大型蛋白质图中基于邻域的标记传播
Neighborhood-Based Label Propagation in Large Protein Graphs
摘要：
了解蛋白质的功能是在分子水平上理解生命的关键之一。它在包括人类疾病和药物发现在内的几种情况下也很重要。在这个快速和负担得起的生物测序的时代，在数据库中积累的序列的数量正在以增加的速率增加。这对生物学家和计算机科学家都提出了许多挑战。为了理解这一巨大的数据量，这些序列应该用功能属性进行注释。UniProtKB由两个部分组成：i）UniProtKB/Swiss-Prot数据库，包含由专家生物管理员手动审查的具有可靠信息的蛋白质序列; ii）UniProtKB/TrEMBL数据库，用于存储和处理未知序列。因此，对于所有的蛋白质，我们有可用的序列以及一些更多的信息，如分类单位和一些结构域。基于这些属性，可以在蛋白质上定义和计算成对相似性。其他重要的属性，虽然存在于Swiss-Prot中的蛋白质中，但在TrEMBL中的蛋白质中通常缺失，例如它们的功能和细胞定位。现在TrEMBL中大量的蛋白质序列需要快速的程序来自动注释它们。在这项工作中，我们提出了DistNBLP，一种新的分布式基于邻域的标签传播方法的大规模注释的蛋白质。为了做到这一点，使用在蛋白质数据库的图形表示上的标签传播，使用已审查蛋白质的功能注释来预测未审查蛋白质的功能注释。DistNBLP构建在“akka”工具包之上，用于构建弹性分布式消息驱动应用程序。
英文摘要：
Understanding protein function is one of the keys to understanding life at the molecular level. It is also important in several scenarios including human disease and drug discovery. In this age of rapid and affordable biological sequencing, the number of sequences accumulating in databases is rising with an increasing rate. This presents many challenges for biologists and computer scientists alike. In order to make sense of this huge quantity of data, these sequences should be annotated with functional properties. UniProtKB consists of two components: i) the UniProtKB/Swiss-Prot database containing protein sequences with reliable information manually reviewed by expert bio-curators and ii) the UniProtKB/TrEMBL database that is used for storing and processing the unknown sequences. Hence, for all proteins we have available the sequence along with few more information such as the taxon and some structural domains. Pairwise similarity can be defined and computed on proteins based on such attributes. Other important attributes, while present for proteins in Swiss-Prot, are often missing for proteins in TrEMBL, such as their function and cellular localization. The enormous number of protein sequences now in TrEMBL calls for rapid procedures to annotate them automatically. In this work, we present DistNBLP, a novel Distributed Neighborhood-Based Label Propagation approach for large-scale annotation of proteins. To do this, the functional annotations of reviewed proteins are used to predict those of non-reviewed proteins using label propagation on a graph representation of the protein database. DistNBLP is built on top of the "akka" toolkit for building resilient distributed message-driven applications.
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