Comparative analysis of models in predicting the effects of SNPs on TF-DNA binding using large-scale in vitro and in vivo data.

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.

Real-Time Nondestructive Viscosity Measurement of Soft Tissue Based on Viscoelastic Response Optical Coherence Elastography.

Viscoelasticity of the soft tissue is an important mechanical factor for disease diagnosis, biomaterials testing and fabrication. Here, we present a real-time and high-resolution viscoelastic response-optical coherence elastography (VisR-OCE) method based on acoustic radiation force (ARF) excitation and optical coherence tomography (OCT) imaging. The relationship between displacements induced by two sequential ARF loading-unloading and the relaxation time constant of the soft tissue-is established for the Kelvin-Voigt material. Through numerical simulation, the optimal experimental parameters are determined, and the influences of material parameters are evaluated. Virtual experimental results show that there is less than 4% fluctuation in the relaxation time constant values obtained when various Young's modulus and Poisson's ratios were given for simulation. The accuracy of the VisR-OCE method was validated by comparing with the tensile test. The relaxation time constant of phantoms measured by VisR-OCE differs from the tensile test result by about 3%. The proposed VisR-OCE method may provide an effective tool for quick and nondestructive viscosity testing of biological tissues.