Chen Shann-Ching, Zhao Ting, Gordon Geoffrey J, Murphy Robert F
Center for Bioimage Informatics, Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Bioinformatics. 2007 Jul 1;23(13):i66-71. doi: 10.1093/bioinformatics/btm206.
The yeast Saccharomyces cerevisiae is the first eukaryotic organism to have its genome completely sequenced. Since then, several large-scale analyses of the yeast genome have provided extensive functional annotations of individual genes and proteins. One fundamental property of a protein is its subcellular localization, which provides critical information about how this protein works in a cell. An important project therefore was the creation of the yeast GFP fusion localization database by the University of California, San Francisco, USA (UCSF). This database provides localization data for 75% of the proteins believed to be encoded by the yeast genome. These proteins were classified into 22 distinct subcellular location categories by visual examination. Based on our past success at building automated systems to classify subcellular location patterns in mammalian cells, we sought to create a similar system for yeast.
We developed computational methods to automatically analyze the images created by the UCSF yeast GFP fusion localization project. The system was trained to recognize the same location categories that were used in that study. We applied the system to 2640 images, and the system gave the same label as the previous assignments to 2139 images (81%). When only the highest confidence assignments were considered, 94.7% agreement was observed. Visual examination of the proteins for which the two approaches disagree suggests that at least some of the automated assignments may be more accurate. The automated method provides an objective, quantitative and repeatable assignment of protein locations that can be applied to new collections of yeast images (e.g. for different strains or the same strain under different conditions). It is also important to note that this performance could be achieved without requiring colocalization with any marker proteins.
The original images analyzed in this article are available at http://yeastgfp.ucsf.edu, and source code and results are available at http://murphylab.web.cmu.edu/software.
酿酒酵母是首个基因组被完全测序的真核生物。自那时起，对酵母基因组进行的多项大规模分析为各个基因和蛋白质提供了广泛的功能注释。蛋白质的一个基本特性是其亚细胞定位，这提供了有关该蛋白质在细胞中如何发挥作用的关键信息。因此，美国加利福尼亚大学旧金山分校（UCSF）开展了一个重要项目，即创建酵母绿色荧光蛋白（GFP）融合定位数据库。该数据库提供了据信由酵母基因组编码的75%的蛋白质的定位数据。通过目视检查，这些蛋白质被分类到22个不同的亚细胞定位类别中。基于我们过去在构建自动系统以对哺乳动物细胞中的亚细胞定位模式进行分类方面的成功经验，我们试图为酵母创建一个类似的系统。
我们开发了计算方法来自动分析由UCSF酵母GFP融合定位项目生成的图像。该系统经过训练，以识别该研究中使用的相同定位类别。我们将该系统应用于2640张图像，该系统对2139张图像（81%）给出了与先前分类相同的标签。当仅考虑最高置信度的分类时，观察到一致性为94.7%。对两种方法分类不一致的蛋白质进行目视检查表明，至少一些自动分类可能更准确。这种自动方法提供了一种客观、定量且可重复的蛋白质定位分类，可应用于新的酵母图像集（例如不同菌株或同一菌株在不同条件下的图像）。同样重要的是要注意，无需与任何标记蛋白共定位即可实现这种性能。
本文分析的原始图像可在http://yeastgfp.ucsf.edu获取，源代码和结果可在http://murphylab.web.cmu.edu/software获取。
