SBSA

SBSA annotates altered binding motifs and sequences caused by genomic variants to elucidate molecular regulatory mechanisms.

Key Features:

• Altered motif and sequence annotation: Annotates altered binding motifs and sequences in genomic variants.
• Supported molecular regulators: Analyzes transcription factors (TFs), RNA-binding proteins, miRNA seeds, and miRNA-mRNA 3'-UTR binding targets.
• Custom motifs and sequences: Accepts user-provided custom motifs and sequences for annotation.
• Personalized genome support: Implements a personalized genome approach that accommodates joint adjacent variants.
• Species and reference genomes: Supports an indefinite number of species with preloaded reference genomes including SARS-CoV-2 and 25 other organisms.
• Large-scale validation: Validated by annotating multi-omics data from over 30,890 human subjects.
• High-throughput discovery: Identified millions of somatic binding sequences across analyzed datasets.
• Experimentally supported finding: Annotated a somatic mutation in the TERT promoter that creates an acquired ETS1 binding sequence, experimentally validated in cancer cells.

Scientific Applications:

• Somatic mutation analysis: Characterizes how somatic mutations alter binding sites and regulatory interactions.
• Germline variant and RNA editing analysis: Assesses effects of single nucleotide polymorphisms and RNA editing on binding motifs.
• Regulatory factor studies: Investigates TFs, RNA-binding proteins, miRNA seeds, and miRNA-mRNA 3'-UTR targeting impacts.
• Functional genomics and cancer research: Identifies functionally relevant binding sequence changes with implications for cancer, exemplified by the TERT–ETS1 event.
• Complex variant interaction analysis: Enables analysis of joint adjacent variants to study complex genetic interactions.

Methodology:

Annotates altered binding motifs and sequences in genomic variants, supports custom motifs and sequences, applies a personalized genome approach to accommodate joint adjacent variants, and annotates multi-omics datasets.