IsoDE2
Introduction
A major application of RNA-Seq is to perform differential gene expression analysis. Many tools exist to analyze differentially expressed genes for datasets with biological and/or technical replicates. However, due to the relatively high cost, many RNA-Seq experiments have no, or very few, replicates.
IsoDE is a software package that can be used to perform differential gene expression analysis for RNA-Seq data both with and without replicates. IsoDE is based on bootstrapping, which provides a principled way to test for differential expression based on fold changes obtained from FPKM estimates obtained by resampling the original read alignments. This strategy can be used in conjunction with any method for estimating individual gene expression levels from aligned RNA-Seq reads; in the current version of IsoDE (IsoDE2) we rely on the IsoEM2 algorithm, an accurate expectation-maximization algorithm for gene/isoform level estimation that performs fast in-memory bootstrapping. For details see our Bioinformatics Application Note.
Source code and sample data
The IsoEM2/IsoDE2 software is written in Java so it can be run on any platform with a Java virtual machine. See the README file for installation instructions. Wrappers for the Galaxy platform along with a sample dataset and instructions for testing the installation of IsoEM2 and IsoDE2 are also included below.
- README.TXT
- Git repository for IsoEM2/IsoDE2
- Galaxy wrappers for IsoEM2/IsoDE2
- SAMPLE-README.TXT
- IsoEM2IsoDE2-MAQC-Sample.zip
Bootstrap support calculator
NEW! Public Galaxy installation
A public Galaxy deployment of IsoDE2 is now available at
https://neo.engr.uconn.edu/tool_runner?tool_id=isoDE2
Contact Information
Acknowledgment and Disclaimer
This material is based upon work supported in part by the Agriculture and Food Research Initiative Competitive Grant No. 2011-67016-30331 from the USDA National Institute of Food and Agriculture and awards CCF-16119110, CCF-1618347, DBI-1564936, DBI-1564899, IIS-0916948, and IIS-0916401 from NSF, a Collaborative Research Grant from Life Technologies, and a Molecular Basis of Disease Fellowship from Georgia State University. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies.