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miRPlant

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

Small RNA sequencing is commonly used to identify novel miRNAs and to determine their expression levels in plants. There are several miRNA identification tools for animals such as miRDeep, miRDeep2 and miRDeep*. miRDeep-P was developed to identify plant miRNA using miRDeep's probabilistic model of miRNA biogenesis, but it depends on several third party tools and lacks a user-friendly interface. The objective of our miRPlant program is to predict novel plant miRNA, while providing a user-friendly interface with improved accuracy of prediction.

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vHoT

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

Some viruses have been reported to transcribe microRNAs, implying complex relationships between the host and the pathogen at the post-transcriptional level through microRNAs in virus-infected cells. Although many computational algorithms have been developed for microRNA target prediction, few have been designed exclusively to find cellular or viral mRNA targets of viral microRNAs in a user-friendly manner. To address this, we introduce the viral microRNA host target (vHoT) database for predicting interspecies interactions between viral microRNA and host genomes.

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mrsFAST

Submitted by ChenLiang on Sun, 09/10/2017 - 17:15

Abstract is not available.[1]

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p-TAREF

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

miRNAs are ~21 nucleotide long small noncoding RNA molecules, formed endogenously in most of the eukaryotes, which mainly control their target genes post transcriptionally by interacting and silencing them. While a lot of tools has been developed for animal miRNA target system, plant miRNA target identification system has witnessed limited development. Most of them have been centered around exact complementarity match. Very few of them considered other factors like multiple target sites and role of flanking regions.

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PMTED

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

MicroRNAs (miRNAs) are identified in nearly all plants where they play important roles in development and stress responses by target mRNA cleavage or translation repression. MiRNAs exert their functions by sequence complementation with target genes and hence their targets can be predicted using bioinformatics algorithms. In the past two decades, microarray technology has been employed to study genes involved in important biological processes such as biotic response, abiotic response, and specific tissues and developmental stages, many of which are miRNA targets.

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CANTATAdb

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

Long non-coding RNAs (lncRNAs) represent a class of potent regulators of gene expression that are found in a wide array of eukaryotes; however, our knowledge about these molecules in plants is still very limited. In particular, a number of model plant species still lack comprehensive data sets of lncRNAs and their annotations, and very little is known about their biological roles. To meet these shortcomings, we created an online database of lncRNAs in 10 model plant species.

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GREENC

Submitted by ChenLiang on Thu, 04/06/2017 - 17:53

Long non-coding RNAs (lncRNAs) are functional non-translated molecules greater than 200 nt. Their roles are diverse and they are usually involved in transcriptional regulation. LncRNAs still remain largely uninvestigated in plants with few exceptions. Experimentally validated plant lncRNAs have been shown to regulate important agronomic traits such as phosphate starvation response, flowering time and interaction with symbiotic organisms, making them of great interest in plant biology and in breeding.

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miR-PREFeR

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

Plant microRNA prediction tools that use small RNA-sequencing data are emerging quickly. These existing tools have at least one of the following problems: (i) high false-positive rate; (ii) long running time; (iii) work only for genomes in their databases; (iv) hard to install or use. We developed miR-PREFeR (miRNA PREdiction From small RNA-Seq data), which uses expression patterns of miRNA and follows the criteria for plant microRNA annotation to accurately predict plant miRNAs from one or more small RNA-Seq data samples of the same species.

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miReader

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

Along with computational approaches, NGS led technologies have caused a major impact upon the discoveries made in the area of miRNA biology, including novel miRNAs identification. However, to this date all microRNA discovery tools compulsorily depend upon the availability of reference or genomic sequences. Here, for the first time a novel approach, miReader, has been introduced which could discover novel miRNAs without any dependence upon genomic/reference sequences.

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CyTargetLinker

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

The high complexity and dynamic nature of the regulation of gene expression, protein synthesis, and protein activity pose a challenge to fully understand the cellular machinery. By deciphering the role of important players, including transcription factors, microRNAs, or small molecules, a better understanding of key regulatory processes can be obtained. Various databases contain information on the interactions of regulators with their targets for different organisms, data recently being extended with the results of the ENCODE (Encyclopedia of DNA Elements) project.

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