Current progress of high-throughput microRNA differential expression analysis and random forest gene selection for model and non-model systems: an R implementation

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doi doi:10.2390/biecoll-jib-2016-306
submission November 30, 2016
last revision December 16, 2016
published December 22, 2016
NCBI PubMed PubMed ID 28187420

Jing Zhang, Hanane Hadj-Moussa and Kenneth B. Storey

Correspondence should be addressed to:
Kenneth Storey
Department of Biology and Department of Chemistry, Carleton University, K1S 5B6, 1125 Colonel By Drive, Ottawa, Ontario, Canada
ac.notelrac@nullyerots_htennek


Abstract

MicroRNAs are short non-coding RNA transcripts that act as master cellular egulators with roles in orchestrating virtually all biological functions. The recent affordability and widespread use of high-throughput microRNA profiling technologies has grown along with the advancement of bioinformatics tools available for analysis of the mounting data flow. While there are many computational resources available for the management of data from genome sequenced animals, researchers are often faced with the challenge of identifying the biological implications of the daunting amount of data generated from these high-throughput technologies. In this article, we review the current state of highthroughput microRNA expression profiling platforms, data analysis processes, and computational tools in the context of comparative molecular physiology. We also present RBioMIR and RBioFS, our R package implementations for differential expression analysis and random forest-based gene selection. Detailed installation guides are available at kenstoreylab.com.

Reference

Jing Zhang, Hanane Hadj-Moussa and Kenneth B. Storey. Current progress of high-throughput microRNA differential expression analysis and random forest gene selection for model and non-model systems: an R implementation. Journal of Integrative Bioinformatics, 13(5):306, 2016. Online Journal: http://journal.imbio.de/index.php?paper_id=306
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