exome capture chips and the 1,000 genomes pilot data. To facilitate NS queries based on hg19, we used the liftOver tool from the UCSC Genome Browser [Rhead et al., 2010] to convert the coordinates of NSs to hg19. Only 561 NSs out of 75,931,005 (0.0007%) were not converted successfully. The PhyloP scores were extracted from the placental subset of the precomputed phyloP44way scores [Pollard et al., 2010] provided by the UCSC Genome Browser (see details at http://hgdownload. cse.ucsc.edu/goldenPath/hg18/phyloP44way/). The original score is presented as phyloPori 5 log10P or log10P, if the site is more conserved than neutral (phyloPori40) or less conserved than neutral (phyloPorio0), respectively. P is a two-sided P-value based on a likelihood ratio test. To make it easier to compare with the prediction scores, we rescaled it to a new score as phyloPnew 5 1 0.5 10 phyloPori if phyloPori40 or phyloPnew 5 0.5 10phyloPori if phyloPorio0 to mimic a one-sided P-value. The new score ranges from 0 to 1 and a larger score signifies higher conservation. We used phyloPnew40.95 as a rule to predict conserved site [Siepel et al., 2006]. That is, the prediction is ‘‘C(onserved)’’ if phyloPnew40.95; otherwise, the prediction is ‘‘N(on-conserved).’’ Original SIFT scores were provided by ANNOVAR [Wang et al., 2010], which were originally from a local database format of SIFT 4.0.3. The original SIFT scores (SIFTori) range from 0 to 1. If a score is smaller than 0.05 the corresponding NS is predicted as ‘‘D(amaging)’’; otherwise it is predicted as ‘‘T(olerated).’’ We defined a new score SIFTnew 5 1 SIFTori. The new score still ranges from 0 to 1 and a larger score means more likely to be deleterious. Correspondingly, if a new score is larger than 0.95, the prediction is ‘‘D’’; otherwise, it is ‘‘T.’’ Original LRT scores (LRTori) were downloaded from the LRT Webserver (http://www.genetics.wustl.edu/jflab/lrt_query.html). LRTori is a two-sided P-value of the likelihood ratio test of codon constraint. Each LRTori is associated with an estimated nonsynonymous-to-synonymous-rate ratio (o) and an amino acid alignment of 31 species at the test codon. If the codon is more constrained than neutral, oo1; otherwise, oZ1. We defined our LRT score as LRTnew 5 1 LRTori 0.5 if oo1, or LRTnew 5 LRTori 0.5 if oZ1 to mimic a one-sided p-value. The score ranges from 0 to 1 and a larger score signifies that the codon is more constrained or a NS is more likely to be deleterious. LRT predictions were derived using the rules summarized in Supp. Figure S1, which is similar to those of Chun and Fay [2009]. In short, (1) a predicted D(eleterious) NS needs to fulfill three requirements: (i) from a codon defined by LRT as significantly constrained (LRTorio0.001 and oo1), (ii) from a site with Z10 eutherian mammals alignments, and (iii) the alternative AA is not presented in any of the eutherian mammals; (2) a predicted N(eutral) NS needs to fulfill either of the two requirements: (i) the alternative AA is presented in at least one of the eutherian mammals, or (ii) from a codon defined by LRT as not significantly constrained (LRToriZ0.001 or oZ1) and with Z10 eutherian mammals alignments; (3) otherwise, the NS is reported as U(nknown). More details of the LRT predictions can be found in the Discussion section. Polyphen2 scores were manually queried and downloaded as 500 batches from its batch query server (http://genetics.bwh. harvard.edu/pph2/bgi.shtml). The default query settings (Classifier model: HumDiv, Transcipts: Canonical, Annotations: Missense) were used except with genome assembly NCBI36/hg18 instead of GRCh37/hg19. We used pph2_prob as our Polyphen2 score, which is the classifier probability of the alternative allele being deleterious. The score ranges from 0 to 1, and the corresponding prediction is ‘‘probably damaging’’ (coded as ‘‘D’’) if it is larger than 0.85; ‘‘possibly damaging’’ (coded as ‘‘P’’) if it is between 0.85 and 0.15 and ‘‘benign’’ (coded as ‘‘B’’) if it is smaller than 0.15. Throughout this article, we regard ‘‘D’’ as its category for deleterious NSs. MutationTaster scores were queried from its Webserver (http:// www.mutationtaster.org/) using its batch query Perl scripts for each of the 75,931,005 NSs. The input file needs an ENSEMBL transcript ID and a sequence snippet (the immediate upstream and downstream sequence from the query site) for each SNP. We first used ANNOVAR to annotate the NSs using ENSEMBL database to get the corresponding ENSEMBL transcript ID for each NS. If there is more than one transcript ID, initially the first ID was used to build an input file for the first round query. If it turned out that transcript ID is not in the MutationTaster’s database (as reported in its output file), we then used the second ID to build an input file for the second round of query. Snippets were built using the FASTA files of the human reference sequence downloaded from the UCSC Genome Browser. For the first round query, we used 12 upstream and 12 downstream nucleotides from the query site to build the snippet. If the output file reported that the snippet is not unique, then 100 upstream and 100 downstream nucleotides from the query site were used to build a new snippet for a second round query. MutationTater has four types of predictions: ‘‘disease_causing_automatic,’’ ‘‘disease_causing,’’ ‘‘polymorphism,’’ and ‘‘polymorphism_automatic,’’ which we coded as ‘‘A,’’ ‘‘D,’’ ‘‘N,’’ and ‘‘P,’’ respectively. Among them, ‘‘D’’ and ‘‘N’’ are determined by the prediction algorithm, whereas ‘‘A’’ and ‘‘P’’ are determined by external information (see the Discussion section for details). We regard ‘‘A’’ and ‘‘D’’ as categories for deleterious NSs. MutationTaster also reports a P_value for each prediction, which represents the probability that the prediction is true. We defined our MutationTaster score using the following rule: if the prediction is ‘‘disease_causing’’ or ‘‘disease_causing_automatic,’’ score 5 P_value; if the prediction is ‘‘polymorphism’’ or ‘‘polymorphism_automatic’’, score 5 1 P_value. The resulting score ranges from 0 to 1 and a larger score means more likely to be deleterious. For various reasons, an algorithm may not output a prediction or score for a NS, thus regarded as missing data and reported it as ‘‘NA.’’ In dbNSFP, we reported imputed scores for missing scores (see Missing Data and Imputation). Therefore, a prediction of ‘‘NA’’ can be used as an indicator to determine whether the corresponding score is imputed or not. Rarely, probably due to different gene annotations for the same NS, an algorithm may report a different reference AA or alternative AA as to those defined by CCDS. In that case, we considered the predictions and scores (if reported) as missing data. On the other hand, for a small number of NSs, CCDS may define more than one reference AA or alternative AA for the same nucleotide site change, due to different annotations for the same gene. We treated those definitions as different NSs. In another words, we defined a NS as a unique combination of chromosome, position, reference nucleotide allele, alternative nucleotide allele, reference AA allele, and alternative AA allele. Database Contents In the current version, each NS links to the following entries: chromosome number, physical position on the chromosome as to hg18 (1-based coordinate), reference nucleotide allele (as on the 1 strand), alternative nucleotide allele (as on the 1 strand), reference AA, alternative AA, physical position on the chromo- some as to hg19 (1-based coordinate), gene name, gene Entrez ID, CCDS ID, reference codon, position on the codon (1, 2, or 3), HUMAN MUTATION, Vol. 32, No. 8, 894–899, 2011 895