Browsing by Author "Celik, Mete"

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Ceph-based Storage Server Application
(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2018) Azginoglu, Nuh; Eren, Mehmet Akif; Celik, Mete; Aydin, Zafer; 0000-0002-4074-7366; 0000-0002-1488-1502; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
Ceph is a scalable and high performance distributed file system. In this study, a Ceph-based storage server was implemented and used actively. This storage system has been used as a disk of 40 virtual servers in 4 different Proxmox servers. Performance evaluation of the system has been conducted on virtual servers that holds Windows and Linux based operating systems.
Developing structural profile matrices for protein secondary structure and solvent accessibility prediction
(OXFORD UNIV PRESS, GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND, 2019) Aydin, Zafer; Azginoglu, Nuh; Bilgin, Halil Ibrahim; Celik, Mete; 0000-0002-4074-7366; 0000-0002-1488-1502; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
Motivation: 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.
Multi fragment melting analysis system (MFMAS) for one-step identification of lactobacilli
(ELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS, 2020) Kesmen, Zulal; Kilic, Ozge; Gormez, Yasin; Celik, Mete; Bakir-Gungor, Burcu; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
The accurate identification of lactobacilli is essential for the effective management of industrial practices associated with lactobacilli strains, such as the production of fermented foods or probiotic supplements. For this reason, in this study, we proposed the Multi Fragment Melting Analysis System (MFMAS)-lactobacilli based on high resolution melting (HRM) analysis of multiple DNA regions that have high interspecies heterogeneity for fast and reliable identification and characterization of lactobacilli. The MFMAS-lactobacilli is a new and customized version of the MFMAS, which was developed by our research group. MFMAS-lactobacilli is a combined system that consists of i) a ready-to-use plate, which is designed for multiple HRM analysis, and ii) a data analysis software, which is used to characterize lactobacilli species via incorporating machine learning techniques. Simultaneous HRM analysis of multiple DNA fragments yields a fingerprint for each tested strain and the identification is performed by comparing the fingerprints of unknown strains with those of known lactobacilli species registered in the MFMAS. In this study, a total of 254 isolates, which were recovered from fermented foods and probiotic supplements, were subjected to MFMAS analysis, and the results were confirmed by a combination of different molecular techniques. All of the analyzed isolates were exactly differentiated and accurately identified by applying the single-step procedure of MFMAS, and it was determined that all of the tested isolates belonged to 18 different lactobacilli species. The individual analysis of each target DNA region provided identification with an accuracy range from 59% to 90% for all tested isolates. However, when each target DNA region was analyzed simultaneously, perfect discrimination and 100% accurate identification were obtained even in closely related species. As a result, it was concluded that MFMAS-lactobacilli is a multi-purpose method that can be used to differentiate, classify, and identify lactobacilli species. Hence, our proposed system could be a potential alternative to overcome the inconsistencies and difficulties of the current methods.
Open Source Slurm Computer Cluster System Design and a Sample Application
(IEEE345 E 47TH ST, NEW YORK, NY 10017 USA, 2017) Azginoglu, Nuh; Atasever, Mehmet Umt; Aydin, Zafer; Celik, Mete; Erbay, Hasan; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Aydin, Zafer
Cluster computing combines the resources of multiple computers as they act like a single high-performance computer. In this study, a computer cluster consisting of Lustre distributed file system with one cluster server based on Slurm resource management system and thirteen calculation nodes were built by using available and inert computers that have different processors. Different bioinformatics algorithms were run using different data sets in the cluster, and the performance of the clusters was evaluated with the amount of time the computing cluster spent to finish the jobs.
Structural profile matrices for predicting structural properties of proteins
(WORLD SCIENTIFIC PUBL CO PTE LTD, 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE, 2020) Azginoglu, Nuh; Aydin, Zafer; Celik, Mete; 0000-0002-4074-7366; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
Predicting structural properties of proteins plays a key role in predicting the 3D structure of proteins. In this study, new structural profile matrices (SPM) are developed for protein secondary structure, solvent accessibility and torsion angle class predictions, which could be used as input to 3D prediction algorithms. The structural templates employed in computing SPMs are detected by eight alignment methods in LOMETS server, gap affine alignment method, ScanProsite, PfamScan, and HHblits. The contribution of each template is weighted by its similarity to target, which is assessed by several sequence alignment scores. For comparison, the SPMs are also computed using Homolpro, which uses BLAST for target template alignments and does not assign weights to templates. Incorporating the SPMs into DSPRED classifier, the prediction accuracy improves significantly as demonstrated by cross-validation experiments on two difficult benchmarks. The most accurate predictions are obtained using the SPMs derived by threading methods in LOMETS server. On the other hand, the computational cost of computing these SPMs was the highest.