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小规模测序可实现核糖体图谱测序(Ribo-Seq)数据的质量评估:以拟南芥细胞培养为例。

Small-scale sequencing enables quality assessment of Ribo-Seq data: an example from Arabidopsis cell culture.

作者信息

Mahboubi Amir, Delhomme Nicolas, Häggström Sara, Hanson Johannes

机构信息

Department of Plant Physiology, Umeå Plant Science Centre, Umeå University, Umeå, Sweden.

Department of Forest Genetics and Plant Physiology, Umeå Plant Science Centre, Swedish University of Agricultural Sciences, Umeå, Sweden.

出版信息

Plant Methods. 2021 Aug 24;17(1):92. doi: 10.1186/s13007-021-00791-w.

Abstract

BACKGROUND

Translation is a tightly regulated process, controlling the rate of protein synthesis in cells. Ribosome sequencing (Ribo-Seq) is a recently developed tool for studying actively translated mRNA and can thus directly address translational regulation. Ribo-Seq libraries need to be sequenced to a great depth due to high contamination by rRNA and other contaminating nucleic acid fragments. Deep sequencing is expensive, and it generates large volumes of data, making data analysis complicated and time consuming.

METHODS AND RESULTS

Here we developed a platform for Ribo-Seq library construction and data analysis to enable rapid quality assessment of Ribo-Seq libraries with the help of a small-scale sequencer. Our data show that several qualitative features of a Ribo-Seq library, such as read length distribution, P-site distribution, reading frame and triplet periodicity, can be effectively evaluated using only the data generated by a benchtop sequencer with a very limited number of reads.

CONCLUSION

Our pipeline enables rapid evaluation of Ribo-Seq libraries, opening up possibilities for optimization of Ribo-Seq library construction from difficult samples, and leading to better decision making prior to more costly deep sequencing.

摘要

背景

翻译是一个受到严格调控的过程,控制着细胞中蛋白质合成的速率。核糖体测序(Ribo-Seq)是一种最近开发的用于研究活跃翻译的mRNA的工具,因此可以直接研究翻译调控。由于rRNA和其他污染性核酸片段的高污染率,Ribo-Seq文库需要进行深度测序。深度测序成本高昂,并且会产生大量数据,使得数据分析复杂且耗时。

方法与结果

在此,我们开发了一个用于Ribo-Seq文库构建和数据分析的平台,借助小型测序仪实现对Ribo-Seq文库的快速质量评估。我们的数据表明,仅使用台式测序仪生成的数量非常有限的读数所产生的数据,就可以有效评估Ribo-Seq文库的几个定性特征,如读长分布、P位点分布、阅读框和三联体周期性。

结论

我们的流程能够快速评估Ribo-Seq文库,为优化来自困难样本的Ribo-Seq文库构建开辟了可能性,并在进行成本更高的深度测序之前做出更好的决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a013/8386038/360229e6476d/13007_2021_791_Fig1_HTML.jpg

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