High throughput RNA sequencing (RNA-seq) promises to revolutionize our understanding of genes and their role in human disease by characterizing the RNA content of tissues and cells. The realization of this promise, however, is conditional on the development of effective computational methods for the identification and quantification of transcripts from incomplete and noisy data.
Now, a team led by researchers at University of Toronto, Canada have developed iReckon, a method for simultaneous determination of the isoforms and estimation of their abundances. Their probabilistic approach incorporates multiple biological and technical phenomena, including novel isoforms, intron retention, unspliced pre-mRNA, PCR amplification biases, and multi-mapped reads. iReckon utilizes regularized Expectation-Maximization to accurately estimate the abundances of known and novel isoforms.
The team’s results on simulated and real data demonstrate a superior ability to discover novel isoforms with a significantly reduced number of false positive predictions, and our abundance accuracy prediction outmatches that of other state-of-the-art tools. Furthermore they have applied iReckon to two cancer transcriptome datasets, a triple negative breast cancer patient sample and the MCF7 breast cancer cell line, and show that iReckon is able to reconstruct the complex splicing changes that were not previously identified. QT-PCR validations of the isoforms detected in the MCF7 cell line confirmed all of iReckon’s predictions and also showed strong agreement (r^2 = 0.94) with the predicted abundances.
iReckon is available both as a standalone package (open source) that can be downloaded from: http://compbio.cs.toronto.edu/ireckon and as a plugin for the Savant Genome Browser [Fiume et al., 2012, 2010], which enables running iReckon on individual genes in real-time.
- Mezlini AM, JM Smith EJM, Fiume M, Buske O, Savich G, Shah S, Aparicion S, Chiang D, Goldenberg A, Brudno M. (2012) iReckon: Simultaneous isoform discovery and abundance estimation from RNA-seq data. Genome Research [Epub ahead of print]. [abstract]