RNA-Seq techniques generate hundreds of millions of short RNA reads using next-generation sequencing (NGS). These RNA reads can be mapped to reference genomes to investigate changes of gene expression but improved procedures for mining large RNA-Seq datasets to extract valuable biological knowledge are needed. Computer scientists at the University of Missouri have developed RNAMiner—a multi-level bioinformatics protocol and pipeline for such datasets.
It includes five steps:
- mapping RNA-Seq reads to a reference genome,
- calculating gene expression values,
- identifying differentially expressed genes,
- predicting gene functions,
- constructing gene regulatory networks.
To demonstrate its utility, the scientists applied RNAMiner to datasets generated from Human, Mouse, Arabidopsis thaliana, and Drosophila melanogaster cells, and successfully identified differentially expressed genes, clustered them into cohesive functional groups, and constructed novel gene regulatory networks.
Availability – The RNAMiner web service is available at http://calla.rnet.missouri.edu/rnaminer/index.html.