Better prediction of functional effects for sequence variants

Elucidating the effects of naturally occurring genetic variation is one of the major challenges for personalized health and personalized medicine. Here, we introduce SNAP2, a novel neural network based classifier that improves over the state-of-the-art in distinguishing between effect and neutral variants. Our method's improved performance results from screening many potentially relevant protein features and from refining our development data sets. Cross-validated on >100k experimentally annotated variants, SNAP2 significantly outperformed other methods, attaining a two-state accuracy (effect/neutral) of 83%. SNAP2 also outperformed combinations of other methods. Performance increased for human variants but…

• AV
  Alexander von Humboldt-Stiftung
• DF
  Deutsche Forschungsgemeinschaft
• BF
  Bundesministerium für Bildung und Forschung
• TU
  Technische Universität München