

• Identification of splicing patterns: Enables analysis of introns and exons frequently involved in aberrant splicing events.
• Mutation analysis: Supports evaluation of how specific pathogenic mutations activate cryptic or de novo splice sites and alter exon inclusion.
• Algorithm development: Provides curated examples for training or benchmarking splice-site prediction algorithms.
• Auxiliary signal studies: Facilitates definition of auxiliary splicing signals contributing to RNA processing beyond canonical sites.
• Reporter assay design: Supplies sequence and mutation contexts useful for designing reporter constructs to study splicing mechanisms.

Methodology:

Splice-site strengths are estimated using multiple splice-site prediction algorithms.