Figure 3

Weighted scoring scheme. (A) Features used in the weighted scoring scheme. Features can be classified into two classes: discrete and continuous. Discrete features are binary, such as in ultra-conserved elements or not. For continuous features, taking the `motif-breaking score’ as an example, the values would be the changes in PWMs. * - the feature depends on the user (see Figure 2); (B) We weighted each feature based on the mutation patterns observed in natural polymorphisms. Features that are frequently observed are less likely to contribute to the deleteriousness of variants and are weighted less (entropy-based method, details described in Material and methods). For a continuous feature, such as the `motif-breaking scores’, we calculated a weight for each observed value. The x-axis is the observed motif-breaking score and the y-axis is the corresponding weight. The black line shows the values observed in natural polymorphisms. We then fit a smooth curve (the red dashed line) to obtain continuous weights for all possible motif-breaking scores.