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Silent mutations in medical genetics databases like ClinVar contain extra value if analyzed with the most current genomics tools. In most cases the silent mutations are of low priority in big data genomics analysis, unless additional value like them being found at functionally important DNA sequences accompanies them. This presentation describes a method to add value to the silent mutations in human exome. Specifically, mapping variants, including silent variants to the known exon-intron boundaries identifies the silent mutations whose potential as pathogenic would otherwise be a lot more unclear.