BMC Bioinformatics. 2014;15 Suppl 9(Suppl 9):S3. doi: 10.1186/1471-2105-15-S9-S3. Epub 2014 Sep 10.
The advancement of RNA sequencing (RNA-seq) has provided an unprecedented opportunity to assess both the diversity and quantity of transcript isoforms in an mRNA transcriptome. In this paper, we revisit the computational problem of transcript reconstruction and quantification. Unlike existing methods which focus on how to explain the exons and splice variants detected by the reads with a set of isoforms, we aim at reconstructing transcripts by piecing the reads into individual effective transcript copies. Simultaneously, the quantity of each isoform is explicitly measured by the number of assembled effective copies, instead of estimated solely based on the collective read count. We have developed a novel method named Astroid that solves the problem of effective copy reconstruction on the basis of a flow network. The RNA-seq reads are represented as vertices in the flow network and are connected by weighted edges that evaluate the likelihood of two reads originating from the same effective copy. A maximum likelihood set of transcript copies is then reconstructed by solving a minimum-cost flow problem on the flow network. Simulation studies on the human transcriptome have demonstrated the superior sensitivity and specificity of Astroid in transcript reconstruction as well as improved accuracy in transcript quantification over several existing approaches. The application of Astroid on two real RNA-seq datasets has further demonstrated its accuracy through high correlation between the estimated isoform abundance and the qRT-PCR validations.
RNA 测序（RNA-seq）的发展为评估 mRNA 转录组中转录本异构体的多样性和数量提供了前所未有的机会。在本文中，我们重新审视了转录本重建和定量的计算问题。与现有的方法不同，我们的方法不是专注于如何用一组异构体来解释通过读取检测到的外显子和剪接变体，而是旨在通过将读取拼接成单个有效的转录本副本来重建转录本。同时，通过组装的有效副本数量来明确测量每种异构体的数量，而不仅仅是基于集体读取计数进行估计。我们开发了一种名为 Astroid 的新方法，该方法基于流网络解决有效副本重建问题。RNA-seq 读取表示为流网络中的顶点，并通过加权边连接，这些边评估两个读取是否来自同一有效副本的可能性。然后，通过在流网络上求解最小成本流问题，重建转录本副本的最大似然集。在人类转录组上的模拟研究表明，Astroid 在转录本重建方面具有更高的灵敏度和特异性，并且在转录本定量方面的准确性也优于几种现有方法。Astroid 在两个真实的 RNA-seq 数据集上的应用通过与 qRT-PCR 验证之间的高度相关性进一步证明了其准确性，表明了估计的异构体丰度。
