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CePa

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

CePa is an R package aiming to find significant pathways through network topology information. The package has several advantages compared with current pathway enrichment tools. First, pathway node instead of single gene is taken as the basic unit when analysing networks to meet the fact that genes must be constructed into complexes to hold normal functions. Second, multiple network centralities are applied simultaneously to measure importance of nodes from different aspects to make a full view on the biological system.

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miRseqViewer

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

Deep sequencing of small RNAs has become a routine process in recent years, but no dedicated viewer is as yet available to explore the sequence features simultaneously along with secondary structure and gene expression of microRNA (miRNA). We present a highly interactive application that visualizes the sequence alignment, secondary structure and normalized read counts in synchronous multipanel windows.

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NoiseMaker

Submitted by ChenLiang on Thu, 04/06/2017 - 18:47

High-throughput screening (HTS) is a common technique for both drug discovery and basic research, but researchers often struggle with how best to derive hits from HTS data. While a wide range of hit identification techniques exist, little information is available about their sensitivity and specificity, especially in comparison to each other. To address this, we have developed the open-source NoiseMaker software tool for generation of realistically noisy virtual screens.

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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.

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birta

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

There have been many successful experimental and bioinformatics efforts to elucidate transcription factor (TF)-target networks in several organisms. For many organisms, these annotations are complemented by miRNA-target networks of good quality. Attempts that use these networks in combination with gene expression data to draw conclusions on TF or miRNA activity are, however, still relatively sparse.

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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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PACRAT

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

Analysis of intergenic sequences for purposes such as the investigation of transcriptional signals or the identification of small RNA genes is frequently complicated by traditional biological database structures. Genome data is commonly treated as chromosome-length sequence records, detailed by gene calls demarcating subsequences of the chromosomes. Given this model, the determination of non-called subsequences between any gene and its nearest neighbors requires an exhaustive search of all gene calls associated with the chromosome.

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ARTS

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

A fast growing number of non-coding RNAs have recently been discovered to play essential roles in many cellular processes. Similar to proteins, understanding the functions of these active RNAs requires methods for analyzing their tertiary structures. However, in contrast to the wide range of structure-based approaches available for proteins, there is still a lack of methods for studying RNA structures.

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DynaMod

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

A comprehensive analysis of enriched functional categories in differentially expressed genes is important to extract the underlying biological processes of genome-wide expression profiles. Moreover, identification of the network of significant functional modules in these dynamic processes is an interesting challenge. This study introduces DynaMod, a web-based application that identifies significant functional modules reflecting the change of modularity and differential expressions that are correlated with gene expression profiles under different conditions.

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