Improved transcriptome quantification and reconstruction from RNA-Seq reads using partial annotations.

Q2 Medicine In Silico Biology Pub Date : 2011-01-01 DOI:10.3233/ISB-2012-0459
Serghei Mangul, Adrian Caciula, Olga Glebova, Ion Mandoiu, Alex Zelikovsky
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引用次数: 8

Abstract

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.

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利用部分注释改进RNA-Seq reads的转录组定量和重建。
本文解决了如何使用RNA-Seq数据进行转录组重建和定量的问题,以及在部分注释的基因组中发现新的转录本。我们提出了一种新的注释引导的转录组发现、重建和定量的通用框架,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了比较。我们的方法被称为发现和重建未注释转录本(DRUT),可用于增强现有的转录组组装器,如袖扣,以及准确估计转录本频率。对合成数据集的实证分析证实,经DRUT增强的袖扣具有较好的转录本重建和频率估计质量。
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来源期刊
In Silico Biology
In Silico Biology Computer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
期刊介绍: The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.
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