Ribosome Profiling and Bioinformatics

核糖体分析（Ribosomeprofiling，Ribo-seq）是一种基于核糖体足迹的RNA测序技术，是分析翻译组动态的有效方法，尤其适用于全基因组开放阅读框架（ORF）的定位，包括小于70个氨基酸的小蛋白。在这个中心项目Z2中，我们一直在为SPP 2002提供Ribo-seq平台和相关的生物信息学支持，以识别和表征不同原核生物中的小蛋白。我们已经从原始的Ribo-seq协议中广泛地调整了不同原核生物的步骤，并成功地生成了E.大肠杆菌，空肠弯曲杆菌，以及其他六种细菌物种和两个古菌与SPP 2002的几个小组合作。这些数据集是这些生物体中sORF的第一次普查的基础。除了进一步优化Ribo-seq方案外，我们现在的目标是利用Ribo-seq在不同的环境、胁迫或感染相关条件下对sORF进行表达谱分析。我们还建立了C. jejuni，我们现在将适应SPP 2002的其他生物。这些先进的Ribo-seq方法可以揭示隐藏和嵌套的sORF，并增加ORF边界注释的置信度。Z2项目还与实验人员密切合作，建立了第一个用于原核Ribo-seq数据生物计算分析的高通量工作流程HRIBO。我们对可用的基于Ribo-seq的ORF预测工具进行了基准测试，并将两个性能最好的工具纳入工作流程。然后分析注释的和新预测的ORF的差异转录和翻译。使用新的Ribo-TIS/TTS数据，我们将取代使用的ORF预测工具，因为现有的工具无法处理这种类型的数据。因此，我们现在的目标是开发和整合基于机器学习的方法，用于从TIS/TTS分析中注释起始和终止密码子，并将这些信息用于改进ORF预测。一个重要的方面也是古细菌中的sORF预测，因为可用的工具对这些生物体表现不佳。我们的分析管道将在SPP内为这类数据建立一个通用标准。总的来说，对于Z2的第二阶段，我们的目标是1）进一步扩展和应用湿实验室和生物信息学Ribo-seq分析，用于SPP 2002中的其他生物体，2）通过进一步建立Ribo-seq方法来绘制翻译起始/终止位点来完善翻译组注释，以及3）使用Ribo-seq通过测量选定生长，胁迫或感染条件下的翻译来促进sORF的功能表征。最后，我们将继续提供实验和计算支持和培训，并确保所有方法将提供给SPP 2002的所有成员，具有高标准和可重复性。

Ribosome profiling (Ribo-seq) based on RNA-seq of ribosome footprints on mRNAs is a powerful method for analyzing translatome dynamics, and especially for genome-wide mapping of open reading frames (ORFs), including those of small proteins of less than 70 aa. In this central project Z2, we have been providing a platform for Ribo-seq and associated bioinformatics support for the SPP2002 to identify and characterize small proteins in diverse prokaryotes. We have extensively adapted steps from the original Ribo-seq protocol for diverse prokaryotes, and have successfully generated datasets for E. coli, Campylobacter jejuni, as well as six other bacterial species and two archaea in collaboration with several groups of the SPP2002. These datasets are the basis for the first census of sORFs in these organisms. Besides further optimizing Ribo-seq protocols, we now aim to employ Ribo-seq for expression profiling of sORFs under diverse environmental, stress, or infection-relevant conditions. We have also set up Ribo-seq based initiation and termination site profiling (Ribo-TIS/TTS) for C. jejuni, which we will now adapt to other organisms of the SPP2002. These advanced Ribo-seq approaches can reveal hidden and nested sORFs and increase the confidence in ORF boundary annotations. The Z2 project has also established the first high-throughput workflow for the biocomputational analysis of prokaryotic Ribo-seq data, HRIBO, in close collaboration with the experimentalists. We benchmarked the available Ribo-seq-based ORF prediction tools and included the two best performing ones into the workflow. The annotated and newly predicted ORFs are then analysed for both differential transcription and translation. With the new Ribo-TIS/TTS data, we will replace the used ORF prediction tools as the available ones cannot handle this type of data. Thus, we now aim to develop and integrate machine-learning-based approaches for the annotation of start and stop codons from TIS/TTS profiling into our pipeline and we will use this information to improve ORF prediction. An important aspect is also the sORF prediction in archaea as the available tools do not perform well for these organisms. Our analysis pipeline will foster a common standard within the SPP for this type of data. Overall, for the second period of Z2, we aim to 1) further extend and apply wet-lab and bioinformatic Ribo-seq analyses for additional organisms from the SPP2002, 2) refine translatome annotations by further establishing Ribo-seq methods to map translation start/stop sites, and 3) use Ribo-seq to facilitate functional characterization of sORFs by measuring translation under selected growth, stress, or infection conditions. Finally, we will continue to provide both experimental and computational support and training and to ensure that all methods will be available to all members of the SPP2002 with high standards and reproducibility.