Washingtongibbs1238

High throughput sequencing technologies have revolutionized the identification of mutations responsible for a diverse set of Mendelian disorders, including inherited retinal disorders (IRDs). However, the causal mutations remain elusive for a significant proportion of patients. This may be partially due to pathogenic mutations located in non-coding regions, which are largely missed by capture sequencing targeting the coding regions. The advent of whole-genome sequencing (WGS) allows us to systematically detect non-coding variations. However, the interpretation of these variations remains a significant bottleneck. In this study, we investigated the contribution of deep-intronic splice variants to IRDs. WGS was performed for a cohort of 571 IRD patients who lack a confident molecular diagnosis, and potential deep intronic variants that affect proper splicing were identified using SpliceAI. A total of six deleterious deep intronic variants were identified in eight patients. An in vitro minigene system was applied to further validate the effect of these variants on the splicing pattern of the associated genes. The prediction scores assigned to splice-site disruption positively correlated with the impact of mutations on splicing, as those with lower prediction scores demonstrated partial splicing. Through this study, we estimated the contribution of deep-intronic splice mutations to unassigned IRD patients and leveraged in silico and in vitro methods to establish a framework for prioritizing deep intronic variant candidates for mechanistic and functional analyses.Landes geese and Sichuan White geese are two important genetic materials for commercial goose breeding. However, the differences in the male reproductive capacity between these two breeds and the potential molecular mechanisms and associated key genes have not been reported to date. The present study compared the testicular histology and mRNA-long non-coding RNA (lncRNA) expression patterns to reveal the differences in male reproductive performance between Sichuan White geese and Landes geese, as well as to explore the underlying molecular mechanisms. Histological results showed that the testicular organ index, semen volume, and long diameter of seminiferous tubules of Landes geese were significantly larger than those of Sichuan White geese. Analyses of mRNA-lncRNA expression profile showed that compared with Sichuan White geese, a total of 462 differentially expressed mRNAs (DEGs) (173 up-regulated and 289 down-regulated) and 329 differentially expressed lncRNAs (DE lncRNAs) (280 up-regulated, 49 down-regulated) were identified in Landes geese. Among these DEGs, there were 10 spermatogenesis-related and highly expressed (FPKM > 10) DEGs. Except for SEPP1, all of these DEGs were significantly up-regulated in the testes of Landes geese. Functional enrichment analysis indicated that the pathway related to metabolism progress and phosphoinositol signal is vitally responsible for differences in male reproductive performance between Landes geese and Sichuan White geese. These results show that compared with Sichuan White geese, the spermatogenesis in the testis of Landes geese was more active, which may be mainly related to the inositol phosphate signal. These data contribute to a better understanding of the mechanisms underlying different male reproductive performances between Landes geese and Sichuan White geese. This knowledge might eventually provide a theoretical basis for improving male reproductive performance in geese.To analyze and construct a survival-related endogenous RNA (ceRNA) network in gastric cancer (GC) with lymph node metastasis, we obtained expression profiles of long non-coding RNAs (lncRNAs), mRNAs, and microRNAs (miRNAs) in GC from The Cancer Genome Atlas database. The edgeR package was used to screen differentially expressed lncRNAs, mRNAs, and miRNAs between GC patients with lymphatic metastasis and those without lymphatic metastasis. Then, we used univariate Cox regression analysis to identify survival-related differentially expressed RNAs. In addition, we used multivariate Cox regression analysis to screen lncRNAs, miRNAs, and mRNAs for use in the prognostic prediction models. Avadomide The results showed that 2,247 lncRNAs, 155 miRNAs, and 1,253 mRNAs were differentially expressed between the two patient groups. Using univariate Cox regression analysis, we found that 395 lncRNAs, eight miRNAs, and 180 mRNAs were significantly related to the survival time of GC patients. We next created a survival-related network consisting of 59 lncRNAs, seven miRNAs, and 36 mRNAs. In addition, we identified eight RNAs associated with prognosis by multivariate Cox regression analysis, comprising three lncRNAs (AC094104.2, AC010457.1, and AC091832.1), two miRNAs (miR-653-5p and miR-3923), and three mRNAs (C5orf46, EPHA8, and HPR); these were used to construct the prognostic prediction models, and their risk scores could be used to assess GC patients' prognosis. In conclusion, this study provides new insights into ceRNA networks in GC and the screening of prognostic biomarkers for GC.Deep learning methods, which can predict the binding affinity of a drug-target protein interaction, reduce the time and cost of drug discovery. In this study, we propose a novel deep convolutional neural network called SE-OnionNet, with two squeeze-and-excitation (SE) modules, to computationally predict the binding affinity of a protein-ligand complex. The OnionNet is used to extract a feature map from the three-dimensional structure of a protein-drug molecular complex. The SE module is added to the second and third convolutional layers to improve the non-linear expression of the network to improve model performance. Three different optimizers, stochastic gradient descent (SGD), Adam, and Adagrad, were also used to improve the performance of the model. A majority of protein-molecule complexes were used for training, and the comparative assessment of scoring functions (CASF-2016) was used as the benchmark. Experimental results show that our model performs better than OnionNet, Pafnucy, and AutoDock Vina. Finally, we chose the macrophage migration inhibitor factor (PDB ID 6cbg) to test the stability and robustness of the model.