A novel representation of RNA secondary structure based on element-contact graphs

<p>Abstract</p> <p>Background</p> <p>Depending on their specific structures, noncoding RNAs (ncRNAs) play important roles in many biological processes. Interest in developing new topological indices based on RNA graphs has been revived in recent years, as such indices can be used to compare, identify and classify RNAs. Although the topological indices presented before characterize the main topological features of RNA secondary structures, information on RNA structural details is ignored to some degree. Therefore, it is necessity to identify topological features with low degeneracy based on complete and fine-grained RNA graphical representations.</p> <p>Results</p> <p>In this study, we present a complete and fine scheme for RNA graph representation as a new basis for constructing RNA topological indices. We propose a combination of three vertex-weighted element-contact graphs (ECGs) to describe the RNA element details and their adjacent patterns in RNA secondary structure. Both the stem and loop topologies are encoded completely in the ECGs. The relationship among the three typical topological index families defined by their ECGs and RNA secondary structures was investigated from a dataset of 6,305 ncRNAs. The applicability of topological indices is illustrated by three application case studies. Based on the applied small dataset, we find that the topological indices can distinguish true pre-miRNAs from pseudo pre-miRNAs with about 96% accuracy, and can cluster known types of ncRNAs with about 98% accuracy, respectively.</p> <p>Conclusion</p> <p>The results indicate that the topological indices can characterize the details of RNA structures and may have a potential role in identifying and classifying ncRNAs. Moreover, these indices may lead to a new approach for discovering novel ncRNAs. However, further research is needed to fully resolve the challenging problem of predicting and classifying noncoding RNAs.</p

Correlation between sequence conservation and structural thermodynamics of microRNA precursors from human, mouse, and chicken genomes

<p>Abstract</p> <p>Background</p> <p>Previous studies have shown that microRNA precursors (pre-miRNAs) have considerably more stable secondary structures than other native RNAs (tRNA, rRNA, and mRNA) and artificial RNA sequences. However, pre-miRNAs with ultra stable secondary structures have not been investigated. It is not known if there is a tendency in pre-miRNA sequences towards or against ultra stable structures? Furthermore, the relationship between the structural thermodynamic stability of pre-miRNA and their evolution remains unclear.</p> <p>Results</p> <p>We investigated the correlation between pre-miRNA sequence conservation and structural stability as measured by adjusted minimum folding free energies in pre-miRNAs isolated from human, mouse, and chicken. The analysis revealed that conserved and non-conserved pre-miRNA sequences had structures with similar average stabilities. However, the relatively ultra stable and unstable pre-miRNAs were more likely to be non-conserved than pre-miRNAs with moderate stability. Non-conserved pre-miRNAs had more G+C than A+U nucleotides, while conserved pre-miRNAs contained more A+U nucleotides. Notably, the U content of conserved pre-miRNAs was especially higher than that of non-conserved pre-miRNAs. Further investigations showed that conserved and non-conserved pre-miRNAs exhibited different structural element features, even though they had comparable levels of stability.</p> <p>Conclusions</p> <p>We proposed that there is a correlation between structural thermodynamic stability and sequence conservation for pre-miRNAs from human, mouse, and chicken genomes. Our analyses suggested that pre-miRNAs with relatively ultra stable or unstable structures were less favoured by natural selection than those with moderately stable structures. Comparison of nucleotide compositions between non-conserved and conserved pre-miRNAs indicated the importance of U nucleotides in the pre-miRNA evolutionary process. Several characteristic structural elements were also detected in conserved pre-miRNAs.</p

In silico genetic robustness analysis of microRNA secondary structures: potential evidence of congruent evolution in microRNA

<p>Abstract</p> <p>Background</p> <p>Robustness is a fundamental property of biological systems and is defined as the ability to maintain stable functioning in the face of various perturbations. Understanding how robustness has evolved has become one of the most attractive areas of research for evolutionary biologists, as it is still unclear whether genetic robustness evolved as a direct consequence of natural selection, as an intrinsic property of adaptations, or as congruent correlate of environment robustness. Recent studies have demonstrated that the stem-loop structures of microRNA (miRNA) are tolerant to some structural changes and show thermodynamic stability. We therefore hypothesize that genetic robustness may evolve as a correlated side effect of the evolution for environmental robustness.</p> <p>Results</p> <p>We examine the robustness of 1,082 miRNA genes covering six species. Our data suggest the stem-loop structures of miRNA precursors exhibit a significantly higher level of genetic robustness, which goes beyond the intrinsic robustness of the stem-loop structure and is not a byproduct of the base composition bias. Furthermore, we demonstrate that the phenotype of miRNA buffers against genetic perturbations, and at the same time is also insensitive to environmental perturbations.</p> <p>Conclusion</p> <p>The results suggest that the increased robustness of miRNA stem-loops may result from congruent evolution for environment robustness. Potential applications of our findings are also discussed.</p

Vertical Semi-Federated Learning for Efficient Online Advertising

As an emerging secure learning paradigm in leveraging cross-silo private data, vertical federated learning (VFL) is expected to improve advertising models by enabling the joint learning of complementary user attributes privately owned by the advertiser and the publisher. However, the 1) restricted applicable scope to overlapped samples and 2) high system challenge of real-time federated serving have limited its application to advertising systems. In this paper, we advocate new learning setting Semi-VFL (Vertical Semi-Federated Learning) as a lightweight solution to utilize all available data (both the overlapped and non-overlapped data) that is free from federated serving. Semi-VFL is expected to perform better than single-party models and maintain a low inference cost. It's notably important to i) alleviate the absence of the passive party's feature and ii) adapt to the whole sample space to implement a good solution for Semi-VFL. Thus, we propose a carefully designed joint privileged learning framework (JPL) as an efficient implementation of Semi-VFL. Specifically, we build an inference-efficient single-party student model applicable to the whole sample space and meanwhile maintain the advantage of the federated feature extension. Novel feature imitation and ranking consistency restriction methods are proposed to extract cross-party feature correlations and maintain cross-sample-space consistency for both the overlapped and non-overlapped data. We conducted extensive experiments on real-world advertising datasets. The results show that our method achieves the best performance over baseline methods and validate its effectiveness in maintaining cross-view feature correlation

Selection of antisense oligonucleotides based on multiple predicted target mRNA structures

BACKGROUND: Local structures of target mRNAs play a significant role in determining the efficacies of antisense oligonucleotides (ODNs), but some structure-based target site selection methods are limited by uncertainties in RNA secondary structure prediction. If all the predicted structures of a given mRNA within a certain energy limit could be used simultaneously, target site selection would obviously be improved in both reliability and efficiency. In this study, some key problems in ODN target selection on the basis of multiple predicted target mRNA structures are systematically discussed. RESULTS: Two methods were considered for merging topologically different RNA structures into integrated representations. Several parameters were derived to characterize local target site structures. Statistical analysis on a dataset with 448 ODNs against 28 different mRNAs revealed 9 features quantitatively associated with efficacy. Features of structural consistency seemed to be more highly correlated with efficacy than indices of the proportion of bases in single-stranded or double-stranded regions. The local structures of the target site 5' and 3' termini were also shown to be important in target selection. Neural network efficacy predictors using these features, defined on integrated structures as inputs, performed well in "minus-one-gene" cross-validation experiments. CONCLUSION: Topologically different target mRNA structures can be merged into integrated representations and then used in computer-aided ODN design. The results of this paper imply that some features characterizing multiple predicted target site structures can be used to predict ODN efficacy

RDMAS: a web server for RNA deleterious mutation analysis

BACKGROUND: The diverse functions of ncRNAs critically depend on their structures. Mutations in ncRNAs disrupting the structures of functional sites are expected to be deleterious. RNA deleterious mutations have attracted wide attentions because some of them in cells result in serious disease, and some others in microbes influence their fitness. RESULTS: The RDMAS web server we describe here is an online tool for evaluating structural deleteriousness of single nucleotide mutation in RNA genes. Several structure comparison methods have been integrated; sub-optimal structures predicted can be optionally involved to mitigate the uncertainty of secondary structure prediction. With a user-friendly interface, the web application is easy to use. Intuitive illustrations are provided along with the original computational results to facilitate quick analysis. CONCLUSION: RDMAS can be used to explore the structure alterations which cause mutations pathogenic, and to predict deleterious mutations which may help to determine the functionally critical regions. RDMAS is freely accessed via

Achieving Lightweight Federated Advertising with Self-Supervised Split Distillation

As an emerging secure learning paradigm in leveraging cross-agency private data, vertical federated learning (VFL) is expected to improve advertising models by enabling the joint learning of complementary user attributes privately owned by the advertiser and the publisher. However, there are two key challenges in applying it to advertising systems: a) the limited scale of labeled overlapping samples, and b) the high cost of real-time cross-agency serving. In this paper, we propose a semi-supervised split distillation framework VFed-SSD to alleviate the two limitations. We identify that: i) there are massive unlabeled overlapped data available in advertising systems, and ii) we can keep a balance between model performance and inference cost by decomposing the federated model. Specifically, we develop a self-supervised task Matched Pair Detection (MPD) to exploit the vertically partitioned unlabeled data and propose the Split Knowledge Distillation (SplitKD) schema to avoid cross-agency serving. Empirical studies on three industrial datasets exhibit the effectiveness of our methods, with the median AUC over all datasets improved by 0.86% and 2.6% in the local deployment mode and the federated deployment mode respectively. Overall, our framework provides an efficient federation-enhanced solution for real-time display advertising with minimal deploying cost and significant performance lift.Comment: Accepted to the Trustworthy Federated Learning workshop of IJCAI2022 (FL-IJCAI22). 6 pages, 3 figures, 3 tables Old title: Semi-Supervised Cross-Silo Advertising with Partial Knowledge Transfe

EvoRSR: an integrated system for exploring evolution of RNA structural robustness

BACKGROUND: Robustness, maintaining a constant phenotype despite perturbations, is a fundamental property of biological systems that is incorporated at various levels of biological complexity. Although robustness has been frequently observed in nature, its evolutionary origin remains unknown. Current hypotheses suggest that robustness originated as a direct consequence of natural selection, as an intrinsic property of adaptations, or as a congruent correlate of environment robustness. To elucidate the evolutionary origins of robustness, a convenient computational package is strongly needed. RESULTS: In this study, we developed the open-source integrated system EvoRSR (Evolution of RNA Structural Robustness) to explore the evolution of robustness based on biologically important landscapes induced by RNA folding. EvoRSR is object-oriented, modular, and freely available at under the GNU/GPL license. We present an overview of EvoRSR package and illustrate its features with the miRNA gene cel-mir-357. CONCLUSION: EvoRSR is a novel and flexible package for exploring the evolution of robustness. Accordingly, EvoRSR can be used for future studies to investigate the evolution and origin of robustness and to address other common questions about robustness. While the current EvoRSR environment is a versatile analysis framework, future versions can include features to enhance evolutionary studies of robustness