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Mirinho

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

Several methods exist for the prediction of precursor miRNAs (pre-miRNAs) in genomic or sRNA-seq (small RNA sequences) data produced by NGS (Next Generation Sequencing). One key information used for this task is the characteristic hairpin structure adopted by pre-miRNAs, that in general are identified using RNA folders whose complexity is cubic in the size of the input. The vast majority of pre-miRNA predictors then rely on further information learned from previously validated miRNAs from the same or a closely related genome for the final prediction of new miRNAs.

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targetS

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

Currently available microRNA (miRNA) target prediction algorithms require the presence of a conserved seed match to the 5' end of the miRNA and limit the target sites to the 3' untranslated regions of mRNAs. However, it has been noted that these requirements may be too stringent, leading to a substantial number of missing targets.

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iSRAP

Submitted by ChenLiang on Thu, 04/06/2017 - 17:59

Small non-coding RNAs have been significantly recognized as the key modulators in many biological processes, and are emerging as promising biomarkers for several diseases. These RNA species are transcribed in cells and can be packaged in extracellular vesicles, which are small vesicles released from many biotypes, and are involved in intercellular communication.

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miRModule

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

MicroRNAs (miRNAs) play critical roles in gene regulation. Although it is well known that multiple miRNAs may work as miRNA modules to synergistically regulate common target mRNAs, the understanding of miRNA modules is still in its infancy.

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Omics Pipe

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

Omics Pipe (http://sulab.scripps.edu/omicspipe) is a computational framework that automates multi-omics data analysis pipelines on high performance compute clusters and in the cloud. It supports best practice published pipelines for RNA-seq, miRNA-seq, Exome-seq, Whole-Genome sequencing, ChIP-seq analyses and automatic processing of data from The Cancer Genome Atlas (TCGA). Omics Pipe provides researchers with a tool for reproducible, open source and extensible next generation sequencing analysis.

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Raccess

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

The importance of RNA sequence analysis has been increasing since the discovery of various types of non-coding RNAs transcribed in animal cells. Conventional RNA sequence analyses have mainly focused on structured regions, which are stabilized by the stacking energies acting on adjacent base pairs. On the other hand, recent findings regarding the mechanisms of small interfering RNAs (siRNAs) and transcription regulation by microRNAs (miRNAs) indicate the importance of analyzing accessible regions where no base pairs exist.

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miARma-Seq

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

Large-scale RNAseq has substantially changed the transcriptomics field, as it enables an unprecedented amount of high resolution data to be acquired. However, the analysis of these data still poses a challenge to the research community. Many tools have been developed to overcome this problem, and to facilitate the study of miRNA expression profiles and those of their target genes. While a few of these enable both kinds of analysis to be performed, they also present certain limitations in terms of their requirements and/or the restrictions on data uploading.

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Automatic learning of pre-miRNAs from different species

Submitted by ChenLiang on Thu, 04/06/2017 - 17:26

Discovery of microRNAs (miRNAs) relies on predictive models for characteristic features from miRNA precursors (pre-miRNAs). The short length of miRNA genes and the lack of pronounced sequence features complicate this task. To accommodate the peculiarities of plant and animal miRNAs systems, tools for both systems have evolved differently. However, these tools are biased towards the species for which they were primarily developed and, consequently, their predictive performance on data sets from other species of the same kingdom might be lower.

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sRNAnalyzer

Submitted by ChenLiang on Tue, 01/09/2018 - 19:05

Although many tools have been developed to analyze small RNA sequencing (sRNA-Seq) data, it remains challenging to accurately analyze the small RNA population, mainly due to multiple sequence ID assignment caused by short read length. Additional issues in small RNA analysis include low consistency of microRNA (miRNA) measurement results across different platforms, miRNA mapping associated with miRNA sequence variation (isomiR) and RNA editing, and the origin of those unmapped reads after screening against all endogenous reference sequence databases.

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miRNALasso

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

MicroRNAs (miRNAs) play important roles in general biological processes and diseases pathogenesis. Identifying miRNA target genes is an essential step to fully understand the regulatory effects of miRNAs. Many computational methods based on the sequence complementary rules and the miRNA and mRNA expression profiles have been developed for this purpose. It is noted that there have been many sequence features of miRNA targets available, including the context features of the target sites, the thermodynamic stability and the accessibility energy for miRNA-mRNA interaction.

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