DATABASES Human Mutation dbNSFP: A Lightweight Database of Human Nonsynonymous SNPs and Their Functional Predictions Xiaoming Liu, Xueqiu Jian, and Eric Boerwinkle Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas Communicated by George Patrinos Received 23 February 2011; accepted revised manuscript 13 April 2011. Published online 21 April 2011 in Wiley Online Library (www.wiley.com/humanmutation). DOI 10.1002/humu.21517 OFFICIAL JOURNAL www.hgvs.org ABSTRACT: With the advance of sequencing technologies, whole exome sequencing has increasingly been used to identify mutations that cause human diseases, especially rare Mendelian diseases. Among the analysis steps, functional prediction (of being deleterious) plays an important role in filtering or prioritizing nonsynonymous SNP (NS) for further analysis. Unfortunately, different prediction algorithms use different information and each has its own strength and weakness. It has been suggested that investigators should use predictions from multiple algorithms instead of relying on a single one. However, querying predictions from different databases/Web-servers for different algorithms is both tedious and time consum- ing, especially when dealing with a huge number of NSs identified by exome sequencing. To facilitate the process, we developed dbNSFP (database for nonsynonymous SNPs’ functional predictions). It compiles prediction scores from four new and popular algorithms (SIFT, Polyphen2, LRT, and MutationTaster), along with a conservation score (PhyloP) and other related information, for every potential NS in the human genome (a total of 75,931,005). It is the first integrated database of functional predictions from multiple algorithms for the comprehensive collection of human NSs. dbNSFP is freely available for download at http://sites.google.com/site/jpopgen/dbNSFP. Hum Mutat 32:894–899, 2011. & 2011 Wiley-Liss, Inc. KEY WORDS: dbNSFP; functional prediction; database; SIFT; Polyphen2; LRT; MutationTaster; PhyloP Introduction A nonsynonymous SNP (NS) is a single nucleotide variant that causes an amino acid change of its corresponding protein. Because of their direct link to protein changes, NSs are believed to be the major contributor to heritable human diseases among all types of variants in the human genome. As evidence, NSs constitute more than half of the entries of disease-causing genetic changes in the Human Gene Mutation Database (HGMD) [Stenson et al., 2009]. Additional Supporting Information may be found in the online version of this article. Correspondence to: Xiaoming Liu, Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, 1200 Herman Pressler Drive, E529, Houston, Texas 77030. E-mail: xiaoming.liu@uth.tmc.edu Contract grant sponsor: The National Institutes of Health; Contract grant numbers: RC2-HL02419-01; RC2 HL103010-01; 1U01HG005728-01. Based on current technologies, it is too costly and time- consuming to investigate the functional effect of every NS experimentally. Fortunately, there are algorithms available to help to predict the potential of a NS being functional or deleterious. Functional prediction algorithms for NS have been developed in recent years with the advance of phylogenetics, structure biology, bioinformatics and population genetics (see reviews by [Horner et al., 2010; Karchin, 2009; Ng and Henikoff, 2006; Thusberg and Vihinen, 2009]). Usually, the algorithms output a score measuring how likely an NS is deleterious, along with its binary prediction. Functional predictions play an important role in sequencing based genotype–phenotype association analyses. Prediction scores have been used to weight different NSs in order to increase the power of detecting genes influencing a trait [Price et al., 2010]. More importantly, functional prediction has become an indispensable step in identifying genes causing rare Mendelian diseases using an exome-sequencing approach [Cooper et al., 2010; Ng et al., 2010a,b,c]. Functional predictions or conservation information are used to filter or prioritize the novel NSs for further analysis or confirmation [Ng et al., 2010]. Because different algorithms use different information and are based on different training data, each has its own strength and weakness. It has been suggested to use the outputs from multiple algorithms to make a more reliable prediction for a NS (e.g., by using consensus prediction or majority vote) [Chun and Fay, 2009]. Because querying predictions from different databases/Web- servers for different algorithms is both tedious and time consuming, we developed dbNSFP (database for nonsynonymous SNPs’ functional predictions) to facilitate the process. We first compiled a collection of all possible NSs in the human genome (a total of 75,931,005) based on the annotation of the Consensus Coding Sequence (CCDS) project [Pruitt et al., 2009]. We next collected their corresponding prediction scores from four new and popular prediction algorithms (SIFT [Kumar et al., 2009], Polyphen2 [Adzhubei et al., 2010], LRT [Chun and Fay, 2009], and MutationTaster [Schwarz et al., 2010]). We also added other related information including a conservation score (PhyloP) [Siepel et al., 2006], degenerate type, and corresponding codons and genes. The dbNSFP is the first known integrated database of functional predictions from multiple algorithms for the compre- hensive collection of human NSs. Data Sources and Processing The genes and their corresponding codons were determined based on CCDS version 20090327. This is the latest version based on the human reference sequence build hg18. Although the current human reference sequence is build hg19, many important human sequence resources are based on hg18, including current & 2011 WILEY-LISS, INC.