mTim: Rapid and accurate transcript reconstruction from RNA-Seq data
mTim: Rapid and accurate transcript reconstruction from RNA-Seq data
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mTim：从 RNA-Seq 数据快速准确地重建转录本
DOI：
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
G. Raetsch
中科院分区：
文献类型：
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作者：
G. Zeller;Nico Goernitz;A. Kahles;Jonas Behr;Pramod Kaushik Mudrakarta;Soeren Sonnenburg;G. Raetsch
高通量cDNA测序(RNA-Seq)技术的最新进展使转录组研究发生了革命性的变化。RNA-Seq的一个主要动机是在核苷酸分辨率下绘制表达转录本的结构图。有了精确的转录本重建计算工具，这项技术也可能对基因组(重新)注释有用，这主要依赖于从头发现基因，其中基因结构主要是从基因组序列推断出来的。我们开发了一种基于区分训练的隐马尔可夫支持向量机的机器学习方法，称为基于边距的转录推理方法(MTIM)，用于从RNA-Seq阅读序列中重建转录。除了来自阅读比对的特征外，它还利用特征基因组序列，例如剪接位点周围的序列，来改进转录本预测。与广泛使用的转录组装方法袖扣相比，mTim推断出的转录本具有很高的准确性和相对较强的对准误差。
Recent advances in high-throughput cDNA sequencing (RNA-Seq) technology have revolutionized transcriptome studies. A major motivation for RNA-Seq is to map the structure of expressed transcripts at nucleotide resolution. With accurate computational tools for transcript reconstruction, this technology may also become useful for genome (re-)annotation, which has mostly relied on de novo gene finding where gene structures are primarily inferred from the genome sequence. We developed a machine-learning method, called mTim (margin-based transcript inference method) for transcript reconstruction from RNA-Seq read alignments that is based on discriminatively trained hidden Markov support vector machines. In addition to features derived from read alignments, it utilizes characteristic genomic sequences, e.g. around splice sites, to improve transcript predictions. mTim inferred transcripts that were highly accurate and relatively robust to alignment errors in comparison to those from Cufflinks, a widely used transcript assembly method.
DOI：
10.1093/bioinformatics/btt442
发表时间：
2013-10-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
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作者：
Behr J;Kahles A;Zhong Y;Sreedharan VT;Drewe P;Rätsch G
通讯作者：
Rätsch G
影响因子：
7
作者：
Marioni, John C.;Mason, Christopher E.;Gilad, Yoav
通讯作者：
Gilad, Yoav
影响因子：
4.3
作者：
Rätsch G;Sonnenburg S;Srinivasan J;Witte H;Müller KR;Sommer RJ;Schölkopf B
通讯作者：
Schölkopf B