Table 4. Format of Queries for Input File Query type NS Genome position Gene Formata chr pos ref alt chr pos ref alt refAA altAA chr pos gene_name gene_id CCDS_id Example Y 140855 A C Y 140855 A C M L 10 94454459 PLCXD1 55344 CCDS14103.1 aSeparated by tab or space. chr: chromosome number; pos: position on chromosome; ref: reference allele; alt: alternative allele; refAA: reference amino acid; altAA: alternative amino acid; gene_name: gene name; gene_id: gene Entrez ID; CCDS_id: CCDS ID. results will be reported. By default, the program searches all chromosomes with the positions according to the human genome reference sequence hg18. Users can specify both the chromosomes to search for and the reference sequence version by using the format ‘‘java search_dbNSFP [-v reference_version] [-c chromosome_list] -i infile -o outfile,’’ where the contents in [ ] are optional. reference_version can be either hg18 or hg19. chromosome_list is a list of chromosome number (1, 2,y, X, Y) separated by commas without space, for example ‘‘1,3,22,X.’’ If there are some queries that do not have matched NS, those failed queries will be listed in the system output (command line environment). Discussion dbNSFP is a database for all potential NSs (as to the reference sequence) in the human genome and their functional predictions. It was developed for two main purposes. The first is to facilitate the SNP filtering/prioritizing step in studies of mapping rare Mendelian disease genes using the exome-sequencing approach. The second is to facilitate the imputation of missing scores that are required in the weighted sum test (e.g., VT test) [Price et al., 2010] for detecting association between phenotypes and groups of rare variants. A companion search program is provided for fast local queries. For the current version, we attempted to keep the database simple and lightweight to ensure storage and running efficiency. Except for five prediction/conservation scores (PhyloP, SIFT, Polyphen2, LRT, and MutationTaster) for each NS, the database only contains information of its corresponding genomic position, gene, codon, among others. Future development may include expanding the collection of NSs from other genome databases, incorporating the results from other prediction algorithms that do not provide genome position searches, such as PhD-SNP [Capriotti et al., 2006] and PANTHER [Thomas et al., 2003], and building a fully functional Webserver for on-line database query. We plan to keep updating the database to have newer CCDS versions, whenever most of the public available human genome sequence data shift to a new human genome reference sequence. Some cautions are needed when compiling and interpreting the search results from dbNSFP. First, the NSs collected in dbNSFP were based on the annotations of CCDS, which defines consensus CDSs from multiple genome databases. Therefore, CCDS by no means includes the complete set of human CDSs, but rather includes a set of well-annotated CDSs. It is quite possible that some NSs defined by other databases are not contained in dbNSFP. Second, the NSs in dbNSFP are defined as the alternative alleles against the reference alleles of the human reference sequence. However, the reference allele is neither necessary the ‘‘wild-type’’ allele, nor the common allele in all human populations. Some other information about the alternative allele, for example, its 898 HUMAN MUTATION, Vol. 32, No. 8, 894–899, 2011 frequency in populations or sampled individuals, may help to determine its potential to be deleterious. Third, for various reasons, some scores are missing for some NSs and the imputed scores may not be reliable. For example, scores for stop-gain NSs are not available from SIFT and Polyphen2. Although imputed scores for stop-gain NSs are provided, some researchers may prefer to assign a score based on their knowledge of the protein. For instance, a stop-gain near the beginning of the protein is more likely to be deleterious than one near the end of the protein. Fourth, there may be multiple entries in dbNSFP that match the same NS query. This is primarily due to the fact that CCDS contains different annotations for different forms of the same gene. Our rules for LRT predictions (Supp. Fig. S1) are similar to those of Chun and Fay [2009], but be aware that our predictions may not be the same as those reported by the LRT Webserver (http://www.genetics.wustl.edu/jflab/lrt_query.html). The major difference is that the Webserver predicts a NS as either D(eleterious) or N(eutral) according to LRTori and o, regardless of the quality of the underlying amino acid alignment. Chun and Fay [2009] suggested that if the amino acid alignment has less than 10 eutherian mammals, the prediction power is low. Therefore, we add a new prediction category U(nknown) specifically for the low-power situation. As a result, our LRT predictions for D(eleterious) and N(eutral) are more stringent than those provided by the Webserver. Because the predictions of LRT and MutationTaster are not entirely dependent on the prediction scores, discrepancy between a score and its corresponding prediction may occur. As to LRT, the discrepancies come from N(eutral) predictions with high scores (i.e., the codon is highly constrained or a NS is likely to be deleterious). This is because of one prediction rule: if the alternative AA allele is observed in at least one eutherian mammal, the NS is predicted as N(eutral), regardless of the score it associated with (Supp. Fig. S1). The discrepancy cases of MutationTaster come from the two ‘‘automatic’’ predictions, that is, ‘‘disease_causing_automatic’’ (‘‘A’’) and ‘‘polymorphism_automatic’’ (‘‘P’’). An NS will be predicted to be ‘‘A’’ if it causes nonsense-mediated decay (i.e., a stop-gain); and an NS will be predicted to be ‘‘P’’ if all three genotypes of the reference/alternative alleles are observed in the HapMap data [The International HapMap Consortium, 2010]. Both types of prediction are not dependent on the prediction scores at all, although the scores are still reported. As to the missing data imputation, our results clearly showed that BPCAfill can obtain more accurate imputation compared to simply using the average score of the same algorithm (column-average). This is easy to understand because BPCAfill borrows information from the correlated scores of different algorithm while column- average imputation does not use such information. To further evaluate the performance of BPCAfill, we also compared its results to the simple imputation using the average of the nonmissing scores of the other algorithms for the same NS (row-average). For example, if an NS misses a SIFT score while all other four scores (PhyloP, Polyphen2, LRT, MutationTaster) are available, we impute the SIFT score using the average of the other four scores. The results showed that although the row-average imputation’s mean absolute error is smaller than that of the column-average, it is still larger than that of BPCAfill (data not shown). It is also shown that the row-average imputation is significantly biased and has a significantly larger variance than BPCAfill (data not shown). The superior performance of BPCAfill compared to the row-average can be explained by the nonlinear correlation between the scores of different algorithms and the fact that BPCAfill also borrows information from the correlation between NSs while row-average imputation does not.