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TACO可从RNA测序中生成强大的多样本转录组组装。

TACO produces robust multisample transcriptome assemblies from RNA-seq.

作者信息

Niknafs Yashar S, Pandian Balaji, Iyer Hariharan K, Chinnaiyan Arul M, Iyer Matthew K

机构信息

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.

出版信息

Nat Methods. 2017 Jan;14(1):68-70. doi: 10.1038/nmeth.4078. Epub 2016 Nov 21.

Abstract

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.

摘要

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。

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