Bilateral BBSRC-NSF/BIO Collaborative Research: ABI Development: A Critcal Assessment of Protein Function Annotation

生物学家被大量的序列数据淹没，但从中获得的生物学信息相对较少。蛋白质功能的准确注释是理解生命的关键，但通过实验确定每种蛋白质的作用既昂贵又困难，而且无法扩大规模以适应现有的大量序列数据。因此，通过计算而不是实验手段来发现蛋白质的功能是最重要的。基因组序列数据来自数千个物种，这些数据与大量高通量实验数据相结合。总之，这些数据为计算函数预测创造了新的机遇和挑战。因此，世界各地的研究小组已经开发了许多计算注释方法，但它们的准确性和适用性还有待提高。自动功能预测特别兴趣小组（AFP-SIG）的使命是将计算生物学家、实验生物学家和生物馆长聚集在一起，他们正在处理预测蛋白质功能的重要问题，分享想法，创造合作。为了改进计算功能预测方法，建立了蛋白质功能注释关键评估算法（CAFA）作为一项正在进行的实验。CAFA的设计目的是对用于预测蛋白质功能的计算方法进行大规模评估。通过挑战全球数十个研究小组开发和提供他们最好的功能预测软件，参与AFP-SIG的研究人员将提高生物学家在分子水平上理解生命的能力。AFP-SIG的研究人员还将从果蝇、真菌和细菌中获得实验数据，作为测试参与CAFA的软件的基准，并对这些模式生物有更深入的了解。现在有可能收集综合描述复杂生物系统的许多不同状态的数据。使用这些数据应该可以理解和解释底层系统，但仍然存在重大挑战。主要的挑战之一是，随着研究人员从许多不同系统的许多不同生物体中收集到更多的数据，他们发现了更多不同的基因。为这些新发现的基因分配功能是解释高通量数据的关键一步。这导致了对研究人员近年来开发的功能预测方法的质量进行评估的迫切需要。自动功能预测特别兴趣小组（AFP-SIG）成立于2005年，其使命是将致力于解决基因功能预测这一关键挑战的生物信息学家和生物学家聚集在一起。除了分享想法和建立合作之外，AFP-SIG还创建了CAFA:（蛋白质）功能注释的关键评估。CAFA是一项社区驱动的挑战，旨在评估蛋白质功能预测软件的性能，自2010年以来已进行了两次。研究人员将提供以下成果：(1)将功能预测和功能预测方法评估的健壮开源软件纳入UniProt-GOA的高调注释管道；(2)通过生物信息学家、生物馆长和实验学家的参与，扩大AFP社区，从而提高功能预测方法的质量和相关性；(3)在果蝇、念珠菌和假单胞菌中大规模筛选目标功能项与基因的新关联；(4)在项目的最后两年里，扩大了中央美术学院的活动，包括从文献中精选的注释和我们自己的实验屏幕。AFP-SIG和CAFA的进展将从http://BioFunctionPrediction.org获得

Biologists are deluged with sequence data yet have derived comparatively little biological information from it. The accurate annotation of protein function is key to understanding life, but experimentally determining what each protein does is costly and difficult, and cannot scale up to accommodate the vast amount of sequence data already available. Therefore discovering protein protein function by computational, rather than experimental means, is of primary importance. Genomic sequence data are available from thousands of species, and those are coupled with massive high-throughput experimental data. Together, these data have created new opportunities as well as challenges for computational function prediction. As a result, many computational annotation methods have been developed by research groups worldwide, but their accuracy and applicability need to be improved upon. The mission of the Automated Function Prediction Special Interest Group (AFP-SIG) is to bring together computational biologists, experimental biologists and biocurators who are dealing with the important problem of predicting protein function, to share ideas, and create collaborations. To improve computational function prediction methods, the Critical Assessment of protein Function Annotation algorithms (CAFA) was established as an ongoing experiment. CAFA was designed to provide a large-scale assessment of computational methods dedicated to predicting protein function. By challenging dozens of research groups worldwide to develop and provide their best software for function prediction, the researchers involved in the AFP-SIG will improve the ability of biologists to understand life at the molecular level. The AFP-SIG researchers will also generate experimental data from fruit-flies, fungi and bacteria to be used as benchmarks to test the software participating in CAFA, and a deeper understanding of these model organisms. It is now possible to collect data that comprehensively profile many different states of complex biological systems. Using these data it should be possible to understand and explain the underlying systems, but significant challenges remain. One of the primary challenges is that, as researchers collect more data from many different organisms in many different systems, they discover more and different genes. Assigning functions to these newly discovered genes represents a key step towards interpretation of high-throughput data. This leads to a critical need to assess the quality of the function prediction methods that researchers have developed in recent years. The mission of the Automated Function Prediction Special Interest Group (AFP-SIG), founded in 2005, is to bring together bioinformaticians and biologists who are addressing this key challenge of gene function prediction. In addition to sharing ideas and creating collaboration, AFP-SIG has created CAFA: the Critical Assessment of (protein) Function Annotation. CAFA is a community-driven challenge to assess the performance of protein function prediction software, and it has been carried out twice since 2010. The investigators will provide the following outcomes: (1) robust open-source software to be used in function prediction and assessment of function prediction methods, incorporated into the high-profile annotation pipelines of UniProt-GOA; (2) expansion of the AFP community by engaging bioinformaticians, biocurators and experimentalists, thereby improving the quality and relevance of function prediction methods; (3) large-scale experimental screens in Drosophila, Candida and Pseudomonas for novel associations of targeted functional terms with genes; (4) an expanded CAFA event, incorporating both the curated annotations from the literature and our own experimental screens, in the last two years of the project. The progress of the AFP-SIG and CAFA will be available from http://BioFunctionPrediction.org