Mangul Serghei, Caciula Adrian, Glebova Olga, Mandoiu Ion, Zelikovsky Alex
Department of Computer Science, Georgia State University, Atlanta, GA, USA.
In Silico Biol. 2011;11(5-6):251-61. doi: 10.3233/ISB-2012-0459.
The paper addresses the problem of how to use RNA-Seq data for transcriptome reconstruction and quantification, as well as novel transcript discovery in partially annotated genomes. We present a novel annotation-guided general framework for transcriptome discovery, reconstruction and quantification in partially annotated genomes and compare it with existing annotation-guided and genome-guided transcriptome assembly methods. Our method, referred as Discovery and Reconstruction of Unannotated Transcripts (DRUT), can be used to enhance existing transcriptome assemblers, such as Cufflinks, as well as to accurately estimate the transcript frequencies. Empirical analysis on synthetic datasets confirms that Cufflinks enhanced by DRUT has superior quality of reconstruction and frequency estimation of transcripts.
本文探讨了如何利用RNA测序数据进行转录组重建和定量分析,以及在部分注释基因组中发现新转录本的问题。我们提出了一种用于在部分注释基因组中进行转录组发现、重建和定量分析的新型注释引导通用框架,并将其与现有的注释引导和基因组引导转录组组装方法进行比较。我们的方法称为未注释转录本的发现与重建(DRUT),可用于增强现有的转录组组装工具,如Cufflinks,以及准确估计转录本频率。对合成数据集的实证分析证实,由DRUT增强的Cufflinks在转录本重建和频率估计方面具有更高的质量。