Ribosome Profiling and Bioinformatics

基于mRNAs上核糖体足迹的RNA-SEQ的核糖体图谱(Ribo-seq)是分析翻译组学动态的一种有效方法，特别是对于开放阅读框架(ORF)的全基因组作图，包括那些小于70aa的小蛋白。在这个中心项目Z2中，我们一直在为Ribo-seq和相关的生物信息学支持SPP2002提供一个平台，以识别和表征不同原核生物中的小蛋白。我们已经广泛地将原始Ribo-Seq协议的步骤应用于不同的原核生物，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个细菌物种和两个古生菌的数据集。这些数据集是对这些生物体中sORF进行第一次普查的基础。除了进一步优化RIBO-SEQ协议，我们现在的目标是使用RIBO-SEQ在不同的环境、压力或感染相关条件下对sORF进行表达谱分析。我们还为空肠弯曲菌建立了基于Ribo-Seq的起始和终止位点分析(Ribo-TIS/TTS)，现在我们将适应SPP2002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，并增加对ORF边界注释的置信度。Z2项目还与实验者密切合作，建立了用于原核生物核糖核酸序列数据生物计算分析的第一个高通量工作流程--HRIBO。我们对可用的基于Ribo-seq的ORF预测工具进行了基准测试，并将性能最好的两个工具纳入了工作流程。然后对注释和新预测的ORF进行差异转录和翻译分析。对于新的Ribo-TIS/TTS数据，我们将取代使用过的ORF预测工具，因为现有的工具不能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，用于从TIS/TTS简档中注释开始和结束密码子，并将使用这些信息来改进ORF预测。一个重要的方面也是古生菌的sORF预测，因为现有的工具对这些生物体的预测效果并不好。我们的分析管道将在SPP内为这种类型的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RIBO-SEQ分析，以从SPP2002中获得更多的生物；2)通过进一步建立RIBO-SEQ方法来绘制翻译起始/停止位点图，从而精炼翻译组注释；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方面的支持和培训，并确保所有方法都将以高标准和可重现性提供给SPP2002的所有成员。

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.