An Annual Workshop on Automated Function Prediction

新的DNA序列的异常增长继续为生物学家提供同样异常的机会来了解活细胞的基本特性和原理。 但它也提出了一个实质性的障碍，迫使实验主义者改变他们的方法，包括在实验设计本身的主要计算作用。特别是，在完整基因组的情况下，序列获取的速度已经超过了即使是最大的实验室，甚至是整个实验社区研究大量基因并证明其功能的能力，这意味着几乎所有关于基因功能的信息都是通过计算预测获得的，只有一小部分预测是通过实验测试或验证的。然而，预测被反复用于扩展，即，用于为新测序的基因组创建注释，导致传递性错误，限制了现存注释的价值，并严重限制了进展。通过进行函数预测来过滤实验空间的精确的、可推广的方法现在刚刚开始作为一个密集的研究领域被开发出来。 计算机科学和信息技术研究是必要的，以使当代生物学的深刻理解，以及通过软件工具的开发，可以准确地识别基因和预测其功能的应用。这样的预测需要足够准确，以减少实验空间，计算方法可以过滤选项，并允许实验人员专注于最有可能有用的新基因类别。 这个关于自动功能预测评估的年度会议将在互动环境中汇集最好的研究，以评估新工具的实用性，并揭示基本的算法进步，这些进步将促进快速，准确地预测许多新基因的功能。

The exceptional growth in new DNA sequences continues to provide equallyexceptional opportunities for biologists to understand fundamental propertiesand principles of living cells. But it also presents a substantive barrier, forcing experimentalists to change their approaches to include a major computational role in experimental design itself. In particular, the rate of sequence acquisition in the case of complete genomes already exceeds the capacity of even the largest experimental lab or even the entire experimental community to investigate a significant number of the genes and demonstrate their function, which has meant that almost all information about gene function is obtained through computational predictions, and only a small fraction of those predictions are tested or validated experimentally. Yet, the predictions are used repeatedly for extensions, i.e., for creating annotations for newly sequenced genomes, leading to transitive errors and limiting the value of extant annotations and severely limiting progress. Accurate, generalizable approaches to filter experimental space by making functional predictions is just now being starting to be developed as an area of intense research. Computer science and information technology research is required to enable a deep understanding of contemporary biology as well as applications through the development of software tools that accurately can identify genes and predict their function. Such predictions need to be accurate enough that the experimental space is reduced, that the computational methods serve to filter the options and allow experimentalists to focus on the categories of new genes most likely to be useful. This annual meeting on the assessment of automatic function prediction will bring together the best research in an interactive environment to assess the utility of new tools and reveal the basic algorithmic advances that will promote rapid, accurate prediction of function of the many new genes being discovered.