MicroRNAs (miRNAs), present in most metazoans, are small non-coding RNAs that control gene expression by negatively regulating translation through binding to the 3'UTR of mRNA transcripts. Previously, experimental and computational methods were used to construct miRNA gene repositories agreeing with careful submission guidelines.
MicroRNAs (miRNAs) are recognized as one of the most important families of non-coding RNAs that serve as important sequence-specific post-transcriptional regulators of gene expression. Identification of miRNAs is an important requirement for understanding the mechanisms of post-transcriptional regulation. Hundreds of miRNAs have been identified by direct cloning and computational approaches in several species. However, there are still many miRNAs that remain to be identified due to lack of either sequence features or robust algorithms to efficiently identify them.
Small non-coding RNAs (ncRNAs) are key regulators of plant development through modulation of the processing, stability, and translation of larger RNAs. We present small RNA data sets comprising more than 200 million aligned Illumina sequence reads covering all major cell types of the root as well as four distinct developmental zones. MicroRNAs (miRNAs) constitute a class of small ncRNAs that are particularly important for development. Of the 243 known miRNAs, 133 were found to be expressed in the root, and most showed tissue- or zone-specific expression patterns.
MicroRNAs (miRNAs) are small, single stranded RNAs with a key role in post-transcriptional regulation of thousands of genes across numerous species. While several computational methods are currently available for identifying miRNA genes, accurate prediction of the mature miRNA remains a challenge. Existing approaches fall short in predicting the location of mature miRNAs but also in finding the functional strand(s) of miRNA precursors.
MicroRNAs (miRNAs) are short, non-protein coding RNAs that direct the widespread phenomenon of post-transcriptional regulation of metazoan genes. The mature approximately 22-nt long RNA molecules are processed from genome-encoded stem-loop structured precursor genes. Hundreds of such genes have been experimentally validated in vertebrate genomes, yet their discovery remains challenging, and substantially higher numbers have been estimated.
The diversity of microRNAs and small-interfering RNAs has been extensively explored within angiosperms by focusing on a few key organisms such as Oryza sativa and Arabidopsis thaliana. A deeper division of the plants is defined by the radiation of the angiosperms and gymnosperms, with the latter comprising the commercially important conifers. The conifers are expected to provide important information regarding the evolution of highly conserved small regulatory RNAs.
miRNAs are small non coding RNA structures which play important roles in biological processes. Finding miRNA precursors in genomes is therefore an important task, where computational methods are required. The goal of these methods is to select potential pre-miRNAs which could be validated by experimental methods. With the new generation of sequencing techniques, it is important to have fast algorithms that are able to treat whole genomes in acceptable times.
The monarch butterfly (Danaus plexippus) is emerging as a model organism to study the mechanisms of circadian clocks and animal navigation, and the genetic underpinnings of long-distance migration. The initial assembly of the monarch genome was released in 2011, and the biological interpretation of the genome focused on the butterfly's migration biology.
The number of transcriptomic sequencing projects of various non-model organisms is still accumulating rapidly. As non-coding RNAs (ncRNAs) are highly abundant in living organism and play important roles in many biological processes, identifying fragmentary members of ncRNAs in small RNA-seq data is an important step in post-NGS analysis. However, the state-of-the-art ncRNA search tools are not optimized for next-generation sequencing (NGS) data, especially for very short reads.
The majority of existing computational tools rely on sequence homology and/or structural similarity to identify novel microRNA (miRNA) genes. Recently supervised algorithms are utilized to address this problem, taking into account sequence, structure and comparative genomics information. In most of these studies miRNA gene predictions are rarely supported by experimental evidence and prediction accuracy remains uncertain.
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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