CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions

Machine Learning-based scoring and classification of genetic variants aids the assessment of clinical findings and is employed to prioritize variants in diverse genetic studies and analyses. Combined Annotation-Dependent Depletion (CADD) is one of the first methods for the genome-wide prioritization of variants across different molecular functions and has been continuously developed and improved since its original publication. Here, we present our most recent release, CADD v1.7. We explored and integrated new annotation features, among them state-of-the-art protein language model scores (Meta ESM-1v), regulatory variant effect predictions (from sequence-based convolutional neural networks) and sequence…

• DF
  Deutsche Forschungsgemeinschaft

  Award: 464313370
• UZ
  Universität zu Lübeck
• BI
  Berlin Institute of Health
• NH
  National Human Genome Research Institute

  Awards: 1UM1HG011966, 1UM1HG011966-01