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miRNA Prediction

Semirna

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

Many plant genomes are already known, and new ones are being sequenced every year. The next step for researchers is to identify all of the functional elements in these genomes, including the important class of functional elements known as microRNAs (miRNAs), which are involved in posttranscriptional regulatory pathways. However, computational tools for predicting new plant miRNAs are limited, and there is a particular need for tools that can be used easily by laboratory researchers.

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SeRPeNT

Submitted by ChenLiang on Tue, 01/09/2018 - 19:03

Small non-coding RNAs (sncRNAs) are highly abundant molecules that regulate essential cellular processes and are classified according to sequence and structure. Here we argue that read profiles from size-selected RNA sequencing capture the post-transcriptional processing specific to each RNA family, thereby providing functional information independently of sequence and structure.

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BosFinder

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

MicroRNAs (miRNAs) are small non-coding RNAs that modulate gene expression transcriptionally (transcriptional activation or inactivation) and/or post-transcriptionally (translation inhibition or degradation of their target mRNAs). This phenomenon has significant roles in growth and developmental processes in plants and animals. Bos taurus is one of the most important livestock animals, having great importance in food and economical sciences and industries.

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Wgssat

Submitted by ChenLiang on Tue, 01/09/2018 - 19:14

Mining and characterization of SSR markers from whole genomes provide valuable information about biological significance of SSR distribution and also facilitate development of markers for genetic analysis. WGS-SSR Annotation Tool (WGSSAT) is a graphical user interface pipeline developed using Java Netbeans and Perl scripts which facilitates in simplifying the process of SSR mining and characterization.

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MicRooN

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

Since Ambros' discovery of small non-protein coding RNAs in the early 1990s, the past two decades have seen an upsurge in the number of reports of predicted microRNAs (miR), which have been implicated in various functions. The correlation of miRs with cancer has spurred the usage of this class of non-coding RNAs in various cancer therapies, although most of them are at trial stages. However, the experimental identification of a miR to be associated with cancer is still an elaborate, time-consuming process.

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deepSOM

Submitted by ChenLiang on Sun, 01/08/2017 - 16:51

The computational prediction of novel microRNA within a full genome involves identifying sequences having the highest chance of being a miRNA precursor (pre-miRNA). These sequences are usually named candidates to miRNA. The well-known pre-miRNAs are usually only a few in comparison to the hundreds of thousands of potential candidates to miRNA that have to be analyzed, which makes this task a high classimbalance classification problem.

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miRNAss

Submitted by ChenLiang on Tue, 01/09/2018 - 19:24

Although many machine learning techniques have been proposed for distinguishing miRNA hairpins from other stem-loop sequences, most of the current methods use supervised learning, which requires a very good set of positive and negative examples. Those methods have important practical limitations when they have to be applied to a real prediction task. First, there is the challenge of dealing with a scarce number of positive (well-known) pre-miRNA examples.

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