Ribosome Profiling and Bioinformatics

基于mrna上核糖体足迹的RNA-seq的核糖体分析（Ribosome profiling, riboseq）是分析翻译体动力学的一种强大方法，尤其适用于开放阅读框（open reading frame, orf）的全基因组定位，包括那些小于70 aa的小蛋白。在这个中心项目Z2中，我们已经为SPP2002提供了一个平台，用于Ribo-seq和相关的生物信息学支持，以鉴定和表征各种原核生物中的小蛋白。我们已经对原始的核糖核酸序列方案进行了广泛的调整，用于多种原核生物，并与SPP2002的几个小组合作，成功地生成了大肠杆菌、空肠弯曲杆菌以及其他六种细菌和两种古细菌的数据集。这些数据集是对这些生物体中sorf进行首次普查的基础。除了进一步优化Ribo-seq协议，我们现在的目标是利用Ribo-seq分析sorf在不同环境、压力或感染相关条件下的表达谱。我们还建立了基于Ribo-seq的空肠C.起始和终止位点分析（Ribo-TIS/TTS），我们现在将适应SPP2002的其他生物。这些先进的Ribo-seq方法可以揭示隐藏的和嵌套的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，建立了首个用于原核核糖序列数据生物计算分析的高通量工作流程HRIBO。我们对现有的基于ribo -seq的ORF预测工具进行了基准测试，并将两个表现最好的工具纳入工作流程。然后对注释和新预测的orf进行差异转录和翻译分析。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集成基于机器学习的方法，将TIS/TTS分析中的开始和停止密码子注释到我们的管道中，我们将使用这些信息来改进ORF预测。一个重要的方面也是在古生菌sORF预测，因为现有的工具不表现良好的这些生物。我们的分析管道将在SPP中为这类数据建立一个通用标准。总体而言，在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的核糖核酸序列分析，用于SPP2002中的其他生物体；2)通过进一步建立核糖核酸序列方法来绘制翻译开始/停止位点，从而完善翻译组注释；3)通过测量在选定的生长、压力或感染条件下的翻译，利用核糖核酸序列来促进sorf的功能表征。最后，我们将继续提供实验和计算方面的支持和培训，并确保SPP2002的所有成员都能使用高标准和可重复性的所有方法。

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.