Molecular Biology Research Unit, University of Namur - FUNDP, Namur, Belgium.
BMC Bioinformatics. 2010 Oct 22;11:528. doi: 10.1186/1471-2105-11-528.
Microarray experiments have become very popular in life science research. However, if such experiments are only considered independently, the possibilities for analysis and interpretation of many life science phenomena are reduced. The accumulation of publicly available data provides biomedical researchers with a valuable opportunity to either discover new phenomena or improve the interpretation and validation of other phenomena that partially understood or well known. This can only be achieved by intelligently exploiting this rich mine of information.
Considering that technologies like microarrays remain prohibitively expensive for researchers with limited means to order their own experimental chips, it would be beneficial to re-use previously published microarray data. For certain researchers interested in finding gene groups (requiring many replicates), there is a great need for tools to help them to select appropriate datasets for analysis. These tools may be effective, if and only if, they are able to re-use previously deposited experiments or to create new experiments not initially envisioned by the depositors. However, the generation of new experiments requires that all published microarray data be completely annotated, which is not currently the case. Thus, we propose the PathEx approach.
This paper presents PathEx, a human-focused web solution built around a two-component system: one database component, enriched with relevant biological information (expression array, omics data, literature) from different sources, and another component comprising sophisticated web interfaces that allow users to perform complex dataset building queries on the contents integrated into the PathEx database.
微阵列实验在生命科学研究中变得非常流行。然而，如果仅独立考虑此类实验，则会降低分析和解释许多生命科学现象的可能性。可公开获得的数据的积累为生物医学研究人员提供了一个宝贵的机会，可以发现新现象，或者改进对部分理解或熟知的其他现象的解释和验证。这只能通过智能利用这一丰富的信息矿来实现。
考虑到对于资金有限的研究人员来说，微阵列等技术仍然过于昂贵，无法自行订购实验芯片，因此重新使用以前发表的微阵列数据将是有益的。对于某些有兴趣寻找基因组（需要许多重复）的研究人员来说，非常需要工具来帮助他们选择合适的数据集进行分析。如果这些工具能够重新使用以前存放的实验，或者创建最初存放者没有设想过的新实验，那么这些工具可能会很有效。但是，生成新实验需要完全注释所有已发表的微阵列数据，而目前尚未做到这一点。因此，我们提出了 PathEx 方法。
本文介绍了 PathEx，这是一种以人为中心的 Web 解决方案，围绕着由两个组件系统构建而成：一个数据库组件，其中包含来自不同来源的相关生物学信息（表达阵列、组学数据、文献）；另一个组件由复杂的 Web 界面组成，允许用户对集成到 PathEx 数据库中的内容执行复杂的数据集构建查询。
