Non-coding variants associated with complex traits can alter the motifs of transcription factor (TF)-deoxyribonucleic acid binding. Although many computational models have been developed to predict the effects of non-coding variants on TF binding, their predictive power lacks systematic evaluation. Here we have evaluated 14 different models built on position weight matrices (PWMs), support vector machines, ordinary least squares and deep neural networks (DNNs), using large-scale in vitro (i.e. SNP-SELEX) and in vivo (i.e. allele-specific binding, ASB) TF binding data. Our results show that the accuracy of each model in predicting SNP effects in vitro significantly exceeds that achieved in vivo. For in vitro variant impact prediction, kmer/gkm-based machine learning methods (deltaSVM_HT-SELEX, QBiC-Pred) trained on in vitro datasets exhibit the best performance. For in vivo ASB variant prediction, DNN-based multitask models (DeepSEA, Sei, Enformer) trained on the ChIP-seq dataset exhibit relatively superior performance. Among the PWM-based methods, tRap demonstrates better performance in both in vitro and in vivo evaluations. In addition, we find that TF classes such as basic leucine zipper factors could be predicted more accurately, whereas those such as C2H2 zinc finger factors are predicted less accurately, aligning with the evolutionary conservation of these TF classes. We also underscore the significance of non-sequence factors such as cis-regulatory element type, TF expression, interactions and post-translational modifications in influencing the in vivo predictive performance of TFs. Our research provides valuable insights into selecting prioritization methods for non-coding variants and further optimizing such models.

We report on a multi-threaded implementation of Fast Fourier Transforms over generalized Fermat prime fields. This work extends a previous study realized on graphics processing units to multi-core processors. In this new context, we overcome the less fine control of hardware resources by successively using FFT in support of the multiplication in those fields. We obtain favorable speedup factors (up to 6.9x on a 6-core, 12 threads node, and 4.3x on a 4-core, 8 threads node) of our parallel implementation compared to the serial implementation for the overall application thanks to the low memory footprint and the sharp control of arithmetic instructions of our implementation of generalized Fermat prime fields.

Summary Vehicular ad hoc networks (VANETs) continually produce large scale of data shared among vehicles nearby to improve driving experience and safety. The vehicular communications contain a large amount of private information. It is well known that group key management is a fundamental cryptographic primitive for providing secure group communication. Although many proposals with regard to group key management have been introduced, they cannot well support secure subgroup and intergroup communications in VANETs. In this paper, we exploit the specific features of vehicular communications and propose two group key distribution schemes providing efficient solutions to these two problems. Our schemes do not require interaction between users. Storage and computation analyses show that compared with existing schemes, our proposals are more efficient and versatile. Based on these basic schemes, we present extensions for multipartite groups, by which the efficiency is greatly improved in this scenario. We also provide the extensions for two‐level multipartite groups, which enable fine‐grained real‐world applications. The experimental results confirm that our group key distribution schemes are practical in security, computation, and communication. Copyright © 2015 John Wiley & Sons, Ltd.