DANN

What DANN Is

DANN (Deleterious Annotation of genetic variants using Neural Networks) is a deep neural network trained to distinguish pathogenic from benign variants using a large set of genomic annotations. It uses the same training features as the CADD scoring system but applies a deep learning architecture instead of a linear model, which allows it to capture more complex relationships between features.

Score Interpretation

DANN scores range from 0 to 1, with higher scores indicating a greater likelihood of pathogenicity.

Strengths and Limitations

DANN's primary strength is breadth: it can score any single nucleotide variant in the genome, not just missense variants in coding regions. This makes it useful as a reference for intronic, synonymous, and UTR variants where protein-specific tools like SIFT or AlphaMissense are not applicable.

The main limitation is its wide ambiguous range (0.5 to 0.95), which means many variants receive scores that are neither clearly damaging nor clearly benign. The binary threshold approach may also miss nuanced pathogenicity signals.