LETTER


maGUI: A Graphical User Interface for Analysis and Annotation of DNA Microarray Data



Dhammapal Bharne1, Praveen Kant1, Vaibhav Vindal*, 1
1 Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad, India.


© 2019 Bharne et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad, India; Tel/Fax: +91-40-23134589; E-mail: vaibhav@uohyd.ac.in


Abstract

Summary:

maGUI is a graphical user interface designed to analyze microarray data produced from experiments performed on various platforms such as Affymetrix, Agilent, Illumina, and Nimblegen and so on, automatically. It follows an integrated workflow for pre-processing and analysis of the microarray data. The user may proceed from loading of microarray data to normalization, quality check, filtering, differential gene expression, principal component analysis, clustering and classification. It also provides miscellaneous applications such as gene set test and enrichment analysis for identifying gene symbols using Bioconductor packages. Further, the user can build a co-expression network for differentially expressed genes. Tables and figures generated during the analysis can be viewed and exported to local disks. The graphical user interface is very friendly especially for the biologists to perform the most microarray data analyses and annotations without much need of learning R command line programming.

Availability and Implementation:

maGUI is an R package which can be downloaded freely from Comprehensive R Archive Network resource. It can be installed in any R environment with version 3.0.2 or above.

Keywords: Graphical user interface, R programming language, Bioconductor, Comprehensive R Archive Network, Microarray data analysis, Gene set test analysis, Gene set enrichment analysis.