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CCmiR

Submitted by ChenLiang on Tue, 01/09/2018 - 17:39

The identification of microRNA (miRNA) target sites is important. In the past decade, dozens of computational methods have been developed to predict miRNA target sites. Despite their existence, rarely does a method consider the well-known competition and cooperation among miRNAs when attempts to discover target sites. To fill this gap, we developed a new approach called CCmiR, which takes the cooperation and competition of multiple miRNAs into account in a statistical model to predict their target sites.

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SIM

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

It has been shown that a random-effects framework can be used to test the association between a gene's expression level and the number of DNA copies of a set of genes. This gene-set modelling framework was later applied to find associations between mRNA expression and microRNA expression, by defining the gene sets using target prediction information.

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miRSeqNovel

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

We present miRSeqNovel, an R based workflow for miRNA sequencing data analysis. miRSeqNovel can process both colorspace (SOLiD) and basespace (Illumina/Solexa) data by different mapping algorithms. It finds differentially expressed miRNAs and gives conservative prediction of novel miRNA candidates with customized parameters. miRSeqNovel is freely available at http://sourceforge.net/projects/mirseq/files.[1]

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PheLiM

Submitted by ChenLiang on Sun, 09/10/2017 - 20:10

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.

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Director

Submitted by ChenLiang on Tue, 01/09/2018 - 17:47

High-throughput measurement technologies have triggered a rise in large-scale cancer studies containing multiple levels of molecular data. While there are a number of efficient methods to analyze individual data types, there are far less that enhance data interpretation after analysis. We present the R package Director, a dynamic visualization approach to linking and interrogating multiple levels of molecular data after analysis for clinically meaningful, actionable insights.

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Average: 4.5 (2 votes)

NqA

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

In this note, we propose an R function named NqA (Normalization qPCR Array, where qPCR is quantitative real-time polymerase chain reaction) suitable for the identification of a set of microRNAs (miRNAs) to be used for data normalization in view of subsequent validation studies with qPCR data.

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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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EPLMI

Submitted by ChenLiang on Tue, 01/09/2018 - 17:50

The interaction of miRNA and lncRNA is known to be important for gene regulations. However, not many computational approaches have been developed to analyse known interactions and predict the unknown ones. Given that there are now more evidences that suggest that lncRNA-miRNA interactions are closely related to their relative expression levels in the form of a titration mechanism, we analyzed the patterns in large-scale expression profiles of known lncRNA-miRNA interactions.

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