Understanding recombination through tractable statistical analysis of whole genome sequences

这个项目涉及对整个基因组序列数据的分析：即一个生物体的完整DNA序列和遗传密码。获取这种序列的技术相对较新。在2003年完成的“人类基因组计划”中，它被用来对单个人类基因组进行测序。这项工程耗时13年，耗资27亿美元。然而，过去10年的技术进步导致基因组测序的成本大幅下降。现在给人类基因组测序只需要1000美元，而且这个价格还在继续下降。为什么要给基因组测序呢？人类基因组可以被认为是构建人类的蓝图。每个人的基因组略有不同：每个人共有的部分使我们成为人类；不同的部分导致我们之间的(基因)差异。通过研究基因组的共同部分和差异，了解我们的基因组，有望在许多领域取得科学突破，特别是医学领域。例如，英国基因组公司目前正计划对10万个基因组进行测序，目的是改善治疗罕见疾病、癌症和传染病的临床实践。DNA序列的研究并不局限于人类。从许多其他生物中获取全基因组序列也是有用的。该项目的重点是分析从细菌获得的遗传信息。研究细菌有很多原因，其中最明显的一个原因是，一些细菌会导致植物和牲畜的疾病(影响我们的食物供应)和人类的疾病(影响我们的健康)。更好地了解细菌的遗传密码，既可以跟踪感染的传播，也可以减少疾病的发生。然而，尽管序列数据相对容易获得，但从其中提取有用的信息可能非常困难。基因组序列可以在计算机上以长字母序列的形式存储在文本文件中。单个基因可能由几百或几千个字母组成。一个包含数千个基因的完整细菌基因组可能有300万个字母长。大多数科学项目都涉及研究一个群体(几十个、数百个或数千个)这样的基因组，因此，一个数据集由超过10亿个字母组成并不罕见！为了理解如此庞大复杂的数据集，需要以计算机程序实现的数学方法。本项目涉及这种数学方法的发展及其实现。该项目的目的是通过研究细菌的基因组序列来了解细菌的进化。通常情况下，细菌会进行克隆繁殖：每个个体都只有一个亲本。然而，在某些情况下，它们也可以交换DNA，方式与人类的有性繁殖有关。理解这种交换具有很大的科学意义，原因有几个，包括这是细菌对抗生素产生抗药性的主要方式之一。耐甲氧西林金黄色葡萄球菌是NHS非常关注的耐抗生素细菌菌株的一个例子。了解抗生素耐药性是如何获得的，是科学家帮助解决这些问题的一种方式。使用数学方法分析全基因组序列是这些研究的基础。目前有几个计算机程序可以用来研究DNA交换，并通过使用它们取得了重要发现。然而，当它们被用于整个基因组序列时，一些程序运行得太慢，在许多情况下没有用(有时它们需要几个月的时间)，而另一些程序无法检测到(或提供不完整的图像)遗传交换事件。这个项目正在开发新的程序，既要准确，又要运行得足够快，才能有用。

This project concerns the analysis of whole genome sequence data: that is, the complete DNA sequence, the genetic code, of an organism. The technology for acquiring such a sequence is relatively new. It was used to sequence a single human genome in the "Human Genome Project", which finished in 2003. This project took 13 years and cost $2.7 billion. However, technological advances in the past 10 years have led to the cost of sequencing genomes to drop dramatically. It now costs only $1,000 to sequence a human genome and this price continues to fall.Why should one wish to sequence a genome? The human genome can be thought of as the blueprint for building a human. Each person has a slightly different genome: the parts that are common to everyone are what make us human; the parts that differ are responsible for the (genetic) differences between us. Understanding our genomes, through studying both the common parts of the genome and the differences, promises scientific breakthroughs in many areas, particularly medicine. For example, Genomics England are currently planning to sequence 100,000 genomes for the purposes of improving clinical practice in dealing with rare disease, cancer and infectious disease.The study of DNA sequences is not restricted to humans. It is also useful to obtain whole genome sequences from many other living things. This project is focussed on analysing genetic information obtained from bacteria. There are many reasons for studying bacteria, one of the most obvious being that some bacteria cause disease in plants and livestock (affecting our food supply) and in humans (affecting our health). Obtaining a better understanding of the genetic code of bacteria offers the promise of both tracking the spread of infections and also reducing the occurrence of disease.However, although sequence data is relatively easy to obtain, extracting useful information from it can be very difficult. Genome sequences can be stored on computers in text files as a long sequence of letters. A single gene might consist of a few hundred or thousand letters. A whole bacterial genome, containing thousands of genes, might be 3 million letters long. Most scientific projects involve studying a population (tens, hundreds or thousands) of these genomes, thus it is not unusual for a dataset to consist of over a billion letters! To make sense of such a large complicated data set, mathematical methods, implemented as computer programs, are required.This project is concerned with the development of such mathematical methods, and their implementation. The aim of the project is to learn about the evolution of bacteria by studying their genome sequences. As a rule, bacteria reproduce clonally: each individual only has a single parent. However, in some cases they can also exchange DNA, in a manner related to sexual reproduction in humans. It is of great scientific interest to understand such exchanges for several reasons, including that it is one of the main ways in which a bacteria can become resistant to antibiotics. MRSA is an example of an antibiotic resistant bacterial strain that has been of significant concern to the NHS. Understanding how antibiotic resistance is acquired is one way in which scientists can help to tackle such problems. Analysing whole genome sequences using mathematical methods, as is done in this project, is fundamental to these investigations.Currently there are several computer programs that can be used to investigate the exchange of DNA, and important discoveries have been made through using them. However, when they are used on whole genome sequences, some of the programs run too slowly to be useful in many cases (sometimes they take months) and others cannot detect (or provide an incomplete picture) of genetic exchange events. This project is developing new programs that are both accurate, and run quickly enough to be useful.