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Öğe Computational Methods for Analysis of the DNA-Binding Preferences of Cys2His2 Zinc-Finger Proteins(Humana Press Inc, 2018) Dogan, Berat; Najafabadi, Hamed S.Cys(2)His(2) zinc-finger proteins (C2H2-ZFPs) constitute the largest class of human transcription factors (TFs) and also the least characterized one. Determining the DNA sequence preferences of C2H2-ZFPs is an important first step toward elucidating their roles in transcriptional regulation. Among the most promising approaches for obtaining the sequence preferences of C2H2-ZFPs are those that combine machine--learning predictions with in vivo binding maps of these proteins. Here, we provide a protocol and guidelines for predicting the DNA-binding preferences of C2H2-ZFPs from their amino acid sequences using a machine learning-based recognition code. This protocol also describes the tools and steps to combine these predictions with ChIP-seq data to remove inaccuracies, identify the zinc-finger domains within each C2H2-ZFP that engage with DNA in vivo, and pinpoint the genomic binding sites of the C2H2-ZFPs.Öğe Metabolomic analysis of endometrial cancer by high-resolution magic angle spinning NMR spectroscopy(Springer Heidelberg, 2022) Duz, Senem Arda; Mumcu, Akin; Dogan, Berat; Yilmaz, Ercan; Coskun, Ebru Inci; Saridogan, Erdinc; Tuncay, GorkemPurpose To analyze endometrial metabolite profiles between patients with endometrial cancer and controls. Methods Seventeen (17) women with endometrium cancer and 18 controls were enrolled in this study. H-1 HR-MAS (High Resolution-Magic Angle Spinning) NMR (Nuclear Magnetic Resonance) spectroscopy data obtained from endometrial tissue samples of patients with endometrial cancer and control group were analyzed with bioinformatics methods. Results Principal component analysis (PCA) and the partial least squares discriminant analysis (PLS-DA) score plots obtained with the multivariate statistical analysis of pre-processed spectral data shows a separation between the samples from patients with endometrial cancer and controls. Analysis results suggest that the levels of lactate, glucose, o-phosphoethanolamine, choline, glycerophosphocholine, phosphocholine, leucine, isoleucine, valine, glutamate, glutamine, n-acetyltyrosine, methionine, taurine, alanine, aspartate and phenylalanine are increased in patients with endometrial cancer compared to the controls. Conclusion The metabolomics signature of patients with endometrial cancer is different from that of benign endometrial tissue.Öğe Metabolomics analysis of follicular fluid in women with ovarian endometriosis undergoing in vitro fertilization(Taylor & Francis Inc, 2019) Karaer, Abdullah; Tuncay, Gorkem; Mumcu, Akin; Dogan, BeratThe purpose of this study was to investigate whether a change in the follicular fluid metabolomics profile due to endometrioma is identifiable. Twelve women with ovarian endometriosis (aged<40 years, with a body mass index [BMI] of <30 kg/m(2)) and 12 age- and BMI-matched controls (women with infertility purely due to a male factor) underwent ovarian stimulation for intracytoplasmic sperm injection (ICSI). Follicular fluid samples were collected from both of groups at the time of oocyte retrieval for ICSI. Next, nuclear magnetic resonance (NMR) spectroscopy was performed for the collected follicular fluids. The metabolic compositions of the follicular fluids were then compared using univariate and multivariate statistical analyses of NMR data. Univariate and multivariate statistical analyses of NMR data showed that the metabolomic profiles of the follicular fluids obtained from the women with ovarian endometriosis were distinctly different from those obtained from the control group. In comparison with the controls, the follicular fluids of the women with ovarian endometriosis had statistically significant elevated levels of lactate, beta-glucose, pyruvate, and valine. We conclude that the levels of lactate, beta-glucose, pyruvate, and valine in the follicular fluid of the women with endometrioma were higher than those of the controls.Öğe Metabolomics analysis of placental tissue obtained from patients with fetal growth restriction(Wiley, 2022) Karaer, Abdullah; Mumcu, Akin; Arda, Senem; Tuncay, Gorkem; Dogan, BeratAim The aim of this study was to determine whether there was a difference in placental metabolite profiles between patients with fetal growth restriction (FGR) and healthy controls. Methods The study included 10 patients with FGR diagnosis with 14 healthy controls with both matched maternal age and body mass index. H-1 HR-MAS NMR spectroscopy data obtained from placental tissue samples of patients with FGR and healthy control group were analyzed with bioinformatics methods. The obtained results of metabolite levels were further validated with the internal standard (IS) quantification method. Results Principal component analysis (PCA) and the partial least squares discriminant analysis (PLS-DA) score plots obtained with the multivariate statistical analysis of preprocessed spectral data shows a separation between the samples from patients with FGR and healthy controls. Bioinformatics analysis results suggest that the placental levels of lactate, glutamine, glycerophosphocholine, phosphocholine, taurine, and myoinositol are increased in patients with FGR compared to the healthy controls. Conclusions Placental metabolic dysfunctions are a common occurrence in FGR.Öğe Microarray analysis of cumulus cells in women with ovarian endometriosis undergoing intracytoplasmic sperm injection(Sage Publications Ltd, 2020) Karaer, Abdullah; Tuncay, Gorkem; Dogan, Berat; Tecellioglu, Nihan; Cigremis, YilmazObjective: The aim of this study was to find the significantly altered genes in cumulus cells of women with ovarian endometriosis by using microarray and quantitative polymerase chain reaction analysis. Methods: Thirty women with ovarian endometriosis and 30 age-body mass index matched controls (women with infertility as a result of pure male factor) were enrolled in this study. Cumulus cells from study participants who underwent controlled ovarian hyperstimulation were isolated mechanically. Microarray comparative genomic hybridization was used to compare the transcriptome of cumulus cells from women with ovarian endometriosis and controls. According to the different expression levels in the microarrays and their putative functions, KRAS, ZNF322, and SDHA were selected and analyzed by real-time quantitative polymerase chain reaction. Results: There was no significant difference in the basal conditions between patients with endometriosis and controls, such as age, body mass index, basal follicle stimulating hormone and estradiol levels, and total gonadotrophin dosage. The gene expression profile of cumulus cells from patients with endometriosis was significantly different from that of controls. A total of 295 genes were significantly up- or down-regulated (p-value < 0.05 and absolute fold change > 1.5). For all of the genes adjusted p-value was found to be 0.999. Polymerase chain reaction analysis showed that KRAS and ZNF322 mRNA levels in the cumulus cells of patients with ovarian endometriosis were significantly up-regulated compared to controls (fold changes: 3.05 and 3.22, respectively). Conclusion: KRAS and ZNF322 mRNA levels in the cumulus cells of patients with ovarian endometriosis were significantly up-regulated.Öğe Optimized spatial filters as a new method for mass spectrometry-based cancer diagnosis(Elsevier Science Bv, 2016) Dogan, BeratIn the past two decades, mass spectrometry-based identification of serum proteomic patterns has emerged as a new diagnostic tool for the early detection of various types of cancers. However, due to its high dimensionality, the analysis of mass spectrometry data poses considerable challenges. Existing methods proposed for the analysis of mass spectrometry data usually consist of a number of steps. In this study, a comparatively simple but efficient method, namely, an optimal spatial filter (OSF) method, is proposed for the classification of mass spectrometry data. The newly proposed method is based on the theory of common spatial patterns (CSPs), which are widely used to classify motor imagery EEG signals in brain-computer interface (BCI) applications. The CSP method aims to find spatial filters to project the data into a new space in which optimal discrimination between classes is achieved. Although it has been shown that the CSP method performs quite well in classifying motor imagery EEG signals, it has a major drawback. In the CSP method, the between-class variance is maximized, but the minimization of within-class variance is ignored. As a result, the projected data may have large within-class variances. To overcome this problem, in this study, optimal filters are found by using the differential evolution (DE) algorithm. For the fitness function of the differential evolution algorithm, a divergence analysis is used. In the divergence analysis, both the between-class and within-class distributions of the projected data are considered. The experimental results obtained using publicly available mass spectrometry datasets showed that, when compared to existing methods, the proposed OSF method is quite simple and achieves the minimum classification error for each dataset. Furthermore, the proposed OSF method highlights the importance of certain parts of the spectra, which is highly valuable for the identification of biomarkers that lie outside the pathological pathway of the disease. (C) 2016 Elsevier B.V. All rights reserved.Öğe Protein-RNA Interaction Prediction Using Graphical Representation of Biological Sequences(Ieee, 2019) Dogan, BeratProtein-RNA interactions play a crucial role in post-transcriptional regulation of gene expression and have diverse functions in various biological processes. Therefore, identification of protein-RNA interactions is quite important. Experimental methods used for this purpose are expensive, time-consuming and labor intensive. Alternatively, machine learning based methods are proposed to detect protein-RNA interactions computationally. In these methods, each protein-RNA pair is represented by a feature vector which is then used to train machine learning methods. Here, in this study, we also proposed an alternative method to form a feature vector for each protein-RNA pair. Compared to the existing methods, the proposed method creates low-dimensional feature vectors which in turn decreases the overall computational time required to train and test the machine learning methods. Moreover, the proposed method does not make any concession on the classification performance.Öğe scMAGS: Marker gene selection from scRNA-seq data for spatial transcriptomics studies(Pergamon-Elsevier Science Ltd, 2023) Baran, Yusuf; Dogan, BeratSingle-Cell RNA sequencing (scRNA-seq) has provided unprecedented opportunities for exploring gene expres-sion and thus uncovering regulatory relationships between genes at the single-cell level. However, scRNA-seq relies on isolating cells from tissues. Therefore, the spatial context of the regulatory processes is lost. A recent technological innovation, spatial transcriptomics, allows for the measurement of gene expression while preser-ving spatial information. An initial step in the spatial transcriptomic analysis is to identify the cell type, which requires a careful selection of cell-specific marker genes. For this purpose, currently, scRNA-seq data is used to select a limited number of marker genes from among all genes that distinguish cell types from each other. This study proposes scMAGS (single-cell MArker Gene Selection), a novel method for marker gene selection from scRNA-seq data for spatial transcriptomics studies. scMAGS uses a filtering step in which the candidate genes are identified before the marker gene selection step. For the selection of marker genes, cluster validity indices, the Silhouette index, or the Calinski-Harabasz index (for large datasets) are utilized. Experimental results showed that, in comparison to the existing methods, scMAGS is scalable, fast, and accurate. Even for large datasets with millions of cells, scMAGS could find the required number of marker genes in a reasonable amount of time with fewer memory requirements. scMAGS is made freely available at https://github.com/doganlab/scmags and can be downloaded from the Python Package Directory (PyPI) software repository with the command pip install scmags.
