Collecting predictions and scores from multiple algorithms has many advantages. Besides providing better imputation for missing data, as we mentioned in the introduction, it also helps to get more accurate functional predictions for NS. A simple way to incorporate multiple predictions is to rank each NS according to the number of algorithms that predict it as deleterious. The larger the number, the higher the confidence that it is truly deleterious. A better solution is to develop a unified score (or a score of scores) based on training data sets and infer predictions from it. Besides NS, other types of variants may also contribute to diseases. However, the functional prediction methods of those variants are typically less mature than those of NS, but never the less they still provide valuable information of those variants. For example, a recent study [Waldman et al., 2010] showed that codon bias is significantly correlated with gene expression levels in human. Therefore, some synonymous changes are more likely to have strong impact on gene expression and lead to phenotypic effects. Other information that can be used to prioritize variants including the prediction of promoter regions, linkage disequilibrium with known genome-wide association study (GWAS) hits, gene expression profile of disease related tissues, known biochemical pathways, known or predicted gene interaction networks, among others. In recent years, some Webservers have been developed to integrate searches or results from multiple bioinformatics tools, for example, the SeattleSeq Annotation server, F-SNP [Lee and Shatkay, 2008], SNPLogic [Pico et al., 2009], SNPit [Shen et al., 2009], and pfSNP [Wang et al., 2011]. As the field moves from whole exome sequencing to whole genome sequencing, more efforts are needed in these nonprotein coding domains. Acknowledgments We thank Bing Yu and Dr. Maja Barbalic for testing the database and its companion search program. 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