PubMed İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397
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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.Article Citation - WoS: 3Citation - Scopus: 3NeRNA: A Negative Data Generation Framework for Machine Learning Applications of Noncoding RNAs(Pergamon-Elsevier Science Ltd, 2023-06) Orhan, Mehmet Emin; Demirci, Yilmaz Mehmet; Demirci, Mueserref Duygu Sacar; Saçar Demirci, Müşerref DuyguMany supervised machine learning based noncoding RNA (ncRNA) analysis methods have been developed to classify and identify novel sequences. During such analysis, the positive learning datasets usually consist of known examples of ncRNAs and some of them might even have weak or strong experimental validation. On the contrary, there are neither databases listing the confirmed negative sequences for a specific ncRNA class nor standardized methodologies developed to generate high quality negative examples. To overcome this challenge, a novel negative data generation method, NeRNA (negative RNA), is developed in this work. NeRNA uses known examples of given ncRNA sequences and their calculated structures for octal representation to create negative sequences in a manner similar to frameshift mutations but without deletion or insertion. NeRNA is tested individually with four different ncRNA datasets including MicroRNA (miRNA), transfer RNA (tRNA), long noncoding RNA (lncRNA), and circular RNA (circRNA). Furthermore, a species-specific case analysis is per-formed to demonstrate and compare the performance of NeRNA for miRNA prediction. The results of 1000 fold cross-validation on Decision Tree, Naive Bayes and Random Forest classifiers, and deep learning algorithms such as Multilayer Perceptron, Convolutional Neural Network, and Simple feedforward Neural Networks indicate that models obtained by using NeRNA generated datasets, achieves substantially high prediction performance. NeRNA is released as an easy-to-use, updatable and modifiable KNIME workflow that can be downloaded with example datasets and required extensions. In particular, NeRNA is designed to be a powerful tool for RNA sequence data analysis.Article Citation - WoS: 2Citation - Scopus: 1Computational Prediction of MicroRNAs in Histoplasma Capsulatum(Academic Press Ltd- Elsevier Science Ltd, 2020-11) Demirci, Mueserref Duygu Sagar; Saçar Demirci, Müşerref DuyguMicroRNAs (miRNAs) are small and non-coding RNAs that regulate gene expression through post-transcriptional regulation. Although, the standard miRNA repository, MiRBase, lists more than 200 organisms having miRNA mediated regulation mechanism and thousands of miRNAs, there is not enough information about miRNAs of fungal species. Considering that there are various fungal pathogens causing disease phenotypes, it is important to search for miRNAs of those organisms. The leading cause of endemic mycosis in the USA is a fungal disease known as histoplasmosis, which is resulted by infection with a fungal intracellular parasite, Histoplasma capsulatum (H. capsulatum). In this work, genomes of H. capsulatum strains NAm1 and G217B were explored for potential miRNA like sequences and structures. Through a complex workflow involving miRNA detection and target prediction, several miRNA candidates of H. capsulatum and their possible targets in human were identified. The results presented here indicate that H. capsulatum might be one of the fungal pathogens having a miRNA based post-transcriptional gene regulation mechanism and it might have a miRNA mediated host - parasite interaction with human.Book Part Citation - WoS: 20Citation - Scopus: 27Computational Prediction of Functional MicroRNA-mRNA Interactions(Humana Press Inc, 2019) Demirci, Muserref Duygu Sacar; Yousef, Malik; Allmer, Jens; Saçar Demirci, Müşerref DuyguProteins have a strong influence on the phenotype and their aberrant expression leads to diseases. MicroRNAs (miRNAs) are short RNA sequences which posttranscriptionally regulate protein expression. This regulation is driven by miRNAs acting as recognition sequences for their target mRNAs within a larger regulatory machinery. A miRNA can have many target mRNAs and an mRNA can be targeted by many miRNAs which makes it difficult to experimentally discover all miRNA-mRNA interactions. Therefore, computational methods have been developed for miRNA detection and miRNA target prediction. An abundance of available computational tools makes selection difficult. Additionally, interactions are not currently the focus of investigation although they more accurately define the regulation than pre-miRNA detection or target prediction could perform alone. We define an interaction including the miRNA source and the mRNA target. We present computational methods allowing the investigation of these interactions as well as how they can be used to extend regulatory pathways. Finally, we present a list of points that should be taken into account when investigating miRNA-mRNA interactions. In the future, this may lead to better understanding of functional interactions which may pave the way for disease marker discovery and design of miRNA-based drugs.Article Citation - WoS: 24Citation - Scopus: 24Circular RNA-MicroRNA Interaction Predictions in SARS-CoV Infection(Walter de Gruyter Gmbh, 2021-03-01) Demirci, Yilmaz Mehmet; Demirci, Muserref Duygu Sacar; Saçar Demirci, Müşerref DuyguDifferent types of noncoding RNAs like MicroRNAs (miRNAs) and circular RNAs (circRNAs) have been shown to take part in various cellular processes including post-transcriptional gene regulation during infection. MiRNAs are expressed by more than 200 organisms ranging from viruses to higher eukaryotes. Since miRNAs seem to be involved in host-pathogen interactions, many studies attempted to identify whether human miRNAs could target severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNAs as an antiviral defence mechanism. In this work, a machine learning based miRNA analysis work flow was developed to predict differential expression patterns of human miRNAs during SARS-CoV-2 infection. In order to obtain the graphical representation of miRNA hairpins, 36 features were defined based on the secondary structures. Moreover, potential targeting interactions between human circRNAs and miRNAs as well as human miRNAs and viral mRNAs were investigated.