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

这个项目是英国和美国合作开发的一个名为RiboViz的软件套件，用于从“核糖体分析”数据中分析和理解蛋白质合成。所有细胞都通过一种叫做核糖体的分子机器制造蛋白质，核糖体读取信使RNA模板并将RNA代码“翻译”成蛋白质代码。细胞需要在正确的时间以正确的数量制造正确的蛋白质，因此这个过程是由同样编码在RNA中的信号精心控制的。这些信号很复杂，只是刚刚开始被理解，因为细胞中有数千种不同的RNA序列，每个序列都有数百到数千个核苷酸（“字母”）长。DNA和RNA测序技术的最新进展意味着我们现在可以使用一种称为核糖体分析的技术来测量RNA翻译成蛋白质的所有部分以及翻译成蛋白质的数量。尽管这项技术令人惊叹，但它并不完美，需要统计工具来将数据中有趣的生物信号与实验测量的不必要偏差分开。这些工具需要在可用且可靠的软件中实现，以便所有研究蛋白质合成的科学家能够从核糖体分析数据中获得最大可能的信息，而核糖体分析数据的收集既昂贵又耗时。RiboViz软件套件是开源的，世界上任何人都可以免费使用，它已经从测序机中获取原始数据，并将其经过一系列处理步骤。RiboViz估计RNA的每个部分被翻译了多少，以及翻译的数量是如何被RNA的代码控制的。RiboViz生成的表格、数字和图表都可以在线访问，因此对专家和非专家都很有用。这种数据共享使科学更容易再现，更容易获得。在这个项目的过程中，我们将与一名软件工程师合作，使RiboViz代码更加可靠，易于使用，并且经得起未来的检验，并添加更准确地量化蛋白质合成的功能。我们将开发统计模型，同时考虑到生物信号和不必要的偏差。我们将运用这些来理解蛋白质合成是如何被调节的一些有趣的特征。首先，从RNA中产生的短蛋白（“上游”）如何控制随后在同一RNA上产生的另一种蛋白（“下游”）。第二是了解RNA编码的同义部分如何影响核糖体的移动以及它们产生多少蛋白质。这项工作将有助于建立关于细胞如何工作的基础知识，并有几个应用。那些通过基因工程改造细胞来表达蛋白质的公司，例如制造治疗药物或人造丝的公司，将有更好的工具来改造这些细胞，使其在适当的时间产生适量的蛋白质。研究进化的科学家将有更好的工具来理解编码序列是如何进化的，从而更深入地了解生命之树。最后，我们将能够更好地了解由蛋白质合成缺陷引起的人类遗传疾病，从长远来看，这可能会导致更好的治疗方法。

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.