Evaluation of performance of leading algorithms for variant pathogenicity predictions and designing a combinatory predictor method: application to Rett syndrome variants.

The results from analysis shows an optimized selection of prediction algorithm and developed a combinatory predictor method. Our combinatory approach of using both best performing independent and ensemble predictors reduces any algorithm biases in variant characterization. The reclassification of variants (such as VUS) in MECP2 gene associated with RETT syndrome suggest that the combinatory in-silico predictor approach had a higher success rate in categorizing their pathogenicity.