NextGenPartiGene: next generation transcriptome assembly annotation and exploitation toolkit

生物学家可以使用不断改进的工具来对自然界提出探索性问题。在过去的四十年里，发生了一项革命性的发展，那就是DNA测序的出现。我们现在有能力破译任何生物体的基因组序列（或“遗传蓝图”），并从中找出它们是如何工作的。大约五年前，随着DNA测序技术的引入，基因组学革命加快了步伐，提高了基因组测序的速度，并降低了成本，降低了很多很多倍。这些“下一代”技术突然使许多研究人员开始在他们的工作中使用基因组测序成为可能。然而，与任何新技术一样，新的解决方案也会带来新的问题。以基因组测序为例，这是一个“富人”的问题：研究人员现在可以在很短的时间内生成数百到数千倍于过去的数据，但他们没有计算机工具来处理和理解这些数据。测序成本的降低也意味着，许多现在能够负担得起使用这项技术的研究人员，不需要经过长期的计算机训练，就能成功地分析大量数据。我们建议开发一套易于使用的工具，我们称之为NextGenPartiGene，使用“下一代”计算框架，这将缓解这个问题。我们关注的问题是弄清楚生物体正在使用（或“表达”）什么基因，以及这些基因可能在做什么。通过对一个生物体（或生物体的一部分，如一片叶子或一种特定的组织类型）的表达基因进行采样，就有可能建立一个生物体运行的各种生化途径（它吃什么，产生什么废物）的详细图像，以及实验性干预如何改变这些途径。我们将使用此类项目的新兴模型来构建NextGenPartiGene工具包：该模型的理念是，大部分艰苦的工作由服务器计算机完成，在幕后运行聪明的程序，并且该服务器由客户端驱动，通过标准的web浏览器访问。通过构建这个客户端-服务器工具包，我们将能够指导拥有大量下一代测序数据的研究人员沿着最佳实践、经过验证的路径进行全面而富有成效的分析。这意味着他们将能够从他们的数据中提取最大的信息，并从他们的研究经费中提取最大的价值。我们将把NextGenPartiGene工具作为开放获取软件发布，这样其他人既可以自由地使用它，也可以自由地修改和改进它以满足他们的需要。

Biologists have access to ever improving toolkits with which to ask probing questions of the natural world. One revolutionary development that has taken place over the last forty years is the advent of DNA sequencing. We now have the ability to decipher the genome sequence (or 'genetic blueprint') of any organism, and from this work out how they tick. About five years ago, this genomics revolution stepped up a gear, with the introduction of DNA sequencing technologies that increased the rate of genome sequencing, and reduced the cost, many, many fold. These 'next generation' technologies have suddenly made it possible for many researchers to start using genome sequencing in their work. However, as with any new technology, new solutions bring new problems. In the case of genome sequencing it is a 'rich person's' problem: researchers now can generate hundreds to thousands of times as much data as they used to, in a small fraction of the time, but they do not have the computer tools to process and understand it. The reduced cost of sequencing also means that many researchers who now can afford to use this technology do not have the long training required in computing to successfully analyse the floods of data. We propose to develop a set of easy-to-use tools, which we call NextGenPartiGene, using 'next generation' computing frameworks, that will alleviate this problem. We are focussing on the problem of working out what genes an organism is using (or 'expressing'), and what it is that these genes are likely to be doing. By sampling only the expressed genes of an organism (or a part of an organism, such as a leaf or a particular tissue type) it is possible to build up a detailed picture of the kinds of biochemical pathways the organism is running (what it can eat and what wastes it produces), and how experimental interventions change these pathways. We will build the NextGenPartiGene toolkit using an emerging model for such projects: the idea that much of the hard work is done by a server computer, running clever programmes behind the scenes, and that this server is driven by a client, accessed through a standard web browser. By building this client-server toolkit, we will be able to guide researchers with vast amounts of next-generation sequencing data down the best-practice, tried-and-tested paths to full and fruitful analysis. This means they will be able to extract maximum information from their data, and maximum value from their research funding. We will release the NextGenPartiGene tools as open-access software, so that others are both free to use it, and free to modify and improve it to fit their needs.