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"Wang, Ban"
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Optimizing 5’UTRs for mRNA-delivered gene editing using deep learning
mRNA therapeutics are revolutionizing the pharmaceutical industry, but methods to optimize the primary sequence for increased expression are still lacking. Here, we design 5’UTRs for efficient mRNA translation using deep learning. We perform polysome profiling of fully or partially randomized 5’UTR libraries in three cell types and find that UTR performance is highly correlated across cell types. We train models on our datasets and use them to guide the design of high-performing 5’UTRs using gradient descent and generative neural networks. We experimentally test designed 5’UTRs with mRNA encoding megaTAL
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gene editing enzymes for two different gene targets and in two different cell lines. We find that the designed 5’UTRs support strong gene editing activity. Editing efficiency is correlated between cell types and gene targets, although the best performing UTR was specific to one cargo and cell type. Our results highlight the potential of model-based sequence design for mRNA therapeutics.
mRNA therapeutics are revolutionizing the pharmaceutical industry. In this study, the authors characterize 5’UTR-regulated translation in cell types relevant for mRNA therapies and with fully random 5’UTRs, and show that 5’UTRs optimized via deep learning support high performance on mRNA-encoded gene editors.
Journal Article
Model and Property Analysis for a Ball-Hinged Three-Degree-of-Freedom Piezoelectric Spherical Motor
Multi-degree-of-freedom piezoelectric motors have the advantages of high torque and resolution, simple structure, and direct drive, which are widely used in robot wrist joints, deep-sea mechanisms, medical equipment, and space mechanisms. To solve the problems of high force/torque coupling degree and ball low stator and rotor bonding strength of the traditional traveling wave type three-degree-of-freedom piezoelectric spherical motor, a new structure of ball-hinged piezoelectric spherical motor is proposed. Through coordinate transformation and force analysis, the driven mathematical model of the spherical motor is given. The model shows that the three degrees of freedom of the motor are coupled with each other. According to the mathematical model of the spherical motor, the mechanical properties of the motor are analyzed by the computer simulation. The results show that the stalling torque coefficient kt has a linear relationship with the friction coefficient ε and the stator preload Fc, has a nonlinear relationship with the stator radius R and the rotor radius r, and increases with the increase of R and decreases with the increase of r. The no-load speed of motor ωn is not related to the friction coefficient ε and the stator preload Fc, and increases with the increase of R and decreases with the increase of r. The anisotropic characteristics of torque and speed of a spherical motor are further analyzed, which lays a theoretical foundation for the drive control of a spherical motor.
Journal Article
Transcriptome diversity is a systematic source of variation in RNA-sequencing data
RNA sequencing has been widely used as an essential tool to probe gene expression. While standard practices have been established to analyze RNA-seq data, it is still challenging to interpret and remove artifactual signals. Several biological and technical factors such as sex, age, batches, and sequencing technology have been found to bias these estimates. Probabilistic estimation of expression residuals (PEER), which infers broad variance components in gene expression measurements, has been used to account for some systematic effects, but it has remained challenging to interpret these PEER factors. Here we show that transcriptome diversity–a simple metric based on Shannon entropy–explains a large portion of variability in gene expression and is the strongest known factor encoded in PEER factors. We then show that transcriptome diversity has significant associations with multiple technical and biological variables across diverse organisms and datasets. In sum, transcriptome diversity provides a simple explanation for a major source of variation in both gene expression estimates and PEER covariates.
Journal Article
YOLOSeaShip: a lightweight model for real-time ship detection
With the rapid advancements in computer vision, ship detection models based on deep learning have been more and more prevalent. However, most network methods use expensive costs with high hardware equipment needed to increase detection accuracy. In response to this challenge, a lightweight real-time detection approach called YOLOSeaShip is proposed. Firstly, derived from the YOLOv7-tiny model, the partial convolution was utilized to replace the original 3×1 convolution in the ELAN module to further fewer parameters and improve the operation speed. Secondly, the parameter-free average attention module was integrated to improve the locating capacity for the hull of a ship in an image. Finally, the accuracy changes of the Focal EIoU hybrid loss function under different parameter changes were studied. The practical results trained on the SeaShips (7000) dataset demonstrate that the suggested method can detect and classify the ship position from the image more efficiently, with mAP of 0.976 and FPS of 119.84, which is ideal for real-time ship detection applications.
Journal Article
Cell-type-specific cis-regulatory divergence in gene expression and chromatin accessibility revealed by human-chimpanzee hybrid cells
Although gene expression divergence has long been postulated to be the primary driver of human evolution, identifying the genes and genetic variants underlying uniquely human traits has proven to be quite challenging. Theory suggests that cell-type-specific cis -regulatory variants may fuel evolutionary adaptation due to the specificity of their effects. These variants can precisely tune the expression of a single gene in a single cell-type, avoiding the potentially deleterious consequences of trans -acting changes and non-cell type-specific changes that can impact many genes and cell types, respectively. It has recently become possible to quantify human-specific cis -acting regulatory divergence by measuring allele-specific expression in human-chimpanzee hybrid cells—the product of fusing induced pluripotent stem (iPS) cells of each species in vitro . However, these cis -regulatory changes have only been explored in a limited number of cell types. Here, we quantify human-chimpanzee cis -regulatory divergence in gene expression and chromatin accessibility across six cell types, enabling the identification of highly cell-type-specific cis -regulatory changes. We find that cell-type-specific genes and regulatory elements evolve faster than those shared across cell types, suggesting an important role for genes with cell-type-specific expression in human evolution. Furthermore, we identify several instances of lineage-specific natural selection that may have played key roles in specific cell types, such as coordinated changes in the cis -regulation of dozens of genes involved in neuronal firing in motor neurons. Finally, using novel metrics and a machine learning model, we identify genetic variants that likely alter chromatin accessibility and transcription factor binding, leading to neuron-specific changes in the expression of the neurodevelopmentally important genes FABP7 and GAD1 . Overall, our results demonstrate that integrative analysis of cis -regulatory divergence in chromatin accessibility and gene expression across cell types is a promising approach to identify the specific genes and genetic variants that make us human.
Journal Article