Acar, İlhan Erkin
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01. Izmir Institute of Technology
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Scholarly Output
4
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2
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13438/1335
Supervised MSc Theses
1
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0
WoS Citation Count
34
Scopus Citation Count
44
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1
WoS Citations per Publication
8.50
Scopus Citations per Publication
11.00
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4
Supervised Theses
1
| Journal | Count |
|---|---|
| 10th International Joint Conference on Biomedical Engineering Systems and Technologies | 1 |
| BMC Bioinformatics | 1 |
| Frontiers in Microbiology | 1 |
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4 results
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Now showing 1 - 4 of 4
Master Thesis Mining the Toxoplasma Gondii Genome for Microrna Regulatory Patterns(Izmir Institute of Technology, 2017) Acar, İlhan Erkin; Allmer, JensToxoplasma gondii is a parasite that causes mental retardation, blindness or nearblindness, and decreased psycho-motor performance if the patient is congenitally infected. There have been efforts to vaccinate humans against this parasite, yet it was not achieved. Therefore, a better understanding of Toxoplasma gondii can be provided by examining its microRNA regulation. MicroRNAs are known to regulate messenger RNAs and prevent translation. This results in different effects in different biological pathways. In this study, the Toxoplasma gondii genome was used to predict precursor and mature microRNAs, while experimentally validated microRNAs were taken into consideration. This was further explored in terms of microRNA targeting, with the known genes of Toxoplasma gondii. Furthermore, RNA Sequencing data of this organism was obtained and analysed in terms of gene expression and possible microRNA expression outcomes. Combining gene expression analyses with targeting predictions, it was possible to create a microRNA - gene interaction network. Gene expression analyses showed that there was no differentially expressed genes, microRNAs or interactions between two developmental stages of Toxoplasma gondii, tachyzoite and bradyzoite. This result was added to interactions to determine up and down regulations. Then, all of these interactions were connected where they intersect, to create a regulation network of microRNAs. This network was further explored and compared to random networks of the same size. It was seen that the biological network contains many larger sized cliques. This knowledge can be further analysed in future work, to create drug leads that will target vital pathways of Toxoplasma gondii.Article Citation - WoS: 20Citation - Scopus: 25Microrna Categorization Using Sequence Motifs and K-Mers(BioMed Central Ltd., 2017) Yousef, Malik; Khalifa, Waleed; Acar, İlhan Erkin; Allmer, JensBackground: Post-transcriptional gene dysregulation can be a hallmark of diseases like cancer and microRNAs (miRNAs) play a key role in the modulation of translation efficiency. Known pre-miRNAs are listed in miRBase, and they have been discovered in a variety of organisms ranging from viruses and microbes to eukaryotic organisms. The computational detection of pre-miRNAs is of great interest, and such approaches usually employ machine learning to discriminate between miRNAs and other sequences. Many features have been proposed describing pre-miRNAs, and we have previously introduced the use of sequence motifs and k-mers as useful ones. There have been reports of xeno-miRNAs detected via next generation sequencing. However, they may be contaminations and to aid that important decision-making process, we aimed to establish a means to differentiate pre-miRNAs from different species. Results: To achieve distinction into species, we used one species' pre-miRNAs as the positive and another species' pre-miRNAs as the negative training and test data for the establishment of machine learned models based on sequence motifs and k-mers as features. This approach resulted in higher accuracy values between distantly related species while species with closer relation produced lower accuracy values. Conclusions: We were able to differentiate among species with increasing success when the evolutionary distance increases. This conclusion is supported by previous reports of fast evolutionary changes in miRNAs since even in relatively closely related species a fairly good discrimination was possible.Conference Object Citation - WoS: 3Citation - Scopus: 8Distinguishing Between Microrna Targets From Diverse Species Using Sequence Motifs and K-Mers(SCITEPRESS, 2017) Yousef, Malik; Khalifa, Waleed; Acar, İlhan Erkin; Allmer, JensA disease phenotype is often due to dysregulation of gene expression. Post-translational regulation of protein abundance by microRNAs (miRNAs) is, therefore, of high importance in, for example, cancer studies. MicroRNAs provide a complementary sequence to their target messenger RNA (mRNA) as part of a complex molecular machinery. Known miRNAs and targets are listed in miRTarBase for a variety of organisms. The experimental detection of such pairs is convoluted and, therefore, their computational detection is desired which is complicated by missing negative data. For machine learning, many features for parameterization of the miRNA targets are available and k-mers and sequence motifs have previously been used. Unrelated organisms like intracellular pathogens and their hosts may communicate via miRNAs and, therefore, we investigated whether miRNA targets from one species can be differentiated from miRNA targets of another. To achieve this end, we employed target information of one species as positive and the other as negative training and testing data. Models of species with higher evolutionary distance generally achieved better results of up to 97% average accuracy (mouse versus Caenorhabditis elegans) while more closely related species did not lead to successful models (human versus mouse; 60%). In the future, when more targeting data becomes available, models can be established which will be able to more precisely determine miRNA targets in hostpathogen systems using this approach.Article Citation - WoS: 11Citation - Scopus: 11The Expressed Microrna-Mrna Interactions of Toxoplasma Gondii(Frontiers Media S.A., 2018) Acar, İlhan Erkin; Saçar Demirci, Müşerref Duygu; Groß, Uwe; Allmer, JensMicroRNAs (miRNAs) are involved in post-transcriptional modulation of gene expression and thereby have a large influence on the resulting phenotype. We have previously shown that miRNAs may be involved in the communication between Toxoplasma gondii and its hosts and further confirmed a number of proposed specific miRNAs. Yet, little is known about the internal regulation via miRNAs in T. gondii. Therefore, we predicted pre-miRNAs directly from the type II ME49 genome and filtered them. For the confident hairpins, we predicted the location of the mature miRNAs and established their target genes. To add further confidence, we evaluated whether the hairpins and their targets were co-expressed. Such co-expressed miRNA and target pairs define a functional interaction. We extracted all such functional interactions and analyzed their differential expression among strains of all three clonal lineages (RH, PLK, and CTG) and between the two stages present in the intermediate host (tachyzoites and bradyzoites). Overall, we found ~65,000 expressed interactions of which ~5,500 are differentially expressed among strains but none are significantly differentially expressed between developmental stages. Since miRNAs and target decoys can be used as therapeutics we believe that the list of interactions we provide will lead to novel approaches in the treatment of toxoplasmosis.