- Open Access
MagiCMicroRna: a web implementation of AgiMicroRna using shiny
© Coonen et al.; licensee BioMed Central. 2015
- Received: 6 August 2014
- Accepted: 15 March 2015
- Published: 26 March 2015
MicroRNA expression can be quantified using sequencing techniques or commercial microRNA-expression arrays. Recently, the AgiMicroRna R-package was published that enabled systematic preprocessing and statistical analysis for Agilent microRNA arrays. Here we describe MagiCMicroRna, which is a user-friendly web interface for this package, together with a new filtering approach.
We used MagiCMicroRna to normalize and filter an Agilent miRNA microarray dataset of cancerous and normal tissues from 14 different patients. With the standard filtering procedure, 250 out of 817 microRNAs remained, whereas the new group-specific filtering approach resulted in broader datasets for further analysis in most groups (>279 microRNAs remaining).
The user-friendly web interface of MagiCMicroRna enables researchers to normalize and filter Agilent microarrays by the click of one button. Furthermore, MagiCMicroRna provides flexibility in choosing the filtering method. The new group-specific filtering approach lead to an increased number and additional tissue-specific microRNAs remaining for subsequent analysis compared to the standard procedure. The MagiCMicroRna web interface and source code can be downloaded from https://bitbucket.org/mutgx/magicmicrorna.git.
- Web interface
- Microarray analysis
- Data analysis
Micro-RNAs (miRNAs) play an important role in post-transcriptional regulation of gene expression by binding to messenger-RNA (mRNA) target genes thereby inducing their silencing. In order to investigate their effect, several commercial array platforms have been developed that quantify the levels of miRNAs present. One of the commonly used miRNA-expression array platforms is manufactured by Agilent. As with all transcriptomics research, data needs to be preprocessed and normalized first before differentially expressed features can be determined. Recently, López-Romero et al. published AgiMicroRna [1,2], an integrated analytical R-package enabling systematic preprocessing and statistical analysis for Agilent microRNA arrays. However, AgiMicroRna can be too demanding for researchers lacking sufficient programming experience.
Therefore we created MagiCMicroRna, a user-friendly web interface for the AgiMicroRna package. Besides the new appearance, we have also made some improvements to the original source code. With MagiCMicroRna, it is now possible to perform comprehensive preprocessing and filtering of Agilent miRNA arrays by the click of a button.
MagiCMicroRna is presented to the user in two ways. General users are invited to use the graphical web-interface that runs locally on their computer using the R-package shiny . Three source files need to be downloaded, after which MagiCMicroRna can be started using the runApp command of shiny. Advanced users that desire to tweak settings or change the analysis according to their specific needs, can also download and edit the source code for running it directly in R.
Both the web interface and source code accept two types of input files. First, the target-file links the raw data files with the experimental design and treatment groups. This file resembles to some extent the original used by López-Romero , but contains additional columns in order to perform the group-specific filtering (described in https://bitbucket.org/mutgx/magicmicrorna.git). Secondly, MagiCMicroRna needs the raw .txt-files obtained by Agilent Feature Extraction Software.
In view of the fact that statistical analysis procedures are hard to generalize due to differences in experimental designs, we decided to leave out the linear modelling implemented in the original AgiMicroRna package and focus only on the comprehensive normalization and filtering procedures. Our normalized and filtered output files can be used directly in most statistical analysis tools.
Group-specific filtering increases the number of filtered miRNAs that remain for subsequent (statistical) analyses
These findings are clearly demonstrating the added value of group-specific filtering in case of heterogeneous datasets where multiple comparisons are not required.
MagiCMicroRna continues on the foundation established by the original AgiMicroRna package. It is presented to the user as executable source code or as a user friendly web interface, enabling researchers without sufficient programming experience to normalize and filter their Agilent miRNA data. Besides the new appearance, some parts of the original source code have been modified to generate a normalized data file and perform a new filtering procedure, which enables group-specific filtering of miRNAs. Tested on a publicly available miRNA-dataset, MagiCMicroRna provided more flexibility in choosing the filtering method, in this case leading to an increased amount and additional tissue-specific miRNAs compared to the standard procedure.
Project name: MagiCMicroRna
Project home page: https://bitbucket.org/mutgx/magicmicrorna.git
Operating system(s): Platform independent
Programming language: R/shiny
Other requirements: R packages AgiMicroRna, shiny and shinyIncubator
Any restrictions to use by non-academics: none
We thank the researchers of the Toxicogenomics department for the use of their datasets that inspired us to create MagiCMicroRna.
The authors did not receive any specific funding for this work.
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