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: 2Citation - Scopus: 2Antiviral Microrna Expression Signatures Are Altered in Subacute Sclerosing Panencephalitis(Wolters Kluwer Medknow Publications, 2021) Tüfekçi, Kemal Uğur; Allmer, Jens; Çarman, Kürşat Bora; Bayram, Erhan; Topçu, Yasemin; Hız, Semra; Genç, Şermin; Yiş, UluçBackground: Subacute sclerosing panencephalitis (SSPE) is a chronic, progressive disease caused by a persistent infection of the measles virus. Despite extensive efforts, the exact neurodegeneration mechanism in SSPE remains unknown. MicroRNAs (miRNAs) have emerged as an essential part of cellular antiviral defense mechanisms and can be modulated by antiviral cytokines Such as interferon-beta (IFN-beta). Aims and Objectives: In this study, we aimed to elucidate the role of antiviral miRNAs in the pathogenesis of SSPE and analyze the interaction between host antiviral miRNAs and virus genes. Materials and Methods: Thirty-seven patients who were followed with SSPE and age-matched healthy children were included in the study. Peripheral blood mononuclear cell levels of miR-196b, miR-296, miR-431, and miR-448 were analyzed using quantitative polymerase chain reaction. Target predictions and pathway constructions of deregulated miRNAs were assessed. Results: Here, we showed that IFN-beta-modulated miR-196b, miR-296, and miR-431 were significantly upregulated in patients with SSPE compared with healthy controls. Besides, sequence complementarity analysis showed that miR-296 and miR-196b predicted binding regions in measles virus genomic RNA. Conclusion: Our findings suggest that antiviral miRNAs are upregulated in patients with SSPE, which could be a part of the host antiviral defense mechanism. </p>Letter Citation - Scopus: 9A Call for Benchmark Data in Mass Spectrometry-Based Proteomics(Proteomass Scientific Society, 2012) Allmer, JensProteomics is a quickly developing field. New and better mass spectrometers, the platform of choice in proteomics, are being introduced frequently. New algorithms for the analysis of mass spectrometric data and assignment of amino acid sequence to tandem mass spectra are also presented on a frequent basis. Unfortunately, the best application area for these algorithms cannot be established at the moment. Furthermore, even the accuracy of the algorithms and their relative performance cannot be established. This is due to the lack of proper benchmark data. This letter first introduces the field of mass spectrometry-based proteomics and then defines the expectations of a well-designed benchmark dataset. Thereafter, the current situation is compared to this ideal. A call for the creation of a proper benchmark dataset is then placed and it is explained how measurement should be performed. Finally, the benefits for the research community are highlighted. © 2012, Proteomass Scientific Society. All rights reserved.Editorial Citation - WoS: 2Computational Mirnomics - Integrative Approaches(Informationsmanagement in der Biotechnologie e.V. (IMBio e.V.), 2017) Hofestaedt, Ralf; Schreiber, Falk; Sommer, Bjoern; Allmer, JensWith this special issue on Computational miRNomics, we would like to start a new generation of publications in the Journal of Integrative Bioinformatics (JIB). From 2017 onwards, JIB will be published by De Gruyter which is one of the largest Open Access publishers in Germany with a long history. Established in 1918 with roots reaching even further back, the JIB editorial board decided that De Gruyter is the perfect partner to increase the level of professionalism for our publication processing and journal development.Conference Object Preparing Sequence Databases for Application in Proteogenomics(Springer, 2016) Has, Canan; Mungan, Mehmet Direnç; Çiftçi, Cansu; Allmer, JensProteomics involves the identification of proteins from complex mixtures which is performed using mass spectrometry (MS) followed by computational data analysis. MS/MS spectra can either be sequenced de novo if no sequence is available for the proteins in the mixture, or by using database search algorithms such as OMSSA, X!Tandem, and MSGF+.Conference Object Database Normalization Is Crucial for Reliable Protein Identification in Mass Spectrometry-Based Proteomics(Springer, 2016) Has, Canan; Mungan, Mehmet Direnç; Çiftçi, Cansu; Allmer, JensResearch in proteomics is driven by mass spectrometry, especially the identification of proteins from complex samples. Computational analysis of the resulting data determines the peptide sequences of the recorded spectra and integrates identifications into proteins. For this, database search algorithms can be employed, but they need a list of amino acid sequences that are expected to exist in the sample. Many algorithms have been proposed and consensus scoring has been performed. While the comparison/integration among results from different algorithms is important, there has been no attempt to integrate the results from searching multiple databases. This is, however, important since it poses technical problems when all databases, needed for a study, are simply concatenated. Unfortunately, it has been shown that databases of different size influence scoring and prohibit the direct comparison of results.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: 4Citation - Scopus: 9Pgminer Reloaded, Fully Automated Proteogenomic Annotation Tool Linking Genomes To Proteomes(Informationsmanagement in der Biotechnologie e.V. (IMBio e.V.), 2016) Has, Canan; Lashin, Sergey A.; Kochetov, Alexey; Allmer, JensImprovements in genome sequencing technology increased the availability of full genomes and transcriptomes of many organisms. However, the major benefit of massive parallel sequencing is to better understand the organization and function of genes which then lead to understanding of phenotypes. In order to interpret genomic data with automated gene annotation studies, several tools are currently available. Even though the accuracy of computational gene annotation is increasing, a combination of multiple lines of experimental evidences should be gathered. Mass spectrometry allows the identification and sequencing of proteins as major gene products; and it is only these proteins that conclusively show whether a part of a genome is a coding region or not to result in phenotypes. Therefore, in the field of proteogenomics, the validation of computational methods is done by exploiting mass spectrometric data. As a result, identification of novel protein coding regions, validation of current gene models, and determination of upstream and downstream regions of genes can be achieved. In this paper, we present new functionality for our proteogenomic tool, PGMiner which performs all proteogenomic steps like acquisition of mass spectrometric data, peptide identification against preprocessed sequence databases, assignment of statistical confidence to identified peptides, mapping confident peptides to gene models, and result visualization. The extensions cover determining proteotypic peptides and thus unambiguous protein identification. Furthermore, peptides conflicting with gene models can now automatically assessed within the context of predicted alternative open reading frames.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: 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 - 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.