Exploration of the omics evidence landscape: adding qualitative labels to predicted protein-protein interactions.
Exploration of the omics evidence landscape: adding qualitative labels to predicted protein-protein interactions.
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DOI：
10.1186/gb-2007-8-9-r197
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发表时间：
2007
期刊：
影响因子：
12.3
通讯作者：
Huynen, Martijn A
中科院分区：
文献类型：
--
作者：
van Noort, Vera;Snel, Berend;Huynen, Martijn A
通过结合不同类型的大数据集，为蛋白质相互作用提供证据，对S.可以推断，直接实验验证预测的相互作用提供指导。在后基因组时代，各种功能基因组学、蛋白质组学和计算技术被发展来阐明蛋白质相互作用网络。虽然这些技术中的一些是特定于某种类型的交互，但大多数预测的是交互的混合。定性标记对于分子生物学家实验验证预测的相互作用是必不可少的。在单个蛋白质-蛋白质相互作用预测方法中，一些可以预测物理相互作用而不产生其他类型的相互作用。没有一种方法可以具体预测代谢相互作用。我们已经构建了一个“组学证据景观”，结合了所有来源的证据，从各种类型的组学数据的酿酒酵母蛋白质相互作用。我们探索这一证据景观，以确定只有代谢或只有物理相互作用的领域，使我们能够具体预测这些领域中新的相互作用的性质。我们将联合收割机的数据集结合起来，检查整个证据景观，而不仅仅是两个数据集中得分最高的蛋白质对，并找到特定的预测。证据景观形式的证据类型的组合允许推断定性标签并将其放置在S.啤酒。这些定性标签将有助于基因网络的生物学解释，并将指导预测的相互作用的实验验证。
By combining different types of large datasets that give evidence for protein-interactions, qualitative labels on the predicted protein interaction network of S. cerevisiae could be inferred, providing guidance towards direct experimental verification of the predicted interactions. In the post-genomic era various functional genomics, proteomics and computational techniques have been developed to elucidate the protein interaction network. While some of these techniques are specific for a certain type of interaction, most predict a mixture of interactions. Qualitative labels are essential for the molecular biologist to experimentally verify predicted interactions. Of the individual protein-protein interaction prediction methods, some can predict physical interactions without producing other types of interactions. None of the methods can specifically predict metabolic interactions. We have constructed an 'omics evidence landscape' that combines all sources of evidence for protein interactions from various types of omics data for Saccharomyces cerevisiae. We explore this evidence landscape to identify areas with either only metabolic or only physical interactions, allowing us to specifically predict the nature of new interactions in these areas. We combine the datasets in ways that examine the whole evidence landscape and not only the highest scoring protein pairs in both datasets and find specific predictions. The combination of evidence types in the form of the evidence landscape allows for qualitative labels to be inferred and placed on the predicted protein interaction network of S. cerevisiae. These qualitative labels will help in the biological interpretation of gene networks and will direct experimental verification of the predicted interactions.