IntroductionRapid and high-throughput screening of antiviral clustered regularly interspaced short palindromic repeat (CRISPR) RNAs (crRNAs) is urgently required for the CRISPR-Cas13a antiviral system. Based on the same principle, we established an efficient screening platform for antiviral crRNA through CRISPR-Cas13a nucleic acid detection.MethodIn this study, crRNAs targeting PA, PB1, NP, and PB2 of the influenza A virus (H1N1) were screened using CRISPR-Cas13a nucleic acid detection, and their antiviral effects were confirmed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The RNA secondary structures were predicted by bioinformatics methods.ResultsThe results showed that crRNAs screened by CRISPR-Cas13a nucleic acid detection could effectively inhibit viral RNA in mammalian cells. Besides, we found that this platform for antiviral crRNA screening was more accurate than RNA secondary structure prediction. In addition, we validated the feasibility of the platform by screening crRNAs targeting NS of the influenza A virus (H1N1).DiscussionThis study provides a new approach for screening antiviral crRNAs and contributes to the rapid advancement of the CRISPR-Cas13a antiviral system.

This paper proposes an approach to querying a relational database D and a graph G taken together in SQL. We introduce a semantic extension of joins across D and G such that if a tuple t in D and a vertex v in G refer to the same real-world entity, then we join t and v to correlate their information and complement tuple t with additional properties of vertex v from the graph. Moreover, we extract hidden relationships between t and other entities by exploring paths from v. To support the semantic joins, we develop an extraction scheme based on LSTM, path clustering and ranking, to fetch important properties from graphs, and incrementally maintain the extracted data in response to updates. We also provide methods for implementing static joins when t is a tuple in D, dynamic joins when t comes from the intermediate result of a sub-query, and heuristic joins to strike a balance between the complexity and accuracy. Using reallife data and queries, we experimentally verify the effectiveness, scalability and efficiency of the methods.