权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Comparative methods for second generation sequence analysis

第二代序列分析的比较方法

基本信息

批准号：
BB/K004344/1
负责人：
Andrew Meade
金额：
$ 12.95万
依托单位：
University of Reading
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2013
资助国家：
英国
起止时间：
2013 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FK004344%2F1
关键词：
Comparative methods second generation sequence

项目摘要

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.

来自不止一个物种的生物数据必须在进化的背景下进行分析，同时考虑到它们的进化史，因为数据是非独立的。例如，在老鼠身上发现的特征在老鼠身上发现的可能性比在人类身上发现的几率更高，因为老鼠与老鼠的共同祖先比人类更近。如果不考虑进化史，也就是所谓的进化史，就会发现错误的结果。在进化的背景下分析数据被称为比较方法。目前的DNA测序技术在物种数量和数据类型方面都创建了非常庞大的数据集。比较方法使用计算复杂的数学模型来将系统发育与感兴趣的数据结合起来，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和执行分析所需的计算能力之间造成差距。在台式计算机上进行一次分析可能需要几周甚至几个月的时间，这是目前生物学研究中的一个速度限制步骤。超级计算机可以用来解决这些问题，但购买和运行成本高昂，稀有、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为它们供电和冷却。PC和游戏机中用于玩电脑游戏的硬件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用于解决其他问题。最近的一项分析医学数据的研究表明，一台配备了多块显卡的个人电脑(售价5300美元)的性能如何超过一台价值460万美元的超级计算机。该项目旨在通过使用图形硬件来极大地加速比较方法分析。广受欢迎的比较方法包BayesTraits将被转换为使用一系列图形硬件。虽然图形硬件具有大量的计算能力，但可能很难利用，因为它们的设计主要是为了执行非常不同的任务。这使得为图形硬件开发程序比传统的计算机编程更加复杂和耗时。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海量数据所需的有效计算机硬件和软件。使生物学家能够探索大型数据集，回答复杂的问题，并对生物系统提出新的见解。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，耗资数百或数千英镑，而不是数百万英镑。显卡需要的电量比传统电脑少1/20，使它们更环保。