BBSRC-NSF/BIO RiboViz for reliable, reproducible and rigorous quantification of protein synthesis from ribosome profiling data

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This project is a UK-USA collaboration to develop a software suite, called RiboViz, to analyse and understand protein synthesis from "ribosome profiling" data. All cells make proteins by using molecular machines called ribosomes, which read a messenger RNA template and "translate" the RNA code into the protein code. Cells need to make the right proteins, at the right time, in the right quantities, and so this process is carefully controlled by signals that are also encoded in the RNA. These signals are complex and only just beginning to be understood because there are thousands of different RNA sequences in a cell and each is hundreds to thousands of nucleotides ("letters") long. Recent advances in DNA & RNA sequencing technology mean that we can now measure all parts of RNA that are translated into protein and how much by using a technique called ribosome profiling. Although this technique is amazing, it is not perfect, and statistical tools are needed to separate the interesting biological signals in the data from unwanted biases of the experimental measurement. These tools need to be implemented in usable and reliable software in order for all scientists studying studying protein synthesis to be able to get the maximum possible information from ribosome profiling data, which is expensive and time-consuming to collect.The RiboViz software suite, which is open source and free to use by anyone in the world, already takes raw data from sequencing machines and puts it through a series of processing steps. RiboViz estimates how much each part of RNA is translated, and how the amount of translation is controlled by the code of that RNA. RiboViz produces tables, figures and graphs that are accessible online, so is useful for both experts and non-experts. This kind of data sharing makes science more reproducible and more accessible.In the course of this project, we will work with a software engineer to make the RiboViz code more reliable, easy to use, and future-proof, and to add features that quantify protein synthesis more accurately. We will develop statistical models that take account of both biological signals and unwanted biases. We will apply these to understand some interesting features of how protein synthesis is regulated. The first is how production of a a short ("upstream") protein from an RNA can control production of another protein later ("downstream") on the same RNA. The second is to understand how synonymous parts of the RNA code affect how ribosomes move and how much protein they produce.This work will help build fundamental knowledge about how cells work, and has several applications. Companies who genetically engineer cells to express proteins, for example to make therapeutic drugs or artificial silk, will have better tools to engineer those cells to produce the right amount of protein at the right time. Scientists studying evolution will have better tools to understand how coding sequences evolve, allowing deeper understanding of the tree of life. Lastly, we will be able to better understand human genetic diseases caused by defects in protein synthesis, which in the long run could lead to better treatments.