Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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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>Article Citation - WoS: 1Citation - Scopus: 1Dnmso; an Ontology for Representing De Novo Sequencing Results From Tandem-Ms Data(PeerJ Inc., 2020) Takan, Savaş; Allmer, JensFor the identification and sequencing of proteins, mass spectrometry (MS) has become the tool of choice and, as such, drives proteomics. MS/MS spectra need to be assigned a peptide sequence for which two strategies exist. Either database search or de novo sequencing can be employed to establish peptide spectrum matches. For database search, mzIdentML is the current community standard for data representation. There is no community standard for representing de novo sequencing results, but we previously proposed the de novo markup language (DNML). At the moment, each de novo sequencing solution uses different data representation, complicating downstream data integration, which is crucial since ensemble predictions may be more useful than predictions of a single tool. We here propose the de novo MS Ontology (DNMSO), which can, for example, provide many-to-many mappings between spectra and peptide predictions. Additionally, an application programming interface (API) that supports any file operation necessary for de novo sequencing from spectra input to reading, writing, creating, of the DNMSO format, as well as conversion from many other file formats, has been implemented. This API removes all overhead from the production of de novo sequencing tools and allows developers to concentrate on algorithm development completely. We make the API and formal descriptions of the format freely available at https://github.com/savastakan/dnmso.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.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: 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.Book Part Citation - Scopus: 9Differential Expression of Toxoplasma Gondii Micrornas in Murine and Human Hosts(Springer, 2016) Allmer, Jens; Saçar Demirci, Müşerref Duygu; Bağcı, CanerMicroRNAs are short RNA sequences involved in post-transcriptional gene regulation. MicroRNAs are known for a wide variety of species ranging from bacteria to plants. It has become clear that some cross-kingdom regulation is possible especially between viruses and their hosts. We hypothesized that intracellular parasites, like Toxoplasma gondii, similar to viruses would be able to modulate their host’s gene expression. We were able to show that T. gondii produces many putative pre-miRNAs which are actually transcribed. Furthermore, some of these expressed pre-miRNAs have a striking resemblance to host mature miRNAs. Previous studies indicated that T. gondii infection coincides with increased abundance of some miRNAs. Here we were able to show that many of these miRNAs have close relatives in T. gondii which may not be distinguishable using PCR. Taken together, the similarity to host miRNAs, their confirmed expression, and their upregulation during infection, it suggests that T. gondii actively transfers miRNAs to regulate its host. We conclude, that this type of cross-kingdom regulation may be possible, but that targeted analysis is necessary to consolidate our computational findings. © Springer International Publishing Switzerland 2016. All rights are reserved.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: 14Citation - Scopus: 16Transcriptomic Analysis of Boron Hyperaccumulation Mechanisms in Puccinellia Distans(Elsevier Ltd., 2018) Öztürk, Saniye Elvan; Göktay, Mehmet; Has, Canan; Babaoğlu, Mehmet; Allmer, Jens; Doğanlar, Sami; Frary, AnnePuccinellia distans, common alkali grass, is found throughout the world and can survive in soils with boron concentrations that are lethal for other plant species. Indeed, P. distans accumulates very high levels of this element. Despite these interesting features, very little research has been performed to elucidate the boron tolerance mechanism in this species. In this study, P. distans samples were treated for three weeks with normal (0.5 mg L−1) and elevated (500 mg L−1) boron levels in hydroponic solution. Expressed sequence tags (ESTs) derived from shoot tissue were analyzed by RNA sequencing to identify genes up and down-regulated under boron stress. In this way, 3312 differentially expressed transcripts were detected, 67.7% of which were up-regulated and 32.3% of which were down-regulated in boron-treated plants. To partially confirm the RNA sequencing results, 32 randomly selected transcripts were analyzed for their expression levels in boron-treated plants. The results agreed with the expected direction of change (up or down-regulation). A total of 1652 transcripts had homologs in A. thaliana and/or O. sativa and mapped to 1107 different proteins. Functional annotation of these proteins indicated that the boron tolerance and hyperaccumulation mechanisms of P. distans involve many transcriptomic changes including: alterations in the malate pathway, changes in cell wall components that may allow sequestration of excess boron without toxic effects, and increased expression of at least one putative boron transporter and two putative aquaporins. Elucidation of the boron accumulation mechanism is important in developing approaches for bioremediation of boron contaminated soils.