Fig. 1

Graphical abstract of the EvORanker pipeline. Starting from a list of annotated variants obtained from a patient’s exome/genome sequencing data and following variant filtering, a list of predicted patient candidate genes harboring putatively pathogenic variants are input to EvORanker. The second input is the HPO terms corresponding to the patient’s phenotypes. The first step of the pipeline is to rank the genes listed in the HPO database according to the input HPO terms using the OntologySimilarity tool. If any of the patient candidate genes is a known disease-causing gene or ranked high using OntologySimilarity, then a genetic diagnosis is achieved. If not, then each patient candidate gene in addition to the ranked HPO gene list is input into a co-evolution and STRING-based algorithm. The algorithm analyzes two lists of genes, the co-evolving and STRING-interacting genes with each patient candidate gene. A one-sided Kolmogorov-Smirnov (K-S) test is then used to test if the co-evolving and interacting genes rank significantly high within the patient’s phenotype-related genes. The p-values obtained from running the K-S test using each dataset are combined using Fisher’s combined test. The output is a list of patient candidate genes ranked based on Fisher’s combined test p-values (from more significant to less significant). A disease-causing candidate is identified among the patient genes where a significant number of co-evolving and/or interacting genes are enriched towards the genes highly related to the patient’s input phenotypes relative to the genes that are unrelated