title: MGMR: leveraging RNA-Seq population data to optimize expression estimation.
abstract: 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 model parameters, 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.
authors.affiliation: The Blavatnik School of Computer Science, Tel-Aviv University, Tel Aviv 69978, Israel.
authors.forename: Roye
authors.initials: R
authors.lastname: Rozov
authors.forename: Eran
authors.initials: E
authors.lastname: Halperin
authors.forename: Ron
authors.initials: R
authors.lastname: Shamir
mesh_terms: D020869:Gene Expression Profiling / Q000379:methods*; D005828:Genetics, Population / Q000379:methods*; D016678:Genome; D060148:HapMap Project; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D015233:Models, Statistical; D017423:Sequence Analysis, RNA