Ribosome Profiling and Bioinformatics

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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.