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: 14Visualization and Analysis of Micrornas Within Kegg Pathways Using Vanesa(Walter de Gruyter GmbH, 2017) Hamzeiy, Hamid; Suluyayla, Rabia; Brinkrolf, Christoph; Janowski, Sebastian Jan; Hofestaedt, Ralf; Allmer, JensMicroRNAs (miRNAs) are small RNA molecules which are known to take part in post-transcriptional regulation of gene expression. Here, VANESA, an existing platform for reconstructing, visualizing, and analysis of large biological networks, has been further expanded to include all experimentally validated human miRNAs available within miRBase, TarBase and miRTarBase. This is done by integrating a custom hybrid miRNA database to DAWIS-M.D., VANESA's main data source, enabling the visualization and analysis of miRNAs within large biological pathways such as those found within the Kyoto Encyclopedia of Genes and Genomes (KEGG). Interestingly, 99.15 % of human KEGG pathways either contain genes which are targeted by miRNAs or harbor them. This is mainly due to the high number of interaction partners that each miRNA could have (e.g.: hsa-miR-335-5p targets 2544 genes and 71 miRNAs target NUFIP2). We demonstrate the usability of our system by analyzing the measles virus KEGG pathway as a proof-of-principle model and further highlight the importance of integrating miRNAs (both experimentally validated and predicted) into biological networks for the elucidation of novel miRNA-mRNA interactions of biological importance.Article Citation - Scopus: 10Visualization and Analysis of Mirnas Implicated in Amyotrophic Lateral Sclerosis Within Gene Regulatory Pathways(IOS Press, 2018) Hamzeiy, Hamid; Allmer, Jens; Suluyayla, Rabia; Brinkrolf, Christoph; Janowski, Sebastian Jan; Hofestadt, Ralf; Allmer, JensMicroRNAs (miRNAs), approximately 22 nucleotides long, post-transcriptionally active gene expression regulators, play active roles in modulating cellular processes. Gene regulation and miRNA regulation are intertwined and the main aim of this study is to facilitate the analysis of miRNAs within gene regulatory pathways. VANESA enables the reconstruction of biological pathways and supports visualization and simulation. To support integrative miRNA and gene pathway analyses, a custom database of experimentally proven miRNAs, integrating data from miRBase, TarBase and miRTarBase, was added to DAWIS-M.D., which is the main data source for VANESA. Analysis of human KEGG pathways within DAWIS-M.D. showed that 661 miRNAs (~1/3 recorded human miRNAs) lead to 65,474 interactions. hsa-miR-335-5p targets most genes in our system (2,544); while the most targeted gene (with 71 miRNAs) is NUFIP2 (Nuclear Fragile X Mental Retardation Protein Interacting Protein 2). Amyotrophic Lateral Sclerosis (ALS), a complex neurodegenerative disease, was chosen as a proof of concept model. Using our system, it was possible to reduce the initially several hundred genes and miRNAs associated with ALS to eight genes, 19 miRNAs and 31 interactions. This highlights the effectiveness of the implemented system to distill important information from otherwise hard to access, highly convoluted and vast regulatory networks.Article Citation - WoS: 37Citation - Scopus: 46Computational Methods for Microrna Target Prediction(Humana Press, 2014) Hamzeiy, Hamid; Yousef, Malik; Allmer, JensMicroRNAs (miRNAs) are important players in gene regulation. The final and maybe the most important step in their regulatory pathway is the targeting. Targeting is the binding of the miRNA to the mature RNA via the RNA-induced silencing complex. Expression patterns of miRNAs are highly specific in respect to external stimuli, developmental stage, or tissue. This is used to diagnose diseases such as cancer in which the expression levels of miRNAs are known to change considerably. Newly identified miRNAs are increasing in number with every new release of miRBase which is the main online database providing miRNA sequences and annotation. Many of these newly identified miRNAs do not yet have identified targets. This is especially the case in animals where the miRNA does not bind to its target as perfectly as it does in plants. Valid targets need to be identified for miRNAs in order to properly understand their role in cellular pathways. Experimental methods for target validations are difficult, expensive, and time consuming. Having considered all these facts it is of crucial importance to have accurate computational miRNA target predictions. There are many proposed methods and algorithms available for predicting targets for miRNAs, but only a few have been developed to become available as independent tools and software. There are also databases which collect and store information regarding predicted miRNA targets. Current approaches to miRNA target prediction produce a huge amount of false positive and an unknown amount of false negative results, and thus the need for better approaches is evermore evident. This chapter aims to give some detail about the current tools and approaches used for miRNA target prediction, provides some grounds for their comparison, and outlines a possible future.Article Citation - WoS: 25Citation - Scopus: 21Can Mirbase Provide Positive Data for Machine Learning for the Detection of Mirna Hairpins?(Informationsmanagement in der Biotechnologie e.V. (IMBio e.V.), 2013) Demirci, Müşerref Duygu Saçar; Hamzeiy, Hamid; Allmer, JensExperimental detection and validation of miRNAs is a tedious, time-consuming, and expensive process. Computational methods for miRNA gene detection are being developed so that the number of candidates that need experimental validation can be reduced to a manageable amount. Computational methods involve homology-based and ab inito algorithms. Both approaches are dependent on positive and negative training examples. Positive examples are usually derived from miRBase, the main resource for experimentally validated miRNAs. We encountered some problems with miRBase which we would like to report here. Some problems, among others, we encountered are that folds presented in miRBase are not always the fold with the minimum free energy; some entries do not seem to conform to expectations of miRNAs, and some external accession numbers are not valid. In addition, we compared the prediction accuracy for the same negative dataset when the positive data came from miRBase or miRTarBase and found that the latter led to more precise prediction models. We suggest that miRBase should introduce some automated facilities for ensuring data quality to overcome these problems.