Accurate transcript structure and abundance inference from RNA-Seq data is foundational for molecular discovery. Here we present TACO, a computational method to reconstruct a consensus transcriptome from multiple RNA-Seq datasets. TACO employs novel change-point detection to demarcate transcript start and end sites, leading to dramatically improved reconstruction accuracy compared to other tools in its class. The tool is available at http://tacorna.github.io and can be readily incorporated into

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... RNA-Seq analysis workflows. High-throughput RNA sequencing (RNA-Seq) has enabled a deep understanding of the transcriptome 1-3 . While efforts to annotate high fidelity gene models by manual and automated systems have relied primarily on low-throughput sequencing methods 4-6 , several studies using RNA-Seq have described an expansive transcriptome, suggesting that reference gene catalogs are far from complete 3, 7, 8 . This annotation gap has been widened further by Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: