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From prioritisation to understanding: mechanistic predictions of variant effects


The widespread application of sequencing technologies, used for example to obtain data from healthy individuals or patient cohorts, has led to the identification of numerous mutations, the effect of which remains largely unclear. Therefore, developing approaches allowing accurate in-silico prediction of mutation effects is becoming increasingly important. In their recent study, Beltrao and colleagues (Wagih et al, 2018) describe an integrative approach for determining the effects of mutations from the perspective of protein structure, conservation and transcription factor binding. This allows for predicting the mechanisms underlying the most impactful variants rather than just identifying these variants.

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