PubMed İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397
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Article TEffectBayes: A Nextflow Pipeline for Exploring the Potential Effect of Transposable Elements in Gene Regulatory Network with Multi-Omic Bayesian Network Model(Springer Heidelberg, 2026-03-10) Karakülah, Gökhan; Güner, Hüseyin; Kutlu, Necati KaanTransposable elements (TEs) are critical contributors to gene regulatory networks, yet their repetitive and abundant nature complicates efforts to elucidate their precise regulatory roles. While existing computational tools facilitate systematic identification of associations between TEs and gene expression, these methods typically cannot account for confounding variables or capture causal and directional interactions. To address these limitations, we developed TEffectBayes, a Nextflow-based pipeline leveraging a multi-omic Bayesian network (BN) framework designed to systematically infer directional, probabilistic regulatory dependencies involving TEs. TEffectBayes integrates diverse omics datasets, including RNA-seq-derived gene and locus-specific TE expression, along with ChIP-seq-based histone modification data processed via custom R and Python scripts. Integrated multi-omic datasets are subsequently employed to build gene-centric Bayesian models, enabling robust inference of context-dependent, probabilistic relationships between TEs, chromatin modifications, and gene expression. TEffectBayes thus provides a reproducible and scalable computational framework for unraveling the complex regulatory landscape shaped by TEs. In summary, TEffectBayes supports systematic prioritization of TE-chromatin-gene regulatory candidates for downstream benchmarking and experimental validation, enabling hypothesis-driven follow-up studies in diverse biological contexts. The pipeline, along with comprehensive user tutorials and example datasets, is publicly accessible at https://github.com/nkaan-kutlu/TEffectBayes.Article Citation - WoS: 26Citation - Scopus: 33miRmoduleNet: Detecting miRNA-mRNA Regulatory Modules(Frontiers Media S.A., 2022-04-12) Yousef, Malik; Goy, Gokhan; Bakir-Gungor, BurcuIncreasing evidence that MicroRNAs (miRNAs) play a key role in carcinogenesis has revealed the need for elucidating the mechanisms of miRNA regulation and the roles of miRNAs in gene-regulatory networks. A better understanding of the interactions between miRNAs and their mRNA targets will provide a better understanding of the complex biological processes that occur during carcinogenesis. Increased efforts to reveal these interactions have led to the development of a variety of tools to detect and understand these interactions. We have recently described a machine learning approach miRcorrNet, based on grouping and scoring (ranking) groups of genes, where each group is associated with a miRNA and the group members are genes with expression patterns that are correlated with this specific miRNA. The miRcorrNet tool requires two types of -omics data, miRNA and mRNA expression profiles, as an input file. In this study we describe miRModuleNet, which groups mRNA (genes) that are correlated with each miRNA to form a star shape, which we identify as a miRNA-mRNA regulatory module. A scoring procedure is then applied to each module to further assess their contribution in terms of classification. An important output of miRModuleNet is that it provides a hierarchical list of significant miRNA-mRNA regulatory modules. miRModuleNet was further validated on external datasets for their disease associations, and functional enrichment analysis was also performed. The application of miRModuleNet aids the identification of functional relationships between significant biomarkers and reveals essential pathways involved in cancer pathogenesis.Article Citation - WoS: 26Citation - Scopus: 31miRcorrNet: Machine Learning-Based Integration of miRNA and mRNA Expression Profiles, Combined with Feature Grouping and Ranking(PeerJ Inc., 2021-05-19) Yousef, M.; Göy, G.; Mitra, R.; Eischen, C.M.; Jabeer, A.; Bakir-Güngör, B.A better understanding of disease development and progression mechanisms at the molecular level is critical both for the diagnosis of a disease and for the development of therapeutic approaches. The advancements in high throughput technologies allowed to generate mRNA and microRNA (miRNA) expression profiles; and the integrative analysis of these profiles allowed to uncover the functional effects of RNA expression in complex diseases, such as cancer. Several researches attempt to integrate miRNA and mRNA expression profiles using statistical methods such as Pearson correlation, and then combine it with enrichment analysis. In this study, we developed a novel tool called miRcorrNet, which performs machine learning-based integration to analyze miRNA and mRNA gene expression profiles. miRcorrNet groups mRNAs based on their correlation to miRNA expression levels and hence it generates groups of target genes associated with each miRNA. Then, these groups are subject to a rank function for classification. We have evaluated our tool using miRNA and mRNA expression profiling data downloaded from The Cancer Genome Atlas (TCGA), and performed comparative evaluation with existing tools. In our experiments we show that miRcorrNet performs as good as other tools in terms of accuracy (reaching more than 95% AUC value). Additionally, miRcorrNet includes ranking steps to separate two classes, namely case and control, which is not available in other tools. We have also evaluated the performance of miRcorrNet using a completely independent dataset. Moreover, we conducted a comprehensive literature search to explore the biological functions of the identified miRNAs. We have validated our significantly identified miRNA groups against known databases, which yielded about 90% accuracy. Our results suggest that miRcorrNet is able to accurately prioritize pan-cancer regulating high-confidence miRNAs. miRcorrNet tool and all other supplementary files are available at https://github.com/ malikyousef/miRcorrNet. © 2021 Elsevier B.V., All rights reserved.Article Citation - WoS: 15Citation - Scopus: 15PriPath: Identifying Dysregulated Pathways From Differential Gene Expression via Grouping, Scoring, and Modeling With an Embedded Feature Selection Approach(BMC, 2023-02-23) Yousef, Malik; Ozdemir, Fatma; Jaber, Amhar; Allmer, Jens; Bakir-Gungor, BurcuBackgroundCell homeostasis relies on the concerted actions of genes, and dysregulated genes can lead to diseases. In living organisms, genes or their products do not act alone but within networks. Subsets of these networks can be viewed as modules that provide specific functionality to an organism. The Kyoto encyclopedia of genes and genomes (KEGG) systematically analyzes gene functions, proteins, and molecules and combines them into pathways. Measurements of gene expression (e.g., RNA-seq data) can be mapped to KEGG pathways to determine which modules are affected or dysregulated in the disease. However, genes acting in multiple pathways and other inherent issues complicate such analyses. Many current approaches may only employ gene expression data and need to pay more attention to some of the existing knowledge stored in KEGG pathways for detecting dysregulated pathways. New methods that consider more precompiled information are required for a more holistic association between gene expression and diseases.ResultsPriPath is a novel approach that transfers the generic process of grouping and scoring, followed by modeling to analyze gene expression with KEGG pathways. In PriPath, KEGG pathways are utilized as the grouping function as part of a machine learning algorithm for selecting the most significant KEGG pathways. A machine learning model is trained to differentiate between diseases and controls using those groups. We have tested PriPath on 13 gene expression datasets of various cancers and other diseases. Our proposed approach successfully assigned biologically and clinically relevant KEGG terms to the samples based on the differentially expressed genes. We have comparatively evaluated the performance of PriPath against other tools, which are similar in their merit. For each dataset, we manually confirmed the top results of PriPath in the literature and found that most predictions can be supported by previous experimental research.ConclusionsPriPath can thus aid in determining dysregulated pathways, which applies to medical diagnostics. In the future, we aim to advance this approach so that it can perform patient stratification based on gene expression and identify druggable targets. Thereby, we cover two aspects of precision medicine.Article Citation - WoS: 36Citation - Scopus: 37Histopathological and Biomechanical Evaluation of Tenocyte Seeded Allografts on Rat Achilles Tendon Regeneration(Elsevier Sci Ltd, 2015-05) Gungormus, Cansin; Kolankaya, Durdane; Aydin, ErkinTendon injuries in humans as well as in animals' veterinary medicine are problematic because tendon has poor regenerative capacity and complete regeneration of the ruptured tendon is never achieved. In the last decade there has been an increasing need of treatment methods with different approaches. The aim of the current study was to improve the regeneration process of rat Achilles tendon with tenocyte seeded decellularized tendon matrices. For this purpose, Achilles tendons were harvested, decellularized and seeded as a mixture of three consecutive passages of tenocytes at a density of 1 x 10(6) cells/ml. Specifically, cells with different passage numbers were compared with respect to growth characteristics, cellular senescence and collagen/tenocyte marker production before seeding process. The viability of reseeded tendon constructs was followed postoperatively up to 6 months in rat Achilles tendon by histopathological and biomechanical analysis. Our results suggests that tenocyte seeded decellularized tendon matrix can significantly improve the histological and biomechanical properties of tendon repair tissue without causing adverse immune reactions. To the best of our knowledge, this is the first long-term study in the literature which was accomplished to prove the use of decellularized matrix in a clinically relevant model of rat Achilles tendon and the method suggested herein might have important implications for translation into the clinic. (C) 2015 Elsevier Ltd. All rights reserved.Article Citation - WoS: 4Citation - Scopus: 4Evaluation of Selected Plant Phenolics Via Beta-Secretase Inhibition, Molecular Docking, and Gene Expression Related to Alzheimer's Disease(MDPI, 2024-10-28) Akyurek, Tugba Ucar; Orhan, Ilkay Erdogan; Deniz, F. Sezer Senol; Eren, Gokcen; Acar, Busra; Sen, Alaattin; Şenol Deniz, F. Sezer; Uçar Akyürek, TugbaBackground: The goal of the current study was to investigate the inhibitory activity of six phenolic compounds, i.e., rosmarinic acid, gallic acid, oleuropein, epigallocatechin gallate (EGCG), 3-hydroxytyrosol, and quercetin, against beta-site amyloid precursor protein cleaving enzyme-1 (BACE1), also known as beta-secretase or memapsin 2, which is implicated in the pathogenesis of Alzheimer's disease (AD). Methods and Results: The inhibitory potential against BACE1, molecular docking simulations, as well as neurotoxicity and the effect on the AD-related gene expression of the selected phenolics were tested. BACE1 inhibitory activity was carried out using the ELISA microplate assay via fluorescence resonance energy transfer (FRET) technology. Molecular docking experiments were performed in the human BACE1 active site (PDB code: 2WJO). Neurotoxicity of the compounds was carried out in SH-SY5Y, a human neuroblastoma cell line, by the Alamar Blue method. A gene expression analysis of the compounds on fourteen genes linked to AD was conducted using the real-time polymerase chain reaction (RT-PCR) method. Rosmarinic acid, EGCG, oleuropein, and quercetin (also used as the reference) were able to inhibit BACE1 with their respective IC50 values 4.06 +/- 0.68, 1.62 +/- 0.12, 9.87 +/- 1.01, and 3.16 +/- 0.30 mM. The inhibitory compounds were observed to occupy the non-catalytic site of the BACE1. However, hydrogen bonds were found to be present between rosmarinic acid and EGCG and aspartic amino acid D228 in the catalytic site. Oleuropein and quercetin effectively suppressed the expression of PSEN, APOE, and CLU, which are recognized to be linked to the pathogenesis of AD. Conclusions: The outcomes of the work bring quercetin, EGCG, and rosmarinic acid to the forefront as promising BACE1 inhibitors.