Functional characterization of the noncoding genome is essential for biological 
understanding of gene regulation and disease. Here, we introduce the 
computational framework PINES (Phenotype-Informed Noncoding Element Scoring), 
which predicts the functional impact of noncoding variants by integrating 
epigenetic annotations in a phenotype-dependent manner. PINES enables analyses 
to be customized towards genomic annotations from cell types of the highest 
relevance given the phenotype of interest. We illustrate that PINES identifies 
functional noncoding variation more accurately than methods that do not use 
phenotype-weighted knowledge, while at the same time being flexible and easy to 
use via a dedicated web portal.