Flexible analysis of transcriptome assemblies with Ballgown

A key advantage of RNA sequencing (RNA-seq) over hybridization-based technologies such as microarrays is that RNA-seq makes it possible to reconstruct complete gene structures, including multiple splice variants, from raw RNA-seq reads without relying on previouslyestablished annotations [20, 32, 9]. But with this added flexibility, there are increased computational demands on upstream processing tasks such as alignment and assembly [28]. There is also a large and active community of developers contributing to downstream statistical modeling through the Bioconductor project [7]. However, there has been a gap between upstream processing tools and downstream statistical modeling tools that made it difficult to analyze assembled transcriptomes using Bioconductor. This gap has prevented rigorous statistical analysis of eQTL, timecourse, continuous covariate, or confounded experimental designs and has led to considerable controversy in the analysis of population-level RNA-seq data [4]. We have developed software that bridges the gap between transcriptome assembly and fast, flexible differential expression analysis (Supplementary Figure 1). First, our tool called