An Annual Workshop on Automated Function Prediction

新DNA序列的超常增长继续为生物学家了解活细胞的基本特性和原理提供同样超常的机会。但它也提出了一个实质性的障碍，迫使实验学家改变他们的方法，在实验设计本身中包括一个主要的计算角色。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实验室甚至整个实验社区调查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，只有一小部分预测经过实验测试或验证。然而，这些预测被反复用于扩展，即为新测序的基因组创建注释，导致传递错误，限制了现有注释的价值，并严重限制了进展。通过进行功能预测来过滤实验空间的准确、可推广的方法现在刚刚开始作为一个激烈的研究领域得到发展。计算机科学和信息技术的研究需要通过软件工具的开发来实现对当代生物学的深刻理解和应用，这些软件工具可以准确地识别基因并预测其功能。这样的预测需要足够精确，从而减少实验空间，使计算方法能够过滤选项，使实验学家能够专注于最有可能有用的新基因类别。本次评估自动功能预测的年度会议将在互动环境中汇集最好的研究成果，以评估新工具的效用，并揭示基本算法的进步，这些进步将促进对许多新发现基因功能的快速、准确预测。

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.