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

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.

Protein synthesis, the translation of protein from messenger RNA templates by ribosomes, is a fundamental biological process that is highly regulated in all cells and consumes a large proportion of their energy, and advances in biological informatics are needed to drive biological discovery from data measuring protein synthesis. Recently, ribosome profiling and other sequencing-based methods have revolutionised our understanding of protein synthesis and genome architecture across the tree of life. However, generation of new biological knowledge from ribosome profiling data is hindered by inconsistent and underdeveloped quantitative data analysis and visualization.

We will:
1. Develop a world-leading reliable, reproducible and rigorous open-source software package for ribosome profiling quantification, by refactoring our existing RiboViz pipeline following best practices for sustainable software and adding key features, in collaboration with the UK's leading research computing facility.
2. Develop statistical tools to quantify translation of open reading frames (ORFs) on RNA, including short ORFs, by explicit regression modeling of technical biases and sequence-based features such as codon contexts.
3. Understand the effect of cis-regulatory features on translation regulation by applying RiboViz to quantify the effect of upstream ORFs on translation in three landmark species, and to quantify per-codon elongation rates and their evolution across a phylogeny of fungal species, shared with online interactive data visualizations.

Our proposed research will provide a reliable, reproducible, and rigorous suite of tools to bridge the gap in analysis of ribosome profiling data, and will generate new knowledge on the sequence features regulating protein translation. These tools will accelerate research in all groups studying translation, and enable rational design of expression of synthetic genes in biotechnology by optimising their translation rates.

1) Academic Impact.
RiboViz is an open-source software pipeline for ribosome profiling analysis, so will accelerate discovery in all groups studying protein synthesis. The proposed work will focus on making RiboViz more reliable, robust, rigorous, and usable, as well as adding new features to better quantify translation. We will share online standardised ribosome profiling analyses from multiple organisms to make these accessible to all, including non-experts.
We will co-organize a 1-day training event on ribosome profiling data analysis immediately preceding the Translation UK conference in July 2020.

2) Economic impact via synthetic biology.
This research project will improve Synthetic Biology toolkits available to control protein output. The open-source tools we develop will give better quantitative predictions of how coding sequence predicts protein output in diverse organisms and conditions, enabling the design of more efficient and controllable synthetic genes. This is of clear commercial interest (see letters from Synpromics and ThermoFisher GeneArt). In the course of this grant, we will seek out industrial partnerships for future collaboration by attending the annual conferences attended by Synthetic Biology companies.

3) Societal impact via bioinformatics education.
The proposed project will provide unique research-linked impact through partnership with a large, successful bioinformatics education and public engagement project, 4273pi. Led from the University of Edinburgh's School of Biological Sciences, 4273pi is one of the largest bioinformatics-at-school projects in the world and has visited more than 50 secondary schools. The RA will contribute 5% FTE to 4273pi engagement, including teaching on schools visits, and adding new teaching material related to the proposed research on protein synthesis.

4) Wider academic impact via research computing education.
The project will also provide research-linked impact through The Carpentries, a voluntary organization that teaches foundational coding and data science skills to researchers worldwide. This project will contribute staff time to Carpentries workshops. The UK RA will assist Dr. Wallace as an instructor carpentries workshops, then train as a certified Carpentries instructor and lead a workshop in year 2. One workshop will target the BBSRC's EASTBIO doctoral training program, using teaching material on genomics related to the proposed bioinformatics research.