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

BBSRC-NSF/BIO 双边合作研究：ABI 开发：蛋白质功能注释的批判性评估

• 批准号: 1458477
• 负责人: Predrag Radivojac
• 金额: $ 28.22万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1458477&HistoricalAwards=false
• 关键词: Bilateral; BBSRC; NSF; BIO; Collaborative

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

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却相对较少。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功能既昂贵又困难，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发现蛋白质的功能是最重要的。数千个物种的基因组序列数据与大量高通量实验数据相结合。这些数据共同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开发了许多计算注释方法，但其准确性和适用性有待提高。自动功能预测特别兴趣小组 (AFP-SIG) 的使命是将正在处理预测蛋白质功能这一重要问题的计算生物学家、实验生物学家和生物管理者聚集在一起，分享想法并开展合作。为了改进计算功能预测方法，蛋白质功能注释算法的关键评估（CAFA）被建立为一项正在进行的实验。 CAFA 旨在对致力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 研究人员还将生成来自果蝇、真菌和细菌的实验数据，作为测试参与 CAFA 的软件的基准，并更深入地了解这些模式生物。现在可以收集全面描述复杂生物系统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。主要挑战之一是，随着研究人员从许多不同系统中的许多不同生物体收集更多数据，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。这导致迫切需要评估研究人员近年来开发的功能预测方法的质量。自动功能预测特别兴趣小组 (AFP-SIG) 成立于 2005 年，其使命是将正在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协作之外，AFP-SIG 还创建了 CAFA：（蛋白质）功能注释的批判性评估。 CAFA 是一项由社区驱动的挑战，旨在评估蛋白质功能预测软件的性能，自 2010 年以来已开展了两次。研究人员将提供以下成果：（1）用于功能预测和分析的强大开源软件功能预测方法的评估，纳入 UniProt-GOA 的高调注释流程中； (2) 通过生物信息学家、生物策展人和实验学家的参与来扩大 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进行大规模实验筛选，以寻找目标功能术语与基因的新关联； (4) 在该项目的最后两年中，扩大了 CAFA 活动，纳入了文献中精选的注释和我们自己的实验屏幕。 AFP-SIG 和 CAFA 的进展可从 http://BioFunctionPrediction.org 获取