You are here

Windows

mirnanalyze

Submitted by ChenLiang on Thu, 04/06/2017 - 19:32

The current state-of-the-art in cancer diagnosis and treatment is not ideal; diagnostic tests are accurate but invasive, and treatments are "one-size fits-all" instead of being personalized. Recently, miRNA's have garnered significant attention as cancer biomarkers, owing to their ease of access (circulating miRNA in the blood) and stability. There have been many studies showing the effectiveness of miRNA data in diagnosing specific cancer types, but few studies explore the role of miRNA in predicting treatment outcome.

Rating: 
Average: 5 (1 vote)

ShrinkBayes

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

Complex designs are common in (observational) clinical studies. Sequencing data for such studies are produced more and more often, implying challenges for the analysis, such as excess of zeros, presence of random effects and multi-parameter inference. Moreover, when sample sizes are small, inference is likely to be too liberal when, in a Bayesian setting, applying a non-appropriate prior or to lack power when not carefully borrowing information across features.

Rating: 
Average: 5 (1 vote)

Functional interpretation of microRNA-mRNA association

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

The prediction of microRNA targets is a challenging task that has given rise to several prediction algorithms. Databases of predicted targets can be used in a microRNA target enrichment analysis, enhancing our capacity to extract functional information from gene lists. However, the available tools in this field analyze gene sets one by one limiting their use in a meta-analysis. Here, we present an R system for miRNA enrichment analysis that is suitable for systems biology.

Rating: 
Average: 5 (1 vote)

application in consensus ranking of microRNA targets

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

MicroRNAs are very recently discovered small noncoding RNAs, responsible for negative regulation of gene expression. Members of this endogenous family of small RNA molecules have been found implicated in many genetic disorders. Each microRNA targets tens to hundreds of genes. Experimental validation of target genes is a time- and cost-intensive procedure. Therefore, prediction of microRNA targets is a very important problem in computational biology.

Rating: 
Average: 5 (1 vote)

MicroSyn

Submitted by ChenLiang on Thu, 04/06/2017 - 19:37

The traditional phylogeny analysis within gene family is mainly based on DNA or amino acid sequence homologies. However, these phylogenetic tree analyses are not suitable for those "non-traditional" gene families like microRNA with very short sequences. For the normal protein-coding gene families, low bootstrap values are frequently encountered in some nodes, suggesting low confidence or likely inappropriateness of placement of those members in those nodes.

Rating: 
Average: 5 (1 vote)

MicroTarget

Submitted by ChenLiang on Sun, 09/10/2017 - 20:23

MicroRNAs are known to play an essential role in gene regulation in plants and animals. The standard method for understanding microRNA-gene interactions is randomized controlled perturbation experiments. These experiments are costly and time consuming. Therefore, use of computational methods is essential. Currently, several computational methods have been developed to discover microRNA target genes. However, these methods have limitations based on the features that are used for prediction.

Rating: 
Average: 5 (1 vote)

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.

Rating: 
Average: 5 (1 vote)

PHMMTSs

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

The computational identification of non-coding RNA regions on the genome is currently receiving much attention. However, it is essentially harder than gene-finding problems for protein-coding regions because non-coding RNA sequences do not have strong statistical signals. Since comparative sequence analysis is effective for non-coding RNA detection, efficient computational methods are expected for structural alignment of RNA sequences.

Rating: 
Average: 5 (1 vote)

biRte

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

In the last years there has been an increasing effort to computationally model and predict the influence of regulators (transcription factors, miRNAs) on gene expression. Here we introduce biRte as a computationally attractive approach combining Bayesian inference of regulator activities with network reverse engineering. biRte integrates target gene predictions with different omics data entities (e.g. miRNA and mRNA data) into a joint probabilistic framework.

Rating: 
Average: 5 (1 vote)

iSmaRT

Submitted by ChenLiang on Mon, 01/09/2017 - 13:33

The interest in investigating the biological roles of small non-coding RNAs (sncRNAs) is increasing, due to the pleiotropic effects of these molecules exert in many biological contexts. While several methods and tools are available to study microRNAs (miRNAs), only few focus on novel classes of sncRNAs, in particular PIWI-interacting RNAs (piRNAs). To overcome these limitations, we implemented iSmaRT (integrative Small RNA Tool-kit), an automated pipeline to analyze smallRNA-Seq data.

Rating: 
Average: 5 (1 vote)

Pages

Subscribe to Windows