Comparative methods for second generation sequence analysis

来自不止一个物种的生物数据必须在进化背景下进行分析，考虑到它们的进化历史，因为数据是非独立的。例如，在小鼠中发现的特征在大鼠中比在人类中发现的概率更高，因为小鼠与大鼠的共同祖先比人类更近。如果不考虑进化的历史，也就是所谓的遗传学，就会得到错误的结果。在进化的背景下分析数据被称为比较方法。 目前的DNA测序技术创建了非常大的数据集，无论是在物种数量还是数据类型方面。比较方法使用计算上复杂的数学模型来联合收割机将遗传学与感兴趣的数据结合，并且正在开发更复杂的数学模型。数据量的增加和模型的复杂性正在生物学家想要测试的想法与执行分析所需的计算能力之间产生差距。在台式计算机上进行一次分析可能需要数周甚至数月，这是目前生物研究中的限速步骤。超级计算机可以用来解决这些问题，但购买和运行成本昂贵，罕见，复杂，需要大量的技术知识才能使用。超级计算机还需要大量的电力来驱动和冷却它们。用于玩电脑游戏的硬件，在PC和游戏机中找到，有可能为这个问题提供解决方案。生成3D图像所需的巨大计算能力现在可以应用于解决其他问题。最近的一项研究分析了医疗数据，显示了一台价格为5300美元的带有多个显卡的个人电脑如何胜过一台460万美元的超级计算机。该项目旨在通过使用图形硬件大大加速比较方法分析。一个流行的比较方法包，贝叶斯特质，将被转换为使用一系列的图形硬件。虽然图形硬件具有大量的计算能力，但很难利用，因为它们的设计主要是为了执行非常不同的任务。这使得开发图形硬件程序比传统的计算机编程更复杂和耗时。将比较方法程序转换为使用图形硬件将使生物学家能够获得分析所产生的大量数据所需的有效计算机硬件和软件。允许生物学家探索大型数据集，回答复杂的问题，并开发对生物系统的新见解。它将消除与超级计算机相关的巨大技术障碍，并且具有成本效益，成本为数百或数千英镑，而不是数百万英镑。显卡所需的电量比传统计算机少1/20，更加环保。

Biological data, from more than once species, must be analysed in an evolutionary context, taking into account their evolutionary histories, as data is non-independent. For example, a trait found in mice will have a higher probability of being found in rats than humans, as mice share a more recent common ancestor with rats than humans. If the evolutionary histories, known as phylogenies, are not accounted for, an incorrect result can be found. Analysing data in an evolutionary context is called comparative methods. Current DNA sequencing technology creates very large data sets, both in terms of the number of species and types of data. Comparative methods use computationally complex mathematical models to combine the phylogeny with the data of interest, and more complex mathematical models are being developed. The increase in the volume of data and complexity of the models is creating a gap between the ideas biologists would like to test and the computational power needed to perform the analysis. A single analysis can take weeks or even months on a desktop computer, this is currently a rate limiting step in biological research. Supercomputers can be used to solve these issues but are expensive to buy and run, are rare, complex and require a large amount of technical knowledge to use. Supercomputers also require large amounts of electricity to power and cool them. The hardware used to play computer games, found in PC and games consoles, have the potential to offer a solution to this problem. The vast computing power needed to generate 3D images can now be applied to solve other problems. A recent study, analysing medical data, showed how a PC with a number of graphics cards, costing $5300, could outperform a $4.6 million supercomputer. This project aims to vastly accelerate comparative methods analysis by using graphics hardware. A popular comparative methods package, BayesTraits, will be converted to use a range of graphics hardware. While graphics hardware has a large amount of computing power, it can be hard to utilise as they are designed, primarily, to perform a very different task. This makes developing programs for graphics hardware more complex and time consuming than traditional computer programming. Converting comparative methods programs to use graphics hardware will give biologists access to effective computer hardware and software required to analyse the vast quantities of data being generated. Allowing biologist to explore large data sets, answer complex questions and develop new insights into biological systems. It will eliminate the large technical hurdle associated with supercomputers and is cost effective, costing hundreds or thousands of pounds instead of millions. Graphics cards require 1/20th less power than traditional computers, making them more environmentally friendly.