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

被引：173

作者：

Capriotti, Emidio ^{[1
]}

Fariselli, Piero ^{[2
]}

机构：

[1] Univ Bologna, Dept Biol Geol & Environm Sci BiGeA, Via F Selmi 3, I-40126 Bologna, Italy

[2] Univ Padua, Dept Comparat Biomed & Food Sci, Viale Univ 16, I-35020 Legnaro, PD, Italy

来源：

NUCLEIC ACIDS RESEARCH | 2017年 / 45卷 / W1期

关键词：

SEQUENCE; BIOINFORMATICS;

D O I：

10.1093/nar/gkx369

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

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 http://snps.biofold.org/phdsnpg.

引用

页码：W247 / W252

页数：6

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