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Target Prediction

MicroTarget

Submitted by ChenLiang on Sun, 09/10/2017 - 20:23

MicroRNAs are known to play an essential role in gene regulation in plants and animals. The standard method for understanding microRNA-gene interactions is randomized controlled perturbation experiments. These experiments are costly and time consuming. Therefore, use of computational methods is essential. Currently, several computational methods have been developed to discover microRNA target genes. However, these methods have limitations based on the features that are used for prediction.

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miRTarVis+

Submitted by ChenLiang on Sun, 09/10/2017 - 20:31

In this paper, we present miRTarVis+, a Web-based interactive visual analytics tool for miRNA target predictions and integrative analyses of multiple prediction results. Various microRNA (miRNA) target prediction algorithms have been developed to improve sequence-based miRNA target prediction by exploiting miRNA-mRNA expression profile data. There are also a few analytics tools to help researchers predict targets of miRNAs.

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IsomiR Bank

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

: Next-Generation Sequencing (NGS) technology has revealed that microRNAs (miRNAs) are capable of exhibiting frequent differences from their corresponding mature reference sequences, generating multiple variants: the isoforms of miRNAs (isomiRs). These isomiRs mainly originate via the imprecise and alternative cleavage during the pre-miRNA processing and post-transcriptional modifications that influence miRNA stability, their sub-cellular localization and target selection.

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New support vector machine-based method for microRNA target prediction

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

MicroRNA (miRNA) plays important roles in cell differentiation, proliferation, growth, mobility, and apoptosis. An accurate list of precise target genes is necessary in order to fully understand the importance of miRNAs in animal development and disease. Several computational methods have been proposed for miRNA target-gene identification. However, these methods still have limitations with respect to their sensitivity and accuracy. Thus, we developed a new miRNA target-prediction method based on the support vector machine (SVM) model.

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CCmiR

Submitted by ChenLiang on Tue, 01/09/2018 - 17:39

The identification of microRNA (miRNA) target sites is important. In the past decade, dozens of computational methods have been developed to predict miRNA target sites. Despite their existence, rarely does a method consider the well-known competition and cooperation among miRNAs when attempts to discover target sites. To fill this gap, we developed a new approach called CCmiR, which takes the cooperation and competition of multiple miRNAs into account in a statistical model to predict their target sites.

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DPMIND

Submitted by ChenLiang on Tue, 01/09/2018 - 17:48

MicroRNAs (miRNAs) play essential roles in plant growth, development and stress responses through post-transcriptionally regulating the expression levels of their target mRNAs. Although some tools and databases were developed for predicting the relationships between miRNAs and their targets (miR-Tar), most of them were dependent on computational methods without experimental validations. With development of degradome sequencing techniques, researchers can identify potential interactions based on degradome sequencing data.

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miRCarta

Submitted by ChenLiang on Tue, 01/09/2018 - 18:46

The continuous increase of available biological data as consequence of modern high-throughput technologies poses new challenges for analysis techniques and database applications. Especially for miRNAs, one class of small non-coding RNAs, many algorithms have been developed to predict new candidates from next-generation sequencing data. While the amount of publications describing novel miRNA candidates keeps steadily increasing, the current gold standard database for miRNAs - miRBase - has not been updated since June 2014.

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iSmaRT

Submitted by ChenLiang on Mon, 01/09/2017 - 13:33

The interest in investigating the biological roles of small non-coding RNAs (sncRNAs) is increasing, due to the pleiotropic effects of these molecules exert in many biological contexts. While several methods and tools are available to study microRNAs (miRNAs), only few focus on novel classes of sncRNAs, in particular PIWI-interacting RNAs (piRNAs). To overcome these limitations, we implemented iSmaRT (integrative Small RNA Tool-kit), an automated pipeline to analyze smallRNA-Seq data.

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TarHunter

Submitted by ChenLiang on Tue, 01/09/2018 - 19:06

In plants, the targets of deeply conserved microRNAs (miRNAs) were comprehensively studied. Evidence is emerging that targets of less conserved miRNAs, endogenous target mimics (eTM) and non-canonical targets play functional roles. Existing plant miRNA prediction tools lack a cross-species conservation filter and eTM prediction function. We developed a tool named TarHunter that features a strict cross-species conservation filter and capability of predicting eTMs.

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miRTP

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

We used a machine learning method, the nearest neighbor algorithm (NNA), to learn the relationship between miRNAs and their target proteins, generating a predictor which can then judge whether a new miRNA-target pair is true or not. We acquired 198 positive (true) miRNA-target pairs from Tarbase and the literature, and generated 4,888 negative (false) pairs through random combination. A 0/1 system and the frequencies of single nucleotides and di-nucleotides were used to encode miRNAs into vectors while various physicochemical parameters were used to encode the targets.

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