Liu Tzu-Yu, Song Yun S
Department of Mathematics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA Department of Electrical Engineering and Computer Sciences.
Department of Mathematics and Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA Department of Electrical Engineering and Computer Sciences Department of Statistics and Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720, USA.
Bioinformatics. 2016 Jun 15;32(12):i183-i191. doi: 10.1093/bioinformatics/btw253.
Ribosome profiling is a useful technique for studying translational dynamics and quantifying protein synthesis. Applications of this technique have shown that ribosomes are not uniformly distributed along mRNA transcripts. Understanding how each transcript-specific distribution arises is important for unraveling the translation mechanism.
Here, we apply kernel smoothing to construct predictive features and build a sparse model to predict the shape of ribosome footprint profiles from transcript sequences alone. Our results on Saccharomyces cerevisiae data show that the marginal ribosome densities can be predicted with high accuracy. The proposed novel method has a wide range of applications, including inferring isoform-specific ribosome footprints, designing transcripts with fast translation speeds and discovering unknown modulation during translation.
A software package called riboShape is freely available at https://sourceforge.net/projects/riboshape
核糖体谱分析是研究翻译动力学和定量蛋白质合成的一项有用技术。该技术的应用表明,核糖体并非沿mRNA转录本均匀分布。了解每种转录本特异性分布是如何产生的,对于阐明翻译机制很重要。
在此,我们应用核平滑来构建预测特征,并构建一个稀疏模型,仅根据转录本序列预测核糖体足迹谱的形状。我们对酿酒酵母数据的结果表明,可以高精度预测边际核糖体密度。所提出的新方法有广泛应用,包括推断异构体特异性核糖体足迹、设计具有快速翻译速度的转录本以及发现翻译过程中未知的调控。
一个名为riboShape的软件包可在https://sourceforge.net/projects/riboshape上免费获取。