Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik
Permanent URI for this collectionhttps://hdl.handle.net/11147/9
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Article Citation - WoS: 7Citation - Scopus: 5A Machine Learning Approach for Microrna Precursor Prediction in Retro-Transcribing Virus Genomes(Informationsmanagement in der Biotechnologie e.V. (IMBio e.V.), 2016) Saçar Demirci, Müşerref Duygu; Toprak, Mustafa; Allmer, JensIdentification of microRNA (miRNA) precursors has seen increased efforts in recent years. The difficulty in experimental detection of pre-miRNAs increased the usage of computational approaches. Most of these approaches rely on machine learning especially classification. In order to achieve successful classification, many parameters need to be considered such as data quality, choice of classifier settings, and feature selection. For the latter one, we developed a distributed genetic algorithm on HTCondor to perform feature selection. Moreover, we employed two widely used classification algorithms libSVM and random forest with different settings to analyze the influence on the overall classification performance. In this study we analyzed 5 human retro virus genomes; Human endogenous retrovirus K113, Hepatitis B virus (strain ayw), Human T lymphotropic virus 1, Human T lymphotropic virus 2, Human immunodeficiency virus 2, and Human immunodeficiency virus 1. We then predicted pre-miRNAs by using the information from known virus and human pre-miRNAs. Our results indicate that these viruses produce novel unknown miRNA precursors which warrant further experimental validation.Article Citation - WoS: 4Citation - Scopus: 4Improving the Quality of Positive Datasets for the Establishment of Machine Learning Models for Pre-Microrna Detection(Informationsmanagement in der Biotechnologie e.V. (IMBio e.V.), 2017) Saçar Demirci, Müşerref Duygu; Allmer, JensMicroRNAs (miRNAs) are involved in the post-transcriptional regulation of protein abundance and thus have a great impact on the resulting phenotype. It is, therefore, no wonder that they have been implicated in many diseases ranging from virus infections to cancer. This impact on the phenotype leads to a great interest in establishing the miRNAs of an organism. Experimental methods are complicated which led to the development of computational methods for pre-miRNA detection. Such methods generally employ machine learning to establish models for the discrimination between miRNAs and other sequences. Positive training data for model establishment, for the most part, stems from miRBase, the miRNA registry. The quality of the entries in miRBase has been questioned, though. This unknown quality led to the development of filtering strategies in attempts to produce high quality positive datasets which can lead to a scarcity of positive data. To analyze the quality of filtered data we developed a machine learning model and found it is well able to establish data quality based on intrinsic measures. Additionally, we analyzed which features describing pre-miRNAs could discriminate between low and high quality data. Both models are applicable to data from miRBase and can be used for establishing high quality positive data. This will facilitate the development of better miRNA detection tools which will make the prediction of miRNAs in disease states more accurate. Finally, we applied both models to all miRBase data and provide the list of high quality hairpins.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.Article Citation - WoS: 37Citation - Scopus: 45On the Performance of Pre-Microrna Detection Algorithms(Nature Publishing Group, 2017) Saçar Demirci, Müşerref Duygu; Baumbach, Jan; Allmer, JensMicroRNAs are crucial for post-transcriptional gene regulation, and their dysregulation has been associated with diseases like cancer and, therefore, their analysis has become popular. The experimental discovery of miRNAs is cumbersome and, thus, many computational tools have been proposed. Here we assess 13 ab initio pre-miRNA detection approaches using all relevant, published, and novel data sets while judging algorithm performance based on ten intrinsic performance measures. We present an extensible framework, izMiR, which allows for the unbiased comparison of existing algorithms, adding new ones, and combining multiple approaches into ensemble methods. In an exhaustive attempt, we condense the results of millions of computations and show that no method is clearly superior; however, we provide a guideline for biomedical researchers to select a tool. Finally, we demonstrate that combining all of the methods into one ensemble approach, for the first time, allows reliable purely computational pre-miRNA detection in large eukaryotic genomes.