Mammalian Per1 and Per2 genes are involved in the mechanism of the circadian clock and are inducible by light. Alight pulse can evoke a change in the onset of wheel-running activity in mice by shifting the onset of activity to earlier times (phase advance) or later times (phase delays) thereby advancing or delaying the clock (clock resetting). To assess the role of mouse Per (mPer) genes in circadian clock resetting, mice carrying mutant mPer1 or mPer2 genes were tested for responses to a light pulse at ZT 14 and ZT 22, respectively. The authors found that mPer1 mutants did not advance and mPer2 mutants did not delay the clock. They conclude that the mammalian Per genes are not only light-responsive components of the circadian oscillator but also are involved in resetting of the circadian clock.

With expanding applications of next-generation sequencing in medical genetics, increasing computational methods are being developed to predict the pathogenicity of missense variants. Selecting optimal methods can accelerate the identification of candidate genes. However, the performances of different computational methods under various conditions have not been completely evaluated. Here, we compared 12 performance measures of 23 methods based on three independent benchmark datasets: (i) clinical variants from the ClinVar database related to genetic diseases, (ii) somatic variants from the IARC TP53 and ICGC databases related to human cancers and (iii) experimentally evaluated PPARG variants. Some methods showed different performances under different conditions, suggesting that they were not always applicable for different conditions. Furthermore, the specificities were lower than the sensitivities for most methods (especially, for the experimentally evaluated benchmark datasets), suggesting that more rigorous cutoff values are necessary to distinguish pathogenic variants. Furthermore, REVEL, VEST3 and the combination of both methods (i.e. ReVe) showed the best overall performances with all the benchmark data. Finally, we evaluated the performances of these methods with de novo mutations, finding that ReVe consistently showed the best performance. We have summarized the performances of different methods under various conditions, providing tentative guidance for optimal tool selection.