Current Progress of High-Throughput MicroRNA Differential Expression Analysis and Random Forest Gene Selection for Model and Non-Model Systems: an R Implementation

MicroRNAs are short non-coding RNA transcripts that act as master cellular regulators 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...

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Bibliographic Details
Main Authors: Zhang Jing, Hadj-Moussa Hanane, Storey Kenneth B.
Format: Article
Language:English
Published: De Gruyter 2016-12-01
Series:Journal of Integrative Bioinformatics
Online Access:https://doi.org/10.1515/jib-2016-306
Description
Summary:MicroRNAs are short non-coding RNA transcripts that act as master cellular regulators 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 genomesequenced 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.
ISSN:1613-4516