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

Abstract

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 diseaserelated 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.