MGMR: Leveraging RNA-Seq Population Data to Optimize Expression Estimation

TitleMGMR: Leveraging RNA-Seq Population Data to Optimize Expression Estimation
Publication TypeConference Paper
Year of Publication2012
AuthorsRozov, R., Halperin E., & Shamir R.
Other Numbers3277

Background: RNA-Seq is a technique that uses Next Generation Sequencing to identify transcripts and estimate transcription levels. When applying this technique for quantification, one must contend with reads that align to multiple positions in the genome (multireads). Previous efforts to resolve multireads have shown that RNA-Seq expression estimation can be improved using probabilistic allocation of reads to genes. These methods use a probabilistic generative model for data generation and resolve ambiguity using likelihood-based approaches. In many instances, RNA-seq experiments are performed in the context of a population. The generative models of current methods do not take into account such population information, and it is an open question whether this information can improve quantification of the individual samples

Results: In order to explore the contribution of population level information in RNA-seq quantification, we apply a hierarchical probabilistic generative model, which assumes that expression levels of different individuals are sampled from a Dirichlet distribution with parameters specific to the population, and reads are sampled from the distribution of expression levels. We introduce an optimization procedure for the estimation of the modelparameters, and use HapMap data and simulated data to demonstrate that the model yields a significant improvement in the accuracy of expression levels of paralogous genes.

Conclusions: We provide a proof of principal of the benefit of drawing on population commonalities to estimate expression. The results of our experiments demonstrate this approach can be beneficial, primarily for estimation at the gene level.

Bibliographic Notes

Proceedings of the Second Annual Satellite Workshop on Massively Parallel Sequencing at the 16th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2012), Barcelona, Spain. Published in BMC Bioinformatics, Vol. 13, Suppl. 6:S2

Abbreviated Authors

R. Rozov, E. Halperin, and R. Shamir

ICSI Research Group


ICSI Publication Type

Article in conference proceedings