Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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Book Part Citation - Scopus: 444 Current Challenges in Mirnomics(Humana Press, 2022) Akgül, Bünyamin; Stadler, Peter F.; Hawkins, Liam J.; Hadj-Moussa, Hanane; Storey, Kenneth B.; Ergin, Kemal; Allmer, JensMature microRNAs (miRNAs) are short RNA sequences about 18–24 nucleotide long, which provide the recognition key within RISC for the posttranscriptional regulation of target RNAs. Considering the canonical pathway, mature miRNAs are produced via a multistep process. Their transcription (pri-miRNAs) and first processing step via the microprocessor complex (pre-miRNAs) occur in the nucleus. Then they are exported into the cytosol, processed again by Dicer (dsRNA) and finally a single strand (mature miRNA) is incorporated into RISC (miRISC). The sequence of the incorporated miRNA provides the function of RNA target recognition via hybridization. Following binding of the target, the mRNA is either degraded or translation is inhibited, which ultimately leads to less protein production. Conversely, it has been shown that binding within the 5? UTR of the mRNA can lead to an increase in protein product. Regulation of homeostasis is very important for a cell; therefore, all steps in the miRNA-based regulation pathway, from transcription to the incorporation of the mature miRNA into RISC, are under tight control. While much research effort has been exerted in this area, the knowledgebase is not sufficient for accurately modelling miRNA regulation computationally. The computational prediction of miRNAs is, however, necessary because it is not feasible to investigate all possible pairs of a miRNA and its target, let alone miRNAs and their targets. We here point out open challenges important for computational modelling or for our general understanding of miRNA-based regulation and show how their investigation is beneficial. It is our hope that this collection of challenges will lead to their resolution in the near future. © 2022, Springer Science+Business Media, LLC, part of Springer Nature.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: 30Machine Learning Methods for Microrna Gene Prediction(Humana Press, 2014) Saçar, Müşerref Duygu; Allmer, JensMicroRNAs (miRNAs) are single-stranded, small, noncoding RNAs of about 22 nucleotides in length, which control gene expression at the posttranscriptional level through translational inhibition, degradation, adenylation, or destabilization of their target mRNAs. Although hundreds of miRNAs have been identified in various species, many more may still remain unknown. Therefore, discovery of new miRNA genes is an important step for understanding miRNA-mediated posttranscriptional regulation mechanisms. It seems that biological approaches to identify miRNA genes might be limited in their ability to detect rare miRNAs and are further limited to the tissues examined and the developmental stage of the organism under examination. These limitations have led to the development of sophisticated computational approaches attempting to identify possible miRNAs in silico. In this chapter, we discuss computational problems in miRNA prediction studies and review some of the many machine learning methods that have been tried to address the issues.