PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7645
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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.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.Article Citation - WoS: 10Citation - Scopus: 11Intersection of Microrna and Gene Regulatory Networks and Their Implication in Cancer(Bentham Science Publishers B.V., 2014) Yousef, Malik; Trinh, Hung V.; Allmer, JensMicroRNAs (miRNAs) have attracted heightened attention for their role as post-transcriptional regulators of gene expression. It has become clear that miRNAs can both up- and downregulate protein expression. According to current estimates, most human genes are harboring miRNAs and/or are regulated by them. Thus miRNAs form a complex network of expression regulation which tightly interacts with known gene regulatory networks. Similar to some transcription factors, some miRNAs can have hundreds of target transcripts whose expression they modulate. Thus miRNAs can form complex regulatory networks by themselves, but because their expression is often tightly coordinated with gene expression, they form an intertwined regulatory network with many possible interactions among gene and miRNA regulatory pathways. In this review we first consider gene regulatory networks. Then we discuss microRNAs and their implication in cancer and how they may form regulatory networks. Finally, we give our perspective and provide an outlook including the aspect of personalized medicine.Article Citation - WoS: 20Citation - Scopus: 24Newly Developed Ssr Markers Reveal Genetic Diversity and Geographical Clustering in Spinach (spinacia Oleracea)(Springer Verlag, 2017) Göl, Şurhan; Göktay, Mehmet; Allmer, Jens; Doğanlar, Sami; Frary, AnneSpinach is a popular leafy green vegetable due to its nutritional composition. It contains high concentrations of vitamins A, E, C, and K, and folic acid. Development of genetic markers for spinach is important for diversity and breeding studies. In this work, Next Generation Sequencing (NGS) technology was used to develop genomic simple sequence repeat (SSR) markers. After cleaning and contig assembly, the sequence encompassed 2.5% of the 980 Mb spinach genome. The contigs were mined for SSRs. A total of 3852 SSRs were detected. Of these, 100 primer pairs were tested and 85% were found to yield clear, reproducible amplicons. These 85 markers were then applied to 48 spinach accessions from worldwide origins, resulting in 389 alleles with 89% polymorphism. The average gene diversity (GD) value of the markers (based on a GD calculation that ranges from 0 to 0.5) was 0.25. Our results demonstrated that the newly developed SSR markers are suitable for assessing genetic diversity and population structure of spinach germplasm. The markers also revealed clustering of the accessions based on geographical origin with clear separation of Far Eastern accessions which had the overall highest genetic diversity when compared with accessions from Persia, Turkey, Europe, and the USA. Thus, the SSR markers have good potential to provide valuable information for spinach breeding and germplasm management. Also they will be helpful for genome mapping and core collection establishment.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.Article Citation - WoS: 4Citation - Scopus: 12Existing Bioinformatics Tools for the Quantitation of Post-Translational Modifications(Springer Verlag, 2012) Allmer, JensMass spectrometry (MS)-based proteomics, by itself, is a vast and complex area encompassing various mass spectrometers, different spectra, and search result representations. When the aim is quantitation performed in different scanning modes at different MS levels, matters become additionally complex. Quantitation of post-translational modifications (PTM) represents the greatest challenge among these endeavors. Many different approaches to quantitation have been described and some of these can be directly applied to the quantitation of PTMs. The amount of data produced via MS, however, makes manual data interpretation impractical. Therefore, specialized software tools meet this challenge. Any software currently able to quantitate differentially labeled samples may theoretically be adapted to quantitate differential PTM expression among samples as well. Due to the heterogeneity of mass spectrometry-based proteomics; this review will focus on quantitation of PTM using liquid chromatography followed by one or more stages of mass spectrometry. Currently available free software, which either allow analysis of PTM or are easily adaptable for this purpose, is briefly reviewed in this paper. Selected studies, especially those related to phosphoproteomics, shall be used to highlight the current ability to quantitate PTMs. © Springer-Verlag 2010Article Citation - WoS: 1Citation - Scopus: 2Label-Free Quantitation, an Extension To 2db(Springer Verlag, 2010) Allmer, JensDetermining the differential expression of proteins under different conditions is of major importance in proteomics. Since mass spectrometry-based proteomics is often used to quantify proteins, several labelling strategies have been developed. While these are generally more precise than label-free quantitation approaches, they imply specifically designed experiments which also require knowledge about peptides that are expected to be measured and need to be modified. We recently designed the 2DB database which aids storage, analysis, and publication of data from mass spectrometric experiments to identify proteins. This database can aid identifying peptides which can be used for quantitation. Here an extension to the database application, named MSMAG, is presented which allows for more detailed analysis of the distribution of peptides and their associated proteins over the fractions of an experiment. Furthermore, given several biological samples in the database, label-free quantitation can be performed. Thus, interesting proteins, which may warrant further investigation, can be identified en passant while performing high-throughput proteomics studies. © 2009 Springer-Verlag.