title: Flexible analysis of RNA-seq data using mixed effects models.
abstract: BACKGROUND
  Most methods for estimating differential expression from RNA-seq are based on statistics that compare normalized read counts between treatment classes. Unfortunately, reads are in general too short to be mapped unambiguously to features of interest, such as genes, isoforms or haplotype-specific isoforms. There are methods for estimating expression levels that account for this source of ambiguity. However, the uncertainty is not generally accounted for in downstream analysis of gene expression experiments. Moreover, at the individual transcript level, it can sometimes be too large to allow useful comparisons between treatment groups.
  
  
  RESULTS
  In this article we make two proposals that improve the power, specificity and versatility of expression analysis using RNA-seq data. First, we present a Bayesian method for model selection that accounts for read mapping ambiguities using random effects. This polytomous model selection approach can be used to identify many interesting patterns of gene expression and is not confined to detecting differential expression between two groups. For illustration, we use our method to detect imprinting, different types of regulatory divergence in cis and in trans and differential isoform usage, but many other applications are possible. Second, we present a novel collapsing algorithm for grouping transcripts into inferential units that exploits the posterior correlation between transcript expression levels. The aggregate expression levels of these units can be estimated with useful levels of uncertainty. Our algorithm can improve the precision of expression estimates when uncertainty is large with only a small reduction in biological resolution.
  
  
  METHODS
  We have implemented our software in the mmdiff and mmcollapse multithreaded C++ programs as part of the open-source MMSEQ package, available on https://github.com/eturro/mmseq.
authors.affiliation: Cancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge CB2 0RE, UK, Department of Haematology, University of Cambridge, NHS Blood and Transplant, Long Road, Cambridge CB2 0PT, UK and Department of Epidemiology, Biostatistics and Occupational Health, McGill University, 1020 Pine Avenue West, Montreal QC H3A 1A2, Canada.
authors.forename: Ernest
authors.initials: E
authors.lastname: Turro
authors.forename: William J
authors.initials: WJ
authors.lastname: Astle
authors.forename: Simon
authors.initials: S
authors.lastname: Tavaré
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D018392:Genomic Imprinting / Q000235:genetics*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D051379:Mice; D008810:Mice, Inbred C57BL; D015233:Models, Statistical; D020033:Protein Isoforms
chemical_list: D020033:Protein Isoforms