This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Advances in transcriptomic methods have led to a large number of published genome-wide expression studies (GWES), in humans and in model organisms. For several years, GWES involved the use of microarray platforms to compare genome-expression data for two or more groups of samples of interest. Meta-analysis of GWES is a powerful approach for the identification of differentially expressed genes in biological topics or diseases of interest, combining information from multiple primary studies. In this article, I review the main features of available software for carrying out meta-analysis of GWES. I describe seven packages from the Bioconductor platform and 5 packages from the CRAN platform. In addition, nine previously described programs and two online programs are reviewed. Finally, I discuss advantages and disadvantages of these available programs and propose key points for future developments.
This manuscript is currently submitted to a preprint to PeerJ preprints. I plan to submit it later to a peer-reviewed scientific journal.