WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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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.Conference Object Citation - WoS: 6Citation - Scopus: 8Comparison of Four Ab Initio Microrna Prediction Tools(SciTePress, 2013) Saçar, Müşerref Duygu; Allmer, JensMicroRNAs are small RNA sequences of 18-24 nucleotides in length, which serve as templates to drive post transcriptional gene silencing. The canonical microRNA pathway starts with transcription from DNA and is followed by processing by the Microprocessor complex, yielding a hairpin structure. This is then exported into the cytosol where it is processed by Dicer and next incorporated into the RNA induced silencing complex. All of these biogenesis steps add to the overall specificity of miRNA production and effect. Unfortunately, experimental detection of miRNAs is cumbersome and therefore computational tools are necessary. Homology-based miRNA prediction tools are limited by fast miRNA evolution and by the fact that they are template driven. Ab initio miRNA prediction methods have been proposed but they have not been analyzed competitively so that their relative performance is largely unknown. Here we implement the features proposed in four miRNA ab initio studies and evaluate them on two data sets. Using the features described in Bentwich 2008 leads to the highest accuracy but still does not provide enough confidence into the results to warrant experimental validation of all predictions in a larger genome like the human genome. Copyright © 2013 SCITEPRESS - Science and Technology Publications.