Saçar Demirci, Müşerref Duygu
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Demirci, MDS
Demirci, Mueserref Duygu Sacar
Demirci, Mueserref Duygu Sagar
Demirci, Muserref Duygu Sacar
Demirci, Müşerref Duygu Saçar
Sacar Demirci, Muserref Duygu
Saçar Demirci, Müşerref Duygu
Demirci, Mueserref Duygu Sacar
Demirci, Mueserref Duygu Sagar
Demirci, Muserref Duygu Sacar
Demirci, Müşerref Duygu Saçar
Sacar Demirci, Muserref Duygu
Saçar Demirci, Müşerref Duygu
Job Title
Doç. Dr.
Email Address
duygu.sacar@agu.edu.tr
Main Affiliation
04.01. Biyomühendislik
Status
Current Staff
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Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
3
GOOD HEALTH AND WELL-BEING
2
Research Products
Documents
23
Citations
457
h-index
11
Documents
0
Citations
0
Scholarly Output
12
Articles
8
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103/131
Supervised MSc Theses
1
Supervised PhD Theses
0
WoS Citation Count
201
Scopus Citation Count
215
WoS h-index
3
Scopus h-index
4
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0
Projects
1
WoS Citations per Publication
16.75
Scopus Citations per Publication
17.92
Open Access Source
7
Supervised Theses
1
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| Journal | Count |
|---|---|
| Methods in Molecular Biology | 2 |
| Eskişehir Technical University Journal of Science and and Technology A- Applied Sciences and Engineering | 1 |
| Gazi Medical Journal | 1 |
| Journal of Integrative Bioinformatics | 1 |
| Journal of Molecular Neuroscience | 1 |
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Now showing 1 - 10 of 12
Article A Comprehensive MicroRNA-seq Transcriptomic Analysis of Tay-Sachs Disease Mice Revealed Distinct miRNA Profiles in Neuroglial Cells(Springernature, 2025) Kaya, Beyza; Orhan, Mehmet Emin; Yanbul, Selman; Demirci, Muserref Duygu Sacar; Demir, Secil Akyildiz; Seyrantepe, VolkanTay-Sachs disease (TSD) is a rare lysosomal storage disorder marked by the progressive buildup of GM2 in the central nervous system (CNS). This condition arises from mutations in the HEXA gene, which encodes the alpha subunit of the enzyme beta-hexosaminidase A. A newly developed mouse model for early-onset TSD (Hexa-/-Neu3-/-) exhibited signs of neurodegeneration and neuroinflammation, evidenced by elevated levels of pro-inflammatory cytokines and chemokines, as well as significant astrogliosis and microgliosis. Identifying disease-specific MicroRNAs (miRNAs) may aid the development of targeted therapies. Although previous small-scale studies have investigated miRNA expression in some regions of GM2 gangliosidosis mouse models, thorough profiling of miRNAs in this innovative TSD model remains to be done. In this study, we employed next-generation sequencing to analyze the complete miRNA profile of neuroglial cells from Hexa-/-Neu3-/- mice. By comparing KEGG and Reactome pathways associated with neurodegeneration, neuroinflammation, and sphingolipid metabolism in Hexa-/-Neu3-/- neuroglial cells, we discovered new MicroRNAs and their targets related to the pathophysiology of GM2 gangliosidosis. For the first time, our findings showed that miR-708-5p, miR-672-5p, miR-204-5p, miR-335-5p, and miR-296-3p were upregulated, while miR-10 b-5p, miR-615-3p, miR-196a-5p, miR-214-5p, and miR-199a-5p were downregulated in Hexa-/-Neu3-/- neuroglial cells in comparison to age-matched wild-type (WT). These specific changes in miRNA expression deepen our understanding of the disease's neuropathological characteristics in Hexa-/-Neu3-/- mice. Our study suggests that miRNA-based therapeutic strategies may improve clinical outcomes for TSD patients.Master Thesis RNA Etkileşimlerinin İn Silico Analizi(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Orhan, Mehmet Emin; Demirci, Müşerref Duygu SaçarMany supervised machine learning models have been developed for the classification and identification of non-coding RNA (ncRNA) sequences. These models play a significant role in the diagnosis and treatment of various diseases. During such analyses, positive learning datasets typically consist of known ncRNA examples, some of which may even be confirmed with strong experimental evidence. However, there is no database of validated negative sequences for ncRNA classes or standardized methodologies for generating high quality negative samples. To overcome this challenge, a new method for generating negative data called the NeRNA (Negative RNA) method has been developed in this study. NeRNA generates negative sequences using known ncRNA sequences and their octal representations, similar with frame shift mutations found in biology but without base deletions or insertions. In this thesis, the NeRNA method was tested separately with four different ncRNA datasets, including microRNA (miRNA), transfer RNA (tRNA), long non-coding RNA (lncRNA), and circular RNA (circRNA). Additionally, a species-specific case study was conducted to demonstrate and compare the performance of the study's miRNA predictions. The results of 1000-fold cross-validation on machine learning algorithms such as Decision Trees, Naive Bayes, Random Forest classifiers, and deep learning algorithms like Multilayer Perceptrons, Convolutional Neural Networks, and Simple Feedforward Neural Networks showed that models developed using datasets generated by NeRNA exhibited significantly high prediction performance. NeRNA has been published as an easy-to-use, updatable, and modifiable KNIME workflow, along with example datasets and required extensions that can be downloaded and utilized. NeRNA is designed specifically as a powerful tool for RNA sequence data analysis.Research Project RNA İkincil Yapılarının Çok Boyutlu Gösterimi ve Pre-Mirna Tespiti Için Uygulamaları(TUBİTAK, 2021) Saçar Demirci, Müşerref Duygu; Demirci, Yilmaz MehmetMikroRNA'lar (miRNA'lar), transkripsiyon sonrası gen ekspresyonu düzenleyicileridir. Bir_x000D_ miRNA yüzlerce haberci RNA'yı (mRNA'lar) hedefleyebildiği gibi, bir mRNA farklı miRNA'lar_x000D_ tarafından hedeflenebilir, üstelik tek bir miRNA bir mRNA sekansında çeşitli bağlanma_x000D_ bölgelerine sahip olabilir. Bu nedenle miRNA'ları deneysel olarak araştırmak oldukça_x000D_ karmaşıktır. Bu tür zorlukları aşabilmek için makine öğrenimi (ML) sıklıkla kullanılmaktadır._x000D_ ML analizinin temel kısımları büyük ölçüde giriş verilerinin kalitesine ve verileri tanımlayan_x000D_ özelliklerin kapasitesine bağlıdır. Daha önce miRNA'lar için 1000'den fazla özellik önerilmişti._x000D_ Bu projede, RNA ikincil yapısını temsil eden yeni özellikler ve yüksek doğruluk değerleri_x000D_ sağlayan, dinamik, çok boyutlu grafik gösterimini tanımlamayı hedeflemiştik. Bu çalışmada,_x000D_ ML tabanlı miRNA tahmini için yeni ve kolayca güncellenebilir bir yaklaşım geliştirilmiştir._x000D_ Bilinen insan miRNA'larının ve sözde saç tokalarının random forest (RF), support vector_x000D_ machine (SVM) ve multilayer perceptron (MLP) gibi çeşitli sınıflandırıcılarla_x000D_ sınıflandırılmasıyla binlerce model oluşturulmuştur. Yöntem insan verilerine dayanarak_x000D_ oluşturulmuş olsa da en iyi model miRBase ve MirGeneDB gibi kamu veri tabanlarından_x000D_ insan olmayan saç tokaları üzerinde test edilmiş ve yüksek skorlar üretilmiştir. Ayrıca,_x000D_ yöntemin farklı veriler üzerindeki etkinliğini göstermek için ekspresyon farkları tahmini_x000D_ (differential expression prediction) analizinde de kullanılmıştır. Bu aşamada SARS-CoV-2_x000D_ enfeksiyonunun etkisini ölçen bir veri setinin analizinden elde edilen sonuçlar yayınlanmıştır.Article Citation - WoS: 3Citation - Scopus: 3MicroRNA Prediction Based on 3D Graphical Representation of RNA Secondary Structures(Tubitak Scientific & Technological Research Council Turkey, 2019) Sacar Demirci, Muserref DuyguMicroRNAs (miRNAs) are posttranscriptional regulators of gene expression. While a miRNA can target hundreds of messenger RNA (mRNAs), an mRNA can be targeted by different miRNAs, not to mention that a single miRNA might have various binding sites in an mRNA sequence. Therefore, it is quite involved to investigate miRNAs experimentally. Thus, machine learning (ML) is frequently used to overcome such challenges. The key parts of a ML analysis largely depend on the quality of input data and the capacity of the features describing the data. Previously, more than 1000 features were suggested for miRNAs. Here, it is shown that using 36 features representing the RNA secondary structure and its dynamic 3D graphical representation provides up to 98% accuracy values. In this study, a new approach for ML-based miRNA prediction is proposed. Thousands of models are generated through classification of known human miRNAs and pseudohairpins with 3 classifiers: decision tree, naive Bayes, and random forest. Although the method is based on human data, the best model was able to correctly assign 96% of nonhuman hairpins from MirGeneDB, suggesting that this approach might be useful for the analysis of miRNAs from other species.Article Citation - WoS: 2Citation - Scopus: 1Computational Prediction of MicroRNAs in Histoplasma Capsulatum(Academic Press Ltd- Elsevier Science Ltd, 2020) Demirci, Mueserref Duygu SagarMicroRNAs (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.Article Citation - WoS: 151Citation - Scopus: 155Computational Analysis of MicroRNA-Mediated Interactions in SARS-CoV Infection(PeerJ Inc, 2020) Demirci, Muserref Duygu Sacar; Adan, AysunMicroRNAs (miRNAs) are post-transcriptional regulators of gene expression found in more than 200 diverse organisms. Although it is still not fully established if RNA viruses could generate miRNAs, there are examples of miRNA like sequences from RNA viruses with regulatory functions. In the case of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), there are several mechanisms that would make miRNAs impact the virus, like interfering with viral replication, translation and even modulating the host expression. In this study, we performed a machine learning based miRNA prediction analysis for the SARS-CoV-2 genome to identify miRNA-like hairpins and searched for potential miRNA-based interactions between the viral miRNAs and human genes and human miRNAs and viral genes. Overall, 950 hairpin structured sequences were extracted from the virus genome and based on the prediction results, 29 of them could be precursor miRNAs. Targeting analysis showed that 30 viral mature miRNA-like sequences could target 1,367 different human genes. PANTHER gene function analysis results indicated that viral derived miRNA candidates could target various human genes involved in crucial cellular processes including transcription, metabolism, defense system and several signaling pathways such as Wnt and EGFR signalings. Protein class-based grouping of targeted human genes showed that host transcription might be one of the main targets of the virus since 96 genes involved in transcriptional processes were potential targets of predicted viral miRNAs. For instance, basal transcription machinery elements including several components of human mediator complex (MED1, MED9, MED 12L, MED 19), basal transcription factors such as TAF4, TAF5, TAF7L and site-specific transcription factors such as STATI were found to be targeted. In addition, many known human miRNAs appeared to be able to target viral genes involved in viral life cycle such as S, M, N, E proteins and ORF lab, ORF3a, ORF8, ORF7a and ORF10. Considering the fact that miRNA-based therapies have been paid attention, based on the findings of this study, comprehending mode of actions of miRNAs and their possible roles during SARS-CoV-2 infections could create new opportunities for the development and improvement of new therapeutics.Article Citation - WoS: 2Citation - Scopus: 2NeRNA: A Negative Data Generation Framework for Machine Learning Applications of Noncoding RNAs(Pergamon-Elsevier Science Ltd, 2023) Orhan, Mehmet Emin; Demirci, Yilmaz Mehmet; Demirci, Mueserref Duygu SacarMany 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 Computational Identification of MicroRNAs From Ssdna Viruses(2018) Demirci, Müşerref Duygu SaçarMicroRNAs (miRNAs) are post-transcriptional regulators of gene expression and the fact that they are associated with variousdisease phenotypes is one of the main reasons for their importance. The complexity of experimental detection of miRNAs dueto their characteristics led to the development of computational methods. In this work, a machine learning based approach wasapplied to identify and analyze potential miRNAs that might be originated from 60 single strand DNA (ssDNA) viruses’genomes. The results suggest that 53 of these viruses may possibly produce proper miRNA precursors. Moreover, thepossibility of these candidate miRNA precursors’ ability to generate mature miRNAs that could target human genes and viralgenomes has been tested. Overall, the outcomes of this research indicate that there might be another level of host-virusinteraction through miRNAs which requires further experimental confirmation.Book Part Citation - WoS: 19Citation - Scopus: 26Computational Prediction of Functional MicroRNA-mRNA Interactions(Humana Press Inc, 2019) Demirci, Muserref Duygu Sacar; Yousef, Malik; Allmer, JensProteins 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) Demirci, Yilmaz Mehmet; Demirci, Muserref Duygu SacarDifferent 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.
