How can one convert the plethora of information provided by Next Generation Sequencing into clinically actionable suggestions for diagnostics and drug treatments. We describe the key tools in the PROGNOSTIX methodology that begins to address these issues. We first describe and validate the ENTPRISE algorithm for predicting the likely disease association of missense variations. Compared to existing algorithms such as FATHMM whose false positive rate is 12.9%, the false positive rate of ENTPRISE is 5.4%. Moreover, unlikely many other approaches it does not assign variations based on the identity of the protein rather than the variations within the protein. We then describe a comprehensive proteome scale approach that predicts human protein targets and side effect of drugs. For drug-protein interaction prediction, FINDSITE^comb, whose average precision is ~30% and recall ~27%, is employed. Successful applications of the methodology to treat Chronic Fatigue Syndrome, to identify novel antibiotic leads and promising anti-seizure drugs are described. For side effect prediction, a new method is developed with a precision of ~57% and a recall of ~24%. Our predictions show that drugs are quite promiscuous, with the average (median) number of human targets per drug of 329 (38), while a given protein interacts with 57 drugs. The result implies that drug side effects are inevitable and existing drugs may be useful for repurposing, with only ~1,000 human proteins likely causing serious side effects. A killing index derived from serious side effects has a strong correlation with FDA approved drugs being withdrawn. Therefore, it provides a pre-filter for new drug development. The methodology is free to the academic community on the DR. PRODIS (DRugome, PROteome, and DISeasome) webserver at http://cssb.biology.gatech.edu/dr.prodis/. DR. PRODIS provides protein targets of drugs, drugs for a given protein target, associated diseases and side effects of drugs, as well as an interface for the virtual target screening of new compounds.