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

新的遗传技术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数据。通常，在多个实验中多次重复研究数千个基因的活性水平。问题是如何将这些庞大的数据转化为生物学知识。对于那些与理解自然环境相关的生物体来说，这个问题更加复杂，因为它们不像果蝇或实验室小鼠这样的典型模型那样资源丰富，在这些模型中，对数千个基因功能的详细理解是由资源丰富的研究社区产生的。如果我们想研究自然环境中生物的遗传学，我们需要尝试以生物学上有意义的方式总结大量数据，我们需要将我们的环境生物与它们更好地研究的模式生物亲属联系起来。在过去的几年里，完成这两件事的工具已经可用，我们能够将这些工具包装成一个“管道”，使我们能够快速分析由三个NERC资助的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是对资源的有效利用，这将导致这些项目产生更多的同行评审论文。我们开发的方法也将帮助NERC社区内的其他研究人员。

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.