Functional enrichment analyses and construction of functional similarity networks with high confidence function prediction by PFP.
Functional enrichment analyses and construction of functional similarity networks with high confidence function prediction by PFP.
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DOI：
10.1186/1471-2105-11-265
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发表时间：
2010-05-19
影响因子：
3
通讯作者：
Kihara D
中科院分区：
文献类型：
--
作者：
Hawkins T;Chitale M;Kihara D
一种新的生物学研究范式利用了产生大量高通量数据集的技术，包括基因组序列、蛋白质相互作用和基因表达。生物学家分析和解释这些数据的能力依赖于对所含蛋白质的功能注释，但即使在高度特征化的生物体中，许多蛋白质也可能缺乏必要的功能证据来推断它们的生物学相关性。在这里，我们将来自我们的自动预测系统PFP的高置信度函数预测应用于三个基因组序列，即大肠杆菌、酿酒酵母和恶性疟原虫(疟疾)。对于所有基因组，PFP使注释基因的数量增加到90%以上。利用功能标注覆盖面大的特点，引入了功能相似网络来表示蛋白质组的功能空间。为每个蛋白质组构建了四个不同的功能相似性网络，每个网络通过考虑单个基因本体论(GO)类别(即生物过程、细胞成分和分子功能)的相似性来构建，另一个网络通过考虑与funSim评分的总体相似性来构建。功能相似网络比蛋白质-蛋白质相互作用网络具有更高的模块化程度。此外，funSim Score网络不同于单一的Go-Score网络，表现出更高的聚集度指数值，因此具有更高的层次化倾向。此外，对蛋白质-蛋白质相互作用网络和基因组局部区域的功能分配进行了研究，发现了许多子网络或局部区域具有功能连贯蛋白质的情况。这些结果将有助于解释基因组中蛋白质和基因序列之间的相互作用。本文重点介绍了这两种分析的几个例子。分析表明，应用PFP的高置信度预测可以对研究人员解释当今正在产生的海量生物数据的能力产生重大影响。与蛋白质-蛋白质相互作用网络相比，新引入的三种生物的功能相似网络表现出不同的网络特性。
A new paradigm of biological investigation takes advantage of technologies that produce large high throughput datasets, including genome sequences, interactions of proteins, and gene expression. The ability of biologists to analyze and interpret such data relies on functional annotation of the included proteins, but even in highly characterized organisms many proteins can lack the functional evidence necessary to infer their biological relevance. Here we have applied high confidence function predictions from our automated prediction system, PFP, to three genome sequences, Escherichia coli, Saccharomyces cerevisiae, and Plasmodium falciparum (malaria). The number of annotated genes is increased by PFP to over 90% for all of the genomes. Using the large coverage of the function annotation, we introduced the functional similarity networks which represent the functional space of the proteomes. Four different functional similarity networks are constructed for each proteome, one each by considering similarity in a single Gene Ontology (GO) category, i.e. Biological Process, Cellular Component, and Molecular Function, and another one by considering overall similarity with the funSim score. The functional similarity networks are shown to have higher modularity than the protein-protein interaction network. Moreover, the funSim score network is distinct from the single GO-score networks by showing a higher clustering degree exponent value and thus has a higher tendency to be hierarchical. In addition, examining function assignments to the protein-protein interaction network and local regions of genomes has identified numerous cases where subnetworks or local regions have functionally coherent proteins. These results will help interpreting interactions of proteins and gene orders in a genome. Several examples of both analyses are highlighted. The analyses demonstrate that applying high confidence predictions from PFP can have a significant impact on a researchers' ability to interpret the immense biological data that are being generated today. The newly introduced functional similarity networks of the three organisms show different network properties as compared with the protein-protein interaction networks.
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影响因子：
14.9
作者：
Güldener U;Münsterkötter M;Oesterheld M;Pagel P;Ruepp A;Mewes HW;Stümpflen V
通讯作者：
Stümpflen V
影响因子：
12.3
作者：
Hart GT;Ramani AK;Marcotte EM
通讯作者：
Marcotte EM
影响因子：
14.9
作者：
Harris, MA;Clark, J;White, R
通讯作者：
White, R
影响因子：
8
作者：
Hawkins, Troy;Luban, Stanislav;Kihara, Daisuke
通讯作者：
Kihara, Daisuke
影响因子：
13.8
作者：
Dandekar, T;Snel, B;Bork, P
通讯作者：
Bork, P