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Open Access Highly Accessed Software

Guide: a desktop application for analysing gene expression data

Jarny Choi

Author Affiliations

The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, 3052, Melbourne, Australia

Department of Medical Biology, The University of Melbourne, Parkville, 3010, Melbourne, Australia

BMC Genomics 2013, 14:688  doi:10.1186/1471-2164-14-688

Published: 7 October 2013

Abstract

Background

Multiplecompeting bioinformatics tools exist for next-generation sequencing data analysis. Many of these tools are available as R/Bioconductor modules, and it can be challenging for the bench biologist without any programming background to quickly analyse genomics data. Here, we present an application that is designed to be simple to use, while leveraging the power of R as the analysis engine behind the scenes.

Results

Genome Informatics Data Explorer (Guide) is a desktop application designed for the bench biologist to analyse RNA-seq and microarray gene expression data. It requires a text file of summarised read counts or expression values as input data, and performs differential expression analyses at both the gene and pathway level. It uses well-established R/Bioconductor packages such as limma for its analyses, without requiring the user to have specific knowledge of the underlying R functions. Results are presented in figures or interactive tables which integrate useful data from multiple sources such as gene annotation and orthologue data. Advanced options include the ability to edit R commands to customise the analysis pipeline.

Conclusions

Guide is a desktop application designed to query gene expression data in a user-friendly way while automatically communicating with R. Its customisation options make it possible to use different bioinformatics tools available through R/Bioconductor for its analyses, while keeping the core usage simple. Guide is written in the cross-platform framework of Qt, and is freely available for use from http://guide.wehi.edu.au webcite.

Keywords:
Data analysis; R; Gene expression; RNA-seq; Microarray; Differential expression; Software