Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik
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Article Citation - WoS: 16Citation - Scopus: 18Computational and Bioinformatics Methods for Microrna Gene Prediction(Humana Press, 2014) Allmer, JensMicroRNAs (miRNAs) have attracted ever-increasing interest in recent years. Since experimental approaches for determining miRNAs are nontrivial in their application, computational methods for the prediction of miRNAs have gained popularity. Such methods can be grouped into two broad categories (1) performing ab initio predictions of miRNAs from primary sequence alone and (2) additionally employing phylogenetic conservation. Most methods acknowledge the importance of hairpin or stem-loop structures and employ various methods for the prediction of RNA secondary structure. Machine learning has been employed in both categories with classification being the predominant method. In most cases, positive and negative examples are necessary for performing classification. Since it is currently elusive to experimentally determine all possible miRNAs for an organism, true negative examples are hard to come by, and therefore the accuracy assessment of algorithms is hampered. In this chapter, first RNA secondary structure prediction is introduced since it provides a basis for miRNA prediction. This is followed by an assessment of homology and then ab initio miRNA prediction methods.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: 2Citation - Scopus: 5Pgminer: Complete Proteogenomics Workflow; From Data Acquisition To Result Visualization(Elsevier Ltd., 2017) Has, Canan; Allmer, JensIn parallel with the development of nucleotide sequencing an equally important interest in further describing the sequence in terms of function arose and the latter represents the current bottleneck in the overall research question. Sequencing the transcriptome allows determination of expressed nucleotide sequences and using mass spectrometry allows sequencing on the protein level. Both approaches can only sequence a subset of the existing transcripts. Moreover, for example post translational modification events can only be determined on the proteomics level. Therefore, it is essential to combine proteomics and genomics. For that purpose, proteogenomics data analysis pipelines have been described. Here, we describe a novel proteogenomics workflow which encompasses everything from the acquisition of data to result visualization in the Konstanz Information Miner (KNIME), a state of the art workflow management and data analytics platform. We amended KNIME with a number of processes like peptide consensus prediction, peptide mapping, and database equalizing, as well as result visualization. This enabled construction of our new workflow, entitled PGMiner, which not only includes all data analysis steps, but is highly customizable which is rather cumbersome for most existing pipelines. Furthermore, no burdensome installation processes have to be performed making PGMiner the most user friendly tool available.Conference Object Citation - Scopus: 13Feature Selection for Microrna Target Prediction Comparison of One-Class Feature Selection Methodologies(Hindawi Publishing Corporation, 2016) Yousef, Malik; Allmer, Jens; Khalifa, WaleedTraditionally, machine learning algorithms build classification models from positive and negative examples. Recently, one-class classification (OCC) receives increasing attention in machine learning for problems where the negative class cannot be defined unambiguously. This is specifically problematic in bioinformatics since for some important biological problems the target class (positive class) is easy to obtain while the negative one cannot be measured. Artificially generating the negative class data can be based on unreliable assumptions. Several studies have applied two-class machine learning to predict microRNAs (miRNAs) and their target. Different approaches for the generation of an artificial negative class have been applied, but may lead to a biased performance estimate. Feature selection has been well studied for the two-class classification problem, while fewer methods are available for feature selection in respect to OCC. In this study, we present a feature selection approach for applying one-class classification to the prediction of miRNA targets. A comparison between one-class and two-class approaches is presented to highlight that their performance are similar while one-class classification is not based on questionable artificial data for training and performance evaluation. We further show that the feature selection method we tried works to a degree, but needs improvement in the future. Perhaps it could be combined with other approaches.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.Conference Object Citation - WoS: 4Citation - Scopus: 4Mining Frequent Patterns From Microarray Data(Institute of Electrical and Electronics Engineers Inc., 2011) Yıldız, Barış; Şelale, HaticeThe rapid development of computers and increasing amount of collected data made data mining a popular analysis tool. Data mining research is interrelated to many fields and one of the most important ones is bioinformatics. Among many techniques, mining association rules or frequent patterns is one of the most studied techniques in computer science and it is applicable to bioinformatics. Association analysis of gene expressions may be used as decision support mechanism for finding genes that are in same pathway. In this work, publicly available yeast microarray data has been analyzed using an efficient frequent pattern mining algorithm Matrix Apriori and frequently co-over-expressed genes have been identified. © 2011 IEEE.Conference Object Citation - Scopus: 17Systematic Computational Analysis of Potential Rnai Regulation in Toxoplasma Gondii(Institute of Electrical and Electronics Engineers Inc., 2010) Çakır, Mehmet Volkan; Allmer, JensRNA interference (RNAi) is the mechanism through which RNA interferes with the production of other RNAs in a sequence specific manner. Micro RNA (miRNA) is a type of RNA which is transcribed as pri-miRNAs and processed to premiRNAs in the nucleus. These pre-miRNAs are then exported from the nucleus and processed in the cytoplasm to double stranded RNA with one strand providing target specificity.. Toxoplasma gondii is a parasitic apicomplexan which causes several diseases. T. gondii is a good candidate for computational efforts with its small and publicly available genome files and extensive information about its gene structure. Although the existence of RNA interference in T. gondii is being debated, establishment of its complete potential RNAi regulatory network may be beneficial for further investigations into the topic. ©2009 IEEE.Conference Object Relative Protein Quantitation With Post Translational Modifications in Mass Spectrometry Based Proteomics(Institute of Electrical and Electronics Engineers Inc., 2010) Allmer, JensMass spectrometry has become the tool of choice for most investigations in proteomics. Identification of proteins from complex mixtures has long been achieved and is now routinely used in countless high throughput studies. Quantitation by mass spectrometry is comparably newer and many different strategies have been proposed. One such strategy quantitates the difference in protein expression level among samples via extracted ion chromatograms, or spectral counts or a combination thereof. Another strategy involves mass modifications of the analytes in one or more of the samples under investigation. MSMAG has been developed as an extension to 2DB and it has been shown that it can aid in quantitation of data from experiments employing label-free quantitation. Recently, it has been extended to allow for analysis of data based on labelling strategies. This also makes it possible to quickly visualize and investigate inherent mass differences as presented by post translational modifications. ©2009 IEEE.