Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada.
PLoS One. 2007 Aug 8;2(8):e718. doi: 10.1371/journal.pone.0000718.
The exploration of microarray data and data from other high-throughput projects for hypothesis generation has become a vital aspect of post-genomic research. For the non-bioinformatics specialist, however, many of the currently available tools provide overwhelming amounts of data that are presented in a non-intuitive way.
METHODOLOGY/PRINCIPAL FINDINGS: In order to facilitate the interpretation and analysis of microarray data and data from other large-scale data sets, we have developed a tool, which we have dubbed the electronic Fluorescent Pictograph - or eFP - Browser, available at http://www.bar.utoronto.ca/, for exploring microarray and other data for hypothesis generation. This eFP Browser engine paints data from large-scale data sets onto pictographic representations of the experimental samples used to generate the data sets. We give examples of using the tool to present Arabidopsis gene expression data from the AtGenExpress Consortium (Arabidopsis eFP Browser), data for subcellular localization of Arabidopsis proteins (Cell eFP Browser), and mouse tissue atlas microarray data (Mouse eFP Browser).
CONCLUSIONS/SIGNIFICANCE: The eFP Browser software is easily adaptable to microarray or other large-scale data sets from any organism and thus should prove useful to a wide community for visualizing and interpreting these data sets for hypothesis generation.
探索微阵列数据和其他高通量项目的数据以生成假设已经成为后基因组研究的一个重要方面。然而，对于非生物信息学专家来说，许多现有的工具提供了大量的数据，这些数据以非直观的方式呈现。
方法/主要发现：为了便于解释和分析微阵列数据和其他大规模数据集的数据，我们开发了一种工具，我们称之为电子荧光象形图 - 或 eFP - Browser，可在 http://www.bar.utoronto.ca/ 上访问，用于探索微阵列和其他用于生成假设的数据。这个 eFP Browser 引擎将来自大规模数据集的数据绘制到用于生成数据集的实验样本的象形图表示上。我们举了一些使用该工具的例子，展示了 Arabidopsis 基因表达数据来自 AtGenExpress 联盟（Arabidopsis eFP Browser）、Arabidopsis 蛋白亚细胞定位的数据（Cell eFP Browser）和小鼠组织图谱微阵列数据（Mouse eFP Browser）。
结论/意义：eFP Browser 软件易于适应任何生物体的微阵列或其他大规模数据集，因此应该对广大社区有用，用于可视化和解释这些数据集以生成假设。
