Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - WoS: 24Citation - Scopus: 27PANOGA: a Web Server for Identification of SNP-Targeted Pathways From Genome-Wide Association Study Data(Oxford Univ Press, 2014-01-11) Bakir-Gungor, Burcu; Egemen, Ece; Sezerman, Osman UgurGenome-wide association studies (GWAS) have revolutionized the search for the variants underlying human complex diseases. However, in a typical GWAS, only a minority of the single-nucleotide polymorphisms (SNPs) with the strongest evidence of association is explained. One possible reason of complex diseases is the alterations in the activity of several biological pathways. Here we present a web server called Pathway and Network-Oriented GWAS Analysis to devise functionally important pathways through the identification of SNP-targeted genes within these pathways. The strength of our methodology stems from its multidimensional perspective, where we combine evidence from the following five resources: (i) genetic association information obtained through GWAS, (ii) SNP functional information, (iii) protein-protein interaction network, (iv) linkage disequilibrium and (v) biochemical pathways.Article Citation - WoS: 53Citation - Scopus: 66Node Similarity-Based Graph Convolution for Link Prediction in Biological Networks(Oxford Univ Press, 2021-06-21) Coskun, Mustafa; Koyuturk, MehmetBackground: Link prediction is an important and well-studied problem in network biology. Recently, graph representation learning methods, including Graph Convolutional Network (GCN)-based node embedding have drawn increasing attention in link prediction. Motivation: An important component of GCN-based network embedding is the convolution matrix, which is used to propagate features across the network. Existing algorithms use the degree-normalized adjacency matrix for this purpose, as this matrix is closely related to the graph Laplacian, capturing the spectral properties of the network. In parallel, it has been shown that GCNs with a single layer can generate more robust embeddings by reducing the number of parameters. Laplacian-based convolution is not well suited to single-layered GCNs, as it limits the propagation of information to immediate neighbors of a node. Results: Capitalizing on the rich literature on unsupervised link prediction, we propose using node similarity-based convolution matrices in GCNs to compute node embeddings for link prediction. We consider eight representative node-similarity measures (Common Neighbors, Jaccard Index, Adamic-Adar, Resource Allocation, Hub- Depressed Index, Hub-Promoted Index, Sorenson Index and Salton Index) for this purpose. We systematically compare the performance of the resulting algorithms against GCNs that use the degree-normalized adjacency matrix for convolution, as well as other link prediction algorithms. In our experiments, we use three-link prediction tasks involving biomedical networks: drug-disease association prediction, drug-drug interaction prediction and protein-protein interaction prediction. Our results show that node similarity-based convolution matrices significantly improve the link prediction performance of GCN-based embeddings. Conclusion: As sophisticated machine-learning frameworks are increasingly employed in biological applications, historically well-established methods can be useful in making a head-start.Article Citation - WoS: 42Citation - Scopus: 42HomSI: A Homozygous Stretch Identifier From Next-Generation Sequencing Data(Oxford Univ Press, 2013-12-03) Gormez, Zeliha; Bakir-Gungor, Burcu; Sagiroglu, Mahmut SamilIn consanguineous families, as a result of inheriting the same genomic segments through both parents, the individuals have stretches of their genomes that are homozygous. This situation leads to the prevalence of recessive diseases among the members of these families. Homozygosity mapping is based on this observation, and in consanguineous families, several recessive disease genes have been discovered with the help of this technique. The researchers typically use single nucleotide polymorphism arrays to determine the homozygous regions and then search for the disease gene by sequencing the genes within this candidate disease loci. Recently, the advent of next-generation sequencing enables the concurrent identification of homozygous regions and the detection of mutations relevant for diagnosis, using data from a single sequencing experiment. In this respect, we have developed a novel tool that identifies homozygous regions using deep sequence data. Using*.vcf (variant call format) files as an input file, our program identifies the majority of homozygous regions found by microarray single nucleotide polymorphism genotype data.Article Citation - WoS: 11Citation - Scopus: 14Geography of Irreducible Plane Sextics(Oxford Univ Press, 2015-11-23) Akyol, Aysegul; Degtyarev, AlexWe complete the equisingular deformation classification of irreducible singular plane sextic curves. As a by-product, we also compute the fundamental groups of the complement of all but a few maximizing sextics.Article Citation - WoS: 13Citation - Scopus: 17Effects of Cell-Mediated Osteoprotegerin Gene Transfer and Mesenchymal Stem Cell Applications on Orthodontically Induced Root Resorption of Rat Teeth(Oxford Univ Press, 2016-10-12) Amuk, Nisa Gul; Kurt, Gokmen; Baran, Yusuf; Seyrantepe, Volkan; Yandim, Melis Kartal; Adan, Aysun; Sonmez, Mehmet FatihAim: The aim of this study is to evaluate and compare therapeutic effects of mesenchymal stem cell (MSCs) and osteoprotegerin (OPG) gene transfer applications on inhibition and/or repair of orthodontically induced inflammatory root resorption (OIIRR). Materials and methods: Thirty Wistar rats were divided into four groups as untreated group (negative control), treated with orthodontic appliance group (positive control), MSCs injection group, and OPG transfected MSCs [gene therapy (GT) group]. About 100 g of orthodontic force was applied to upper first molar teeth of rats for 14 days. MSCs and transfected MSC injections were performed at 1st, 6th, and 11th days to the MSC and GT group rats. At the end of experiment, upper first molar teeth were prepared for genetical, scanning electron microscopy (SEM), fluorescent microscopy, and haematoxylin eosin-tartrate resistant acid phosphatase staining histological analyses. Number of total cells, number of osteoclastic cells, number of resorption lacunae, resorption area ratio, SEM resorption ratio, OPG, RANKL, Cox-2 gene expression levels at the periodontal ligament (PDL) were calculated. Paired t-test, Kruskal-Wallis, and chi-square tests were performed. Results: Transferred MSCs showed marked fluorescence in PDL. The results revealed that number of osteoclastic cells, resorption lacunae, resorption area ratio, RANKL, and Cox-2 were reduced after single MSC injections significantly (P < 0.05). GT group showed the lowest number of osteoclastic cells (P < 0.01), number of resorption lacunae, resorption area ratio, and highest OPG expression (P < 0.001). Conclusions: Taken together all these results, MSCs and GT showed marked inhibition and/or repair effects on OIIRR during orthodontic treatment on rats.Article Citation - WoS: 8Citation - Scopus: 10Developing Structural Profile Matrices for Protein Secondary Structure and Solvent Accessibility Prediction(Oxford Univ Press, 2019-04-01) Aydin, Zafer; Azginoglu, Nuh; Bilgin, Halil Ibrahim; Celik, MeteMotivation: Predicting secondary structure and solvent accessibility of proteins are among the essential steps that preclude more elaborate 3D structure prediction tasks. Incorporating class label information contained in templates with known structures has the potential to improve the accuracy of prediction methods. Building a structural profile matrix is one such technique that provides a distribution for class labels at each amino acid position of the target. Results: In this paper, a new structural profiling technique is proposed that is based on deriving PFAM families and is combined with an existing approach. Cross-validation experiments on two benchmark datasets and at various similarity intervals demonstrate that the proposed profiling strategy performs significantly better than Homolpro, a state-of-the-art method for incorporating template information, as assessed by statistical hypothesis tests.Article Citation - WoS: 18Citation - Scopus: 17ConVarT: A Search Engine for Matching Human Genetic Variants With Variants From Non-Human Species(Oxford Univ Press, 2021-10-28) Pir, Mustafa S.; Bilgin, Halil, I; Sayici, Ahmet; Torun, Furkan M.; Zhao, Pei; Kang, Yahong; Kaplan, Oktay, I; Coşkun, FatihThe availability of genetic variants, togetherwith phenotypic annotations from model organisms, facilitates comparing these variants with equivalent variants in humans. However, existing databases and search tools do not make it easy to scan for equivalent variants, namely 'matching variants' (MatchVars) between humans and other organisms. Therefore, we developed an integrated search engine called ConVarT (http://www.convart.org/) for matching variants between humans, mice, and Caenorhabditis elegans. ConVarT incorporates annotations (including phenotypic and pathogenic) into variants, and these previously unexploited phenotypic MatchVars from mice and C. elegans can give clues about the functional consequence of human genetic variants. Our analysis shows that many phenotypic variants in different genes from mice and C. elegans, so far, have no counterparts in humans, and thus, can be useful resources when evaluating a relationship between a new human mutation and a disease.Article Citation - WoS: 18Citation - Scopus: 16Ciliogenics: An Integrated Method and Database for Predicting Novel Ciliary Genes(Oxford Univ Press, 2024-07-11) Pir, Mustafa S.; Begar, Efe; Yenisert, Ferhan; Demirci, Hasan C.; Korkmaz, Mustafa E.; Karaman, Asli; Kaplan, Oktay, IUncovering the full list of human ciliary genes holds enormous promise for the diagnosis of cilia-related human diseases, collectively known as ciliopathies. Currently, genetic diagnoses of many ciliopathies remain incomplete (). While various independent approaches theoretically have the potential to reveal the entire list of ciliary genes, approximately 30% of the genes on the ciliary gene list still stand as ciliary candidates (,). These methods, however, have mainly relied on a single strategy to uncover ciliary candidate genes, making the categorization challenging due to variations in quality and distinct capabilities demonstrated by different methodologies. Here, we develop a method called CilioGenics that combines several methodologies (single-cell RNA sequencing, protein-protein interactions (PPIs), comparative genomics, transcription factor (TF) network analysis, and text mining) to predict the ciliary capacity of each human gene. Our combined approach provides a CilioGenics score for every human gene that represents the probability that it will become a ciliary gene. Compared to methods that rely on a single method, CilioGenics performs better in its capacity to predict ciliary genes. Our top 500 gene list includes 258 new ciliary candidates, with 31 validated experimentally by us and others. Users may explore the whole list of human genes and CilioGenics scores on the CilioGenics database (https://ciliogenics.com/). Graphical Abstract