Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia.
Institute of Protein Research, Russian Academy of Sciences, Pushchino, Russia.
Methods Mol Biol. 2021;2252:269-294. doi: 10.1007/978-1-0716-1150-0_13.
During translation, the rate of ribosome movement along mRNA varies. This leads to a non-uniform ribosome distribution along the transcript, depending on local mRNA sequence, structure, tRNA availability, and translation factor abundance, as well as the relationship between the overall rates of initiation, elongation, and termination. Stress, antibiotics, and genetic perturbations affecting composition and properties of translation machinery can alter the ribosome positional distribution dramatically. Here, we offer a computational protocol for analyzing positional distribution profiles using ribosome profiling (Ribo-Seq) data. The protocol uses papolarity, a new Python toolkit for the analysis of transcript-level short read coverage profiles. For a single sample, for each transcript papolarity allows for computing the classic polarity metric which, in the case of Ribo-Seq, reflects ribosome positional preferences. For comparison versus a control sample, papolarity estimates an improved metric, the relative linear regression slope of coverage along transcript length. This involves de-noising by profile segmentation with a Poisson model and aggregation of Ribo-Seq coverage within segments, thus achieving reliable estimates of the regression slope. The papolarity software and the associated protocol can be conveniently used for Ribo-Seq data analysis in the command-line Linux environment. Papolarity package is available through Python pip package manager. The source code is available at https://github.com/autosome-ru/papolarity .
在翻译过程中，核糖体沿 mRNA 的移动速度不同。这导致核糖体在转录本上的分布不均匀，具体取决于局部 mRNA 序列、结构、tRNA 的可用性和翻译因子的丰度，以及起始、延伸和终止的总体速率之间的关系。影响翻译机制组成和性质的应激、抗生素和遗传扰动可以显著改变核糖体的位置分布。在这里，我们提供了一种使用核糖体图谱（Ribo-Seq）数据分析位置分布谱的计算方案。该方案使用了 papolarity，这是一个用于分析转录本水平短读序列覆盖度谱的新 Python 工具包。对于单个样本，对于每个转录本，papolarity 允许计算经典的极性度量，在 Ribo-Seq 的情况下，该度量反映了核糖体的位置偏好。对于与对照样本的比较，papolarity 估计了一个改进的度量，即覆盖度相对于转录本长度的线性回归斜率的相对值。这涉及通过泊松模型对谱进行分段去噪，并在分段内聚合 Ribo-Seq 覆盖度，从而可靠地估计回归斜率。papolarity 软件及其相关协议可方便地用于命令行 Linux 环境中的 Ribo-Seq 数据分析。papolarity 包可通过 Python pip 包管理器获得。源代码可在 https://github.com/autosome-ru/papolarity 获得。
