Data-mining pipelines for unbiased interpretation of large-scale microarray datasets in environmental research using non-model species

新的遗传技术已经导致整个生物科学的数据量大幅增加，包括自然环境中生物体的生物学。通常，数千个基因的活性水平是在多个实验中的多个重复中研究的。问题是如何将海量的数据转化为对生物学的理解。对于那些与了解自然环境相关的生物来说，这个问题由于它们不像果蝇或实验室老鼠这样的典型模型那样资源丰富而变得更加复杂，在这些模型中，对数千个基因功能的详细了解是由资源丰富的研究团体产生的。如果我们想要研究自然环境中生物体的遗传学，我们需要尝试以一种生物学上有意义的方式总结大量的数据，我们需要将我们对环境感兴趣的生物体与它们得到更好研究的模式生物体亲戚联系起来。在过去的几年里，完成这两件事的工具已经变得可用，我们能够将这些工具打包到一个“管道”中，使我们能够快速分析由nerc资助的三个项目产生的大量数据。将这些独立项目的数据分析结合起来，可以有效地利用资源，从而产生来自这些项目的更多同行评议论文。我们开发的方法也将帮助NERC社区的其他研究人员。

New genetic techniques have led to a massively increased volume of data across the biological sciences, including on the biology of organisms within the natural environment. Typically the level of activity of thousands of genes is studied in multiple replicates in multiple experiments. The problem is to turn this huge volume of data into biological understanding. For organisms that are relevant to understanding the natural environment, this problem is compounded by the fact that they are not as well resourced as the typical models like a fruitfly or a lab mouse, where a detailed understanding of the function of thousands of genes is generated by well-resourced research communities. If we want to study the genetics of organisms in the natural environment, we need to try to summarise the sheer volume of data in a biologically meaningful way, and we need to relate our organisms of environmental interest to their better-studied, model organism relatives. Within the last few years, the tools to do these two things have become available and we are in a position to wrap these tools into a 'pipeline' that will allow us to analyse rapidly the large amounts of data that have been generated by three NERC-funded projects. Combining the data analyses from these separate projects is an efficient use of resources that will lead to additional peer-reviewed papers from these projects. The methods that we develop will also help other researchers within the NERC community.