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.
Traditionally, microarrays were almost exclusively used for the genome-wide analysis of differential gene expression. But nowadays, their scope of application has been extended to various genomic features, such as microRNAs (miRNAs), proteins and DNA methylation (DNAm). Most available methods for the visualization of these datasets are focused on individual platforms and are not capable of integratively visualizing multiple microarray datasets from cross-platform studies.
The complex traits of an organism are associated with a complex interplay between genetic factors (GFs) and environmental factors (EFs). However, compared with protein-coding genes and microRNAs, there is a paucity of computational methods and bioinformatic resource platform for understanding the associations between lncRNA and EF.
MicroRNAs are a predominant type of small non-coding RNAs approximately 21 nucleotides in length that play an essential role at the post-transcriptional level by either RNA degradation, translational repression or both through an RNA-induced silencing complex. Identification of these molecules can aid the dissecting of their regulatory functions. The secondary structures of plant pre-miRNAs are much more complex than those of animal pre-miRNAs. In contrast to prediction tools for animal pre-miRNAs, much less effort has been contributed to plant pre-miRNAs.
High-throughput RNA interference (RNAi) screening has opened up a path to investigating functional genomics in a genome-wide pattern. However, such studies are often restricted to assays that have a single readout format. Recently, advanced image technologies have been coupled with high-throughput RNAi screening to develop high-content screening, in which one or more cell image(s), instead of a single readout, were generated from each well.
MOTIVATION: PIWI-interacting RNAs (piRNAs) are a class of small non-coding RNAs that are highly abundant in the germline. One important role of piRNAs is to defend genome integrity by guiding PIWI proteins to silence transposable elements (TEs), which have a high potential to cause deleterious effects on their host. The mechanism of piRNA-mediated post-transcriptional silencing was also observed to affect mRNAs, suggesting that piRNAs might play a broad role in gene expression regulation.
Cancer classification has been a crucial topic of research in cancer treatment. In the last decade, messenger RNA (mRNA) expression profiles have been widely used to classify different types of cancers. With the discovery of a new class of small non-coding RNAs; known as microRNAs (miRNAs), various studies have shown that the expression patterns of miRNA can also accurately classify human cancers. Therefore, there is a great demand for the development of machine learning approaches to accurately classify various types of cancers using miRNA expression data.
RNAComposer is a fully automated, web-interfaced system for RNA 3D structure prediction, freely available at http://rnacomposer.cs.put.poznan.pl/ and http://rnacomposer.ibch.poznan.pl/. Its main components are: manually curated database of RNA 3D structure elements, highly efficient computational engine and user-friendly web application.
MicroRNA (miRNA) plays an important role as a regulator in biological processes. Identification of (pre-)miRNAs helps in understanding regulatory processes. Machine learning methods have been designed for pre-miRNA identification. However, most of them cannot provide reliable predictive performances on independent testing datasets. We assumed this is because the training sets, especially the negative training sets, are not sufficiently representative.
RNAi is broadly used to map gene regulatory networks, but the identification of genes that are responsible for the observed phenotypes is challenging, assmall interfering RNAs (siRNAs) simultaneously downregulate the intended on targets and many partially complementary off targets. Additionally, the scarcity of publicly available control datasets hinders the development and comparative evaluation of computational methods for analyzing the data.
Our database aim to make it easy for researchers to find the right tools or data source for their own specific study in miRNA field. Now we have collect >1000 tools. This database will update when every new 100 tools add in. Last update time 22nd April, 2018 (v1.2).
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