DANN: a deep learning approach for annotating the pathogenicity of genetic variants

Bioinformatics. 2015 Mar 1;31(5):761-3. doi: 10.1093/bioinformatics/btu703. Epub 2014 Oct 22.


Annotating genetic variants, especially non-coding variants, for the purpose of identifying pathogenic variants remains a challenge. Combined annotation-dependent depletion (CADD) is an algorithm designed to annotate both coding and non-coding variants, and has been shown to outperform other annotation algorithms. CADD trains a linear kernel support vector machine (SVM) to differentiate evolutionarily derived, likely benign, alleles from simulated, likely deleterious, variants. However, SVMs cannot capture non-linear relationships among the features, which can limit performance. To address this issue, we have developed DANN. DANN uses the same feature set and training data as CADD to train a deep neural network (DNN). DNNs can capture non-linear relationships among features and are better suited than SVMs for problems with a large number of samples and features. We exploit Compute Unified Device Architecture-compatible graphics processing units and deep learning techniques such as dropout and momentum training to accelerate the DNN training. DANN achieves about a 19% relative reduction in the error rate and about a 14% relative increase in the area under the curve (AUC) metric over CADD's SVM methodology.

Availability and implementation: All data and source code are available at https://cbcl.ics.uci.edu/public_data/DANN/.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms*
  • Area Under Curve
  • Computer Graphics
  • Genetic Variation / genetics*
  • Genome, Human*
  • Humans
  • Molecular Sequence Annotation*
  • Neural Networks, Computer*
  • Selection, Genetic
  • Support Vector Machine