PhD-SNPg: a webserver and lightweight tool for scoring single nucleotide variants

Nucleic Acids Res. 2017 Jul 3;45(W1):W247-W252. doi: 10.1093/nar/gkx369.


One of the major challenges in human genetics is to identify functional effects of coding and non-coding single nucleotide variants (SNVs). In the past, several methods have been developed to identify disease-related single amino acid changes but only few tools are able to score the impact of non-coding variants. Among the most popular algorithms, CADD and FATHMM predict the effect of SNVs in non-coding regions combining sequence conservation with several functional features derived from the ENCODE project data. Thus, to run CADD or FATHMM locally, the installation process requires to download a large set of pre-calculated information. To facilitate the process of variant annotation we develop PhD-SNPg, a new easy-to-install and lightweight machine learning method that depends only on sequence-based features. Despite this, PhD-SNPg performs similarly or better than more complex methods. This makes PhD-SNPg ideal for quick SNV interpretation, and as benchmark for tool development.

Availability: PhD-SNPg is accessible at

Publication types

  • Evaluation Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Genetic Variation*
  • Humans
  • Internet
  • Machine Learning
  • Sequence Analysis
  • Software*
  • User-Computer Interface