Best place for figure 2

Biological questions answered using this data

Variant pathogenicity prediction vs. curated datasets

To explore differences between RTT causing and benign MECP2genetic variants we analyzed the annotated results from VEP (see Methods) from six descriptive features (Figure 3). We chose to visualize the obtained scores about conservation (i.e., PolyPhen), pathogenicity estimation scores (i.e., SIFT, CADD, MetaLR, FATHMM-MKL), and the variant frequency in normal population from GnomAD (Lek et al., 2016) (i.e., GnomAD_AF).
We split variants by benign, both and RTT causing, as we identified a subset of 19 variants appearing in both datasets. Overall, we see expected results: the RTT causing variants were found to be in positions significantly more conserved than the benign or both variants (Figure 3, PolyPhen (Wilcoxon test)), as well as less frequent than benign variations even though, all variants presented here are not abundant in the normal population (Figure 3, GnomAD_AF). Analysis of the obtained estimation of pathogenicity from multiple scores (Figure 3 panels SIFT, CADD, MetaLR and FATHMM-MKL), shows that RTT causing variants are on average predicted as more damaging than the benign and both variants (p < 0.0001 in all cases after applying Wilcoxon test). Note that SIFT associates more pathogenic variants to lower scores, whereas CADD, MetaLR and FATHMM-MKL associates more pathogenic variants to higher scores. MetaLR is better than the other three pathogenicity scores in distinguishing benign and RTT causing variant types. This may be because this novel meta-score integrates more features than the other three prediction tools, amongst other pathogenicity scores and frequency information.
The characterization of the both group is located in three of five predictions between the benign and RTT causing, and in two of five closer to the benign group.