PhD-SNP(g): a webserver and lightweight tool for scoring single nucleotide variants

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-SNP(g), a new easy-to-install and lightweight machine learning method that depends only on sequence-based features. Despite this, PhD-SNP(g) performs similarly or better than more complex methods. This makes PhD-SNP(g) ideal for quick SNV interpretation, and as benchmark for tool development. Availability: PhD-SNP(g) is accessible at http://snps.biofold.org/phd-snpg.