Niknafs Yashar S, Pandian Balaji, Iyer Hariharan K, Chinnaiyan Arul M, Iyer Matthew K
Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan, USA.
Department of Cellular and Molecular Biology, University of Michigan, Ann Arbor, Michigan, USA.
Nat Methods. 2017 Jan;14(1):68-70. doi: 10.1038/nmeth.4078. Epub 2016 Nov 21.
Accurate transcript structure and abundance inference from RNA sequencing (RNA-seq) data is foundational for molecular discovery. Here we present TACO, a computational method to reconstruct a consensus transcriptome from multiple RNA-seq data sets. TACO employs novel change-point detection to demarcate transcript start and end sites, leading to improved reconstruction accuracy compared with other tools in its class. The tool is available at http://tacorna.github.io and can be readily incorporated into RNA-seq analysis workflows.
从RNA测序(RNA-seq)数据中准确推断转录本结构和丰度是分子发现的基础。在此,我们展示了TACO,一种从多个RNA-seq数据集重建共有转录组的计算方法。TACO采用新颖的变化点检测来划定转录本的起始和结束位点,与同类其他工具相比,可提高重建准确性。该工具可在http://tacorna.github.io获取,并且可以很容易地整合到RNA-seq分析工作流程中。