<scp>novoMIR</scp>					: De Novo Prediction of MicroRNA‐Coding Regions in a Single Plant‐Genome

Teune, Jan-Hendrik; Steger, Gerhard

doi:10.4061/2010/495904

Cited by 37 publications

(34 citation statements)

References 53 publications

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“…For these reasons, we used the following criteria for selection of the first four tools integrated in miRQuest: a) predictors with greater usage according to citations in the literature; b) high quality of results (e.g., accuracy) according to a literature review; c) availability, i.e., the tool was available for downloading; and finally, d) at least one predictor should have been designed for analysis of human or for plant RNAs, to avoid a bias to specific groups of organisms. As a result of all these criteria, we selected four main tools for this first version of miRQuest: Triplet-SVM (Xue et al, 2005); MiPred (Jiang et al, 2007); HHMMiR, version 1.2 (Kadri et al, 2009);andNOVOMIR, version 2011 (Teune andSteger, 2010).…”

Section: Methodsmentioning

confidence: 99%

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miRQuest: integration of tools on a Web server for microRNA research

et al. 2016

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ABSTRACT. This report describes the miRQuest -a novel middleware available in a Web server that allows the end user to do the miRNA research in a user-friendly way. It is known that there are many prediction tools for microRNA (miRNA) identification that use different programming languages and methods to realize this task. It is difficult to understand each tool and apply it to diverse datasets and organisms available for miRNA analysis. miRQuest can easily be used by biologists and researchers with limited experience with bioinformatics. We built it using the middleware architecture on a Web platform for miRNA research that performs two main functions: i) integration of different miRNA prediction tools for miRNA identification in a user-friendly environment; and ii) comparison of these prediction tools. In both cases, the user provides sequences (in FASTA format) as an input set for the analysis and comparisons. All the tools were selected on the basis of a survey of the literature on the available tools for miRNA prediction. As results, three different cases of use of the tools are also described, where one is the miRNA identification analysis in 30 different species. Finally, miRQuest seems to be a novel and useful tool; and it is freely available for both benchmarking and miRNA identification at

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Section: Methodsmentioning

confidence: 99%

“…NOVOMIR is an algorithm for identifying specific miRNA genes in plant genomes (Teune and Steger, 2010). This is a Perl script that uses a series of filters and statistical models to discriminate a pre-miRNA from all other RNAs as well as to locate the miRNA in a supposed premiRNA sequence.…”

Section: Novomirmentioning

confidence: 99%

miRQuest: integration of tools on a Web server for microRNA research

et al. 2016

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“…25 NovoMIR finds all possible sequences capable of forming the canonical hairpin structure consistent with miRNA biogenesis. These sequences were then examined for sequence similarity to miRNA precursor sequences catalogued in miRBase and PMRD.…”

Section: Softwarementioning

confidence: 99%

“…novoMIR relies on RNAfold and RNAshapes for secondary structure prediction of precursors and the associated free energy measurements. 25 The Bethune genome and Unigene sequences were input into novoMIR. All nonmiRNA hairpin sequences were removed and the remaining putative miRNAs and their hairpins were compiled into a database.…”

Section: Identification Of Micrornas In the Flax Genome And In The Esmentioning

confidence: 99%

Computational prediction of candidate miRNAs and their targets from the completed Linum ussitatissimum genome and EST database

Moss

Cullis

2012

J Nucleic Acids Invest

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Linum usitatissimum (Flax) is an important agronomic crop grown for its fiber (linen) and oil (linseed oil). In spite of thousands of years of breeding some fiber varieties have been shown to rapidly respond to environmental stress with heritable changes to its genome. Many microRNAs (miRNAs) appear to be induced by abiotic or biotic conditions experienced through the plant life cycle. Here a bioinformatics approach is used to screen for miRNAs previously identified in other plant species, as well as to predict putative miRNAs unique to a particular species which may not have been identified as they are less abundant or dependent upon a specific set of environmental conditions. Twelve miRNA genes were identified in flax on the basis of unique premiRNA positions with structural homology to plant pre-miRNAs and complete sequence homology to published plant miRNAs. These miRNAs were found to belong to seven miRNA families, with an additional two matches corresponding to as yet unnamed poplar miRNAs and a parologous miRNA with partial sequence homology to mtr-miR4414b. An additional 649 novel and distinct flax miRNA genes were identified to form from canonical hairpin structures and to have putative targets among the ~30,000 flax Unigenes.

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“…Apart from our previous study [31], only Lopes and colleagues attempted to use pre-miRNAs to discriminate between species [33]. However, they resorted to establishing ab initio pre-miRNA detection models with the same bias on negative data as existing pre-miRNA detection methods [26,[39][40][41][42]; using the same training and testing strategies [32,[42][43][44]. Furthermore, a large part of the features they used assesses structural features of pre-miRNAs, which poses problems when analyzing closely related species since structure is more conserved than sequence.…”

Section: Introductionmentioning

confidence: 99%

Categorization of species based on their microRNAs employing sequence motifs, information-theoretic sequence feature extraction, and k-mers

Yousef

Nigatu

Levy

et al. 2017

EURASIP J. Adv. Signal Process.

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Background: Diseases like cancer can manifest themselves through changes in protein abundance, and microRNAs (miRNAs) play a key role in the modulation of protein quantity. MicroRNAs are used throughout all kingdoms and have been shown to be exploited by viruses to modulate their host environment. Since the experimental detection of miRNAs is difficult, computational methods have been developed. Many such tools employ machine learning for pre-miRNA detection, and many features for miRNA parameterization have been proposed. To train machine learning models, negative data is of importance yet hard to come by; therefore, we recently started to employ pre-miRNAs from one species as positive data versus another species' pre-miRNAs as negative examples based on sequence motifs and k-mers. Here, we introduce the additional usage of information-theoretic (IT) features. Results: Pre-miRNAs from one species were used as positive and another species' pre-miRNAs as negative training data for machine learning. The categorization capability of IT and k-mer features was investigated. Both feature sets and their combinations yielded a very high accuracy, which is as good as the previously suggested sequence motif and k-mer based method. However, for obtaining a high performance, a sufficiently large phylogenetic distance between the species and sufficiently high number of pre-miRNAs in the training set is required. To examine the contribution of the IT and k-mer features, an information gain-based feature ranking was performed. Although the top 3 are IT features, 80% of the top 100 features are k-mers. The comparison of all three individual approaches (motifs, IT, and k-mers) shows that the distinction of species based on their pre-miRNAs k-mers are sufficient. Conclusions: IT sequence feature extraction enables the distinction among species and is less computationally expensive than motif calculations. However, since IT features need larger amounts of data to have enough statistics for producing highly accurate results, future categorization into species can be effectively done using k-mers only. The biological reasoning for this is the existence of a codon bias between species which can, at least, be observed in exonic miRNAs. Future work in this direction will be the ab initio detection of pre-miRNA. In addition, prediction of pre-miRNA from RNA-seq can be done.

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novoMIR : De Novo Prediction of MicroRNA‐Coding Regions in a Single Plant‐Genome

Cited by 37 publications

References 53 publications

miRQuest: integration of tools on a Web server for microRNA research

miRQuest: integration of tools on a Web server for microRNA research

Computational prediction of candidate miRNAs and their targets from the completed Linum ussitatissimum genome and EST database

Categorization of species based on their microRNAs employing sequence motifs, information-theoretic sequence feature extraction, and k-mers

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