PubMed İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397
Browse
2 results
Search Results
Article Integrative Bioinformatics Prediction of West Nile Virus-Derived microRNAs Reveals Potential Host Regulatory Interactions(Elsevier Sci Ltd, 2026-08) Demirci, Muserref Duygu Sacar; Orhan, Mehmet Emin; Erginkoc, Altay Nida; Saçar Demirci, Müşerref DuyguWest Nile virus (WNV) is a mosquito-borne flavivirus linked to severe neuroinvasive disease. Although host and vector microRNAs (miRNAs) have been implicated in viral infection, the presence and functional relevance of WNV-encoded miRNAs remain largely unexplored. Here, we developed an integrative bioinformatics pipeline that combines multiple miRNA prediction algorithms with secondary structure screening and host transcriptomic data to identify high-confidence candidate WNV-derived mature miRNAs. Overlap-based confidence scoring and differential expression support from RNA-seq datasets prioritized a small subset of putative miRNA-mRNA interactions with potential roles in infection-associated gene regulation. A competitive endogenous RNA network constructed from predicted mRNA, lncRNA, and circRNA targets highlighted pathways involving innate immunity, GPCR and Wnt signaling, RNA degradation, and viral replication. Together, these findings provide a reproducible computational workflow and nominate testable regulatory interactions for future experimental validation.Book Part Citation - Scopus: 4Computational Detection of Pre-MicroRNAs(Humana Press Inc., 2021-08-26) Saçar Demirci, Müşerref DuyguMicroRNA (miRNA) studies have been one of the most popular research areas in recent years. Although thousands of miRNAs have been detected in several species, the majority remains unidentified. Thus, finding novel miRNAs is a vital element for investigating miRNA mediated posttranscriptional gene regulation machineries. Furthermore, experimental methods have challenging inadequacies in their capability to detect rare miRNAs, and are also limited to the state of the organism under examination (e.g., tissue type, developmental stage, stress-disease conditions). These issues have initiated the creation of high-level computational methodologies endeavoring to distinguish potential miRNAs in silico. On the other hand, most of these tools suffer from high numbers of false positives and/or false negatives and as a result they do not provide enough confidence for validating all their predictions experimentally. In this chapter, computational difficulties in detection of pre-miRNAs are discussed and a machine learning based approach that has been designed to address these issues is reviewed. © 2021 Elsevier B.V., All rights reserved.