GenoCanyon is a statistical framework to predict functional regions in the human genome through integrative analysis of ENCODE epigenomic data and multiple conservation metrics [17]. Later we have further extended the model and developed GenoSkyline, which aimed to predict tissue-specific functionality [14]. In the AnnoPred model, we incorporated GenoCanyon general functionality scores, GenoSkyline tissue-specific functionality scores for seven tissue types (brain, gastrointestinal tract, lung, heart, blood, muscle, and epithelium), and 53 LDSC baseline annotations that covered a variety of genomic features [18] (S1 Table). We smoothed GenoCanyon scores by a 10Kb window, a strategy previously shown to improve robustness of functionality prediction [22]. The smoothed GenoCanyon annotation and raw GenoSkyline annotations of seven tissue types were dichotomized based on a cutoff of 0.5. The regions with GenoCanyon or GenoSkyline scores greater than the cutoff were interpreted as non-tissue-specific or tissue-specific functional regions in the human genome. Such dichotomization has been previously shown to be robust against the cutoff choice [14]. Notably, the AnnoPred framework allows users to specify their own choice of annotations.
