Mix² RNA-Seq Data Analysis Software
- Precise transcript concentration estimates
- Accurate detection of differential expression
- Exceptional reproducibility across variable conditions
- Fast run-times and small memory footprint
Description
Mix² RNA-Seq Data Analysis Software
A software tool for the accurate estimation of RNA concentration from RNA-Seq data.
Model
Fragment bias in RNA-Seq poses a serious challenge to the accurate quantification of gene isoforms. Mix² makes no assumptions about coverage bias but fits for each gene isoform a mixture model to the data (Fig. 1). Mix² can therefore, for instance, accurately represent the 5’ bias, as shown in Fig. 1 (a and b), whereas Cufflinks is restricted to the uniform distribution (Fig. 1c).
The Mix² software yields accurate isoform quantification from RNA-Seq data
Implementation and run-time performance
The Mix² software runs as a 64-bit Linux command line tool. For an up-to-date list of supported distributions please refer to the User Guide of the Mix² software.
Mix² | Cufflinks w/o bias correction | Cufflinks with bias correction | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Dataset | Min | GB | Min | xRT | GB | xMEM | Min | xRT | GB | xMEM |
Avg(UHR) | 7 | 1.26 | 34 | 4.9 | 0.99 | 0.79 | 542 | 77.4 | 1.32 | 1.05 |
Avg(HBR) | 5 | 1.02 | 32 | 6.4 | 0.90 | 0.88 | 536 | 107.2 | 1.22 | 1.20 |
Table 1 | Memory usage and average run-time statistics on the MAQC UHR and HBR datasets. Min stands for run-time in minutes, GB for memory usage in gigabytes. xRT and xMEM are the factors by which run-time and memory usage increases, respectively, in comparison to Mix².
Featured Publications
Examples
Mix² was tested on the publicly available MicroArray Quality Control (MAQC) [1] and Association of Biomolecular Resource Facilities (ABRF) [2] datasets, containing RNA-Seq data from multiple sequencing facilities and library preparations which started with differently degraded RNA.
The higher accuracy of the concentration estimates of Mix² leads to better correlation between qPCR and FPKM fold-changes and consequently to higher accuracy in the detection of differential expression (Fig. 2).
FAQ
Frequently Asked Questions
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