Biology research has entered into the big data era. Systems biology approaches, therefore, have become essential tools to elucidate the whole landscape of how cells separate, grow, and resist different stresses. In 2009, a novel RNA technology, termed ribosome profiling, was invented by Dr. Jonathan Weissman Lab from UCSF. Ribosome profiling (Ribo-Seq) is a powerful tool which can provide the most direct readout of the intracellular translation state of a protein including information on the location of translation start/stop sites, ribosome distribution pattern, and even the moving rate of the translating ribosome, at the whole-genome scale and single-nucleotide resolution.
To date, many researchers including our lab have successfully applied ribosome profiling method for diverse purposes. We thus review in this chapter the underlying mechanism and recent advances as regards this fantastic tool. Firstly, we introduce the working mechanism, advantages, and study history of ribosome profiling. Secondly, we discuss the data analysis pipeline, also compare different statistical algorithms and data visualization software. Finally, we review the extensive applications of Ribo-seq, for example, identification of uORF, computation of global translation efficiency (TE), the study of the posttranscriptional regulatory role of RNA binding protein and others. We hope this chapter would be useful for interested systems biology researchers as well as RNA biologists.
Ribosome profiling Ribo-Seq RNA-Seq Translation Deep sequencing mRNA Ribosome footprint
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We apologize for not being able to cite many works owing to lack of space.
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