Computational and Bioinformatics Methods for MicroRNA Gene Prediction

Allmer, Jens

doi:10.1007/978-1-62703-748-8_9

Cited by 16 publications

(16 citation statements)

References 65 publications

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“…The current aim in the field is to detect pre-miRNAs in, for example, genomes. A previous classification of pre-miRNAs into groups has also been performed and detected conserved miRNA families [9]. On the other hand, it has been shown that miRNAs can evolve rapidly [10–12].…”

Section: Discussionmentioning

confidence: 99%

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MicroRNA categorization using sequence motifs and k-mers

et al. 2017

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BackgroundPost-transcriptional gene dysregulation can be a hallmark of diseases like cancer and microRNAs (miRNAs) play a key role in the modulation of translation efficiency. Known pre-miRNAs are listed in miRBase, and they have been discovered in a variety of organisms ranging from viruses and microbes to eukaryotic organisms. The computational detection of pre-miRNAs is of great interest, and such approaches usually employ machine learning to discriminate between miRNAs and other sequences. Many features have been proposed describing pre-miRNAs, and we have previously introduced the use of sequence motifs and k-mers as useful ones. There have been reports of xeno-miRNAs detected via next generation sequencing. However, they may be contaminations and to aid that important decision-making process, we aimed to establish a means to differentiate pre-miRNAs from different species.ResultsTo achieve distinction into species, we used one species’ pre-miRNAs as the positive and another species’ pre-miRNAs as the negative training and test data for the establishment of machine learned models based on sequence motifs and k-mers as features. This approach resulted in higher accuracy values between distantly related species while species with closer relation produced lower accuracy values.ConclusionsWe were able to differentiate among species with increasing success when the evolutionary distance increases. This conclusion is supported by previous reports of fast evolutionary changes in miRNAs since even in relatively closely related species a fairly good discrimination was possible.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1584-1) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

“…Therefore, and since it seems futile to try and discover all miRNAs of an organism experimentally, computational prediction of miRNAs has become important. Most such approaches employ machine learning using two-class classification [9, 10]. …”

Section: Introductionmentioning

confidence: 99%

MicroRNA categorization using sequence motifs and k-mers

et al. 2017

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“…This feat is impossible to achieve for all miRNA-mRNA pairs since some may only be expressed under certain conditions (Saçar and Allmer, 2013). For this reason, computational detection of pre-miRNAs has become important and most approaches employ machine learning (Allmer, 2014;. Machine learning models have been established for many species among them for metazoan (Allmer and Yousef, 2012) and plants (Yousef, Allmer and Khalifa, 2016a) and they depend on the parameterization of the folded pre-miRNA's three dimensional structure (Sacar and Allmer, 2013).…”

Section: Introductionmentioning

confidence: 99%

Species Categorization via MicroRNAs - Based on 3’UTR Target Sites using Sequence Features

Yousef

Levy

Allmer

2018

Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies

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Proteins define phenotypes and their dysregulation leads to diseases. Post-translational regulation of protein abundance can be achieved by microRNAs (miRNAs). Therefore studying this method of gene regulation is of high importance. MicroRNAs interact with their target messenger RNA via hybridization within a specialized molecular framework. Many miRNAs and their targets have been identified and they are listed in various databases like miRTarBase. The experimental identification of functional miRNA-mRNA pairs is difficult and, therefore, they are detected computationally which is complicated due to missing negative data. Machine learning has been used for miRNA and target detection and many features have been described for miRNAs and miRNA:mRNA target duplexes generally on a per species basis. However, many claims of cross-kingdom regulation via miRNAs have been made and, therefore, we were interested whether it is possible to differentiate among species based on the target sequence in the mRNA alone. Thus, we investigated whether miRNA targets sites within the 3'UTR can be differentiated between species based on k-mer features only. Target information of one species was used as positive examples and the others as negative ones to establish machine learning models. It was observed that few features were sufficient for successful categorization of mircoRNA targets to species. For example mouse versus Caenorhabditis elegans reached up to 97% average accuracy over 100 fold cross validation. The simplicity of the approach, based on just k-mers, is promising for automatic categorization systems. In the future, this approach will help scrutinize alleged cross-kingdom regulation via miRNAs in respect to miRNA from one species targeting mRNAs in another.

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“…The experimental study of miRNAs is quite involved and complicated by the fact that the miRNA and their targets have to be expressed at the same time in the same cell to lead to a measurable effect. For this reason, computational detection of miRNAs and their targets is important [8] [9]. Different approaches to computational miRNA detection have been applied, but most approaches are based on feature extraction followed by machine learning [10] [11].…”

Section: Introductionmentioning

confidence: 99%

Accurate Plant MicroRNA Prediction Can Be Achieved Using Sequence Motif Features

Yousef¹,

Allmer²,

Khalifa³

2016

JILSA

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MicroRNAs (miRNAs) are short (~21 nt) nucleotide sequences that are either co-transcribed during the production of mRNA or are organized in intergenic regions transcribed by RNA polymerase II. In animals, Drosha, and in plants DCL1 recognize pre-miRNAs which set themselves apart by their characteristic stem loop (hairpin) structure. This structure appears important for their recognition during the process of maturation leading to functioning mature miRNAs. A large body of research is available for computational pre-miRNA detection in animals, but less within the plant kingdom. For the prediction of pre-miRNAs, usually machine learning approaches are employed. Therefore, it is necessary to convert the pre-miRNAs into a set of features that can be calculated and many such features have been described. We here select a subset of the previously described features and add sequence motifs as new features. The resulting model which we called Motif-miRNAPred was tested on known pre-miRNAs listed in miRBase and its accuracy was compared to existing approaches in the field. With an accuracy of 99.95% for the generalized plant model, it distinguishes itself from previously published results which reach an average accuracy between 74% and 98%. We believe that our approach is useful for prediction of pre-miRNAs in plants without per species adjustment.

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Computational and Bioinformatics Methods for MicroRNA Gene Prediction

Cited by 16 publications

References 65 publications

MicroRNA categorization using sequence motifs and k-mers

MicroRNA categorization using sequence motifs and k-mers

Species Categorization via MicroRNAs - Based on 3’UTR Target Sites using Sequence Features

Accurate Plant MicroRNA Prediction Can Be Achieved Using Sequence Motif Features

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