Protein networks reveal detection bias and species consistency when analysed by information-theoretic methods.
Protein networks reveal detection bias and species consistency when analysed by information-theoretic methods.
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DOI：
10.1371/journal.pone.0012083
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发表时间：
2010-08-18
期刊：
影响因子：
3.7
通讯作者：
Fraternali F
中科院分区：
文献类型：
--
作者：
Fernandes LP;Annibale A;Kleinjung J;Coolen AC;Fraternali F
我们应用我们最近开发的信息论方法来描述和比较蛋白质-蛋白质相互作用网络。这些度量被用来通过宏观统计特性来量化拓扑网络特征。网络差异是基于这些宏观属性而不是微观重叠、同源信息或基序出现来评估的。我们给出了对蛋白质-蛋白质相互作用网络的大规模分析结果。在我们的分析中使用了精确的零模型，允许可靠地解释结果。通过量化实验数据的方法偏差，我们可以定义一个信息阈值，超过这个阈值，网络可以被认为包括一致的宏观拓扑属性，尽管它们在微观上有很小的重叠。基于这一原理，酵母双杂交方法的数据足够一致，可以进行种内比较(不同实验之间)和物种间比较，而亲和纯化质谱法的数据即使在种内比较中也显示出很大的差异。
We apply our recently developed information-theoretic measures for the characterisation and comparison of protein–protein interaction networks. These measures are used to quantify topological network features via macroscopic statistical properties. Network differences are assessed based on these macroscopic properties as opposed to microscopic overlap, homology information or motif occurrences. We present the results of a large–scale analysis of protein–protein interaction networks. Precise null models are used in our analyses, allowing for reliable interpretation of the results. By quantifying the methodological biases of the experimental data, we can define an information threshold above which networks may be deemed to comprise consistent macroscopic topological properties, despite their small microscopic overlaps. Based on this rationale, data from yeast–two–hybrid methods are sufficiently consistent to allow for intra–species comparisons (between different experiments) and inter–species comparisons, while data from affinity–purification mass–spectrometry methods show large differences even within intra–species comparisons.
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影响因子：
5.4
作者：
de Silva, Eric;Thorne, Thomas;Ingram, Piers;Agrafioti, Ino;Swire, Jonathan;Wiuf, Carsten;Stumpf, Michael P. H.
通讯作者：
Stumpf, Michael P. H.
通过亲和力纯化测定法识别蛋白质复合物的拓扑结构。
DOI：
10.1093/bioinformatics/btp353
发表时间：
2009-08-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Friedel CC;Zimmer R
通讯作者：
Zimmer R
影响因子：
12.3
作者：
Hart GT;Ramani AK;Marcotte EM
通讯作者：
Marcotte EM
影响因子：
9.8
作者：
Batada NN;Reguly T;Breitkreutz A;Boucher L;Breitkreutz BJ;Hurst LD;Tyers M
通讯作者：
Tyers M
影响因子：
64.8
作者：
Gavin, AC;Bösche, M;Superti-Furga, G
通讯作者：
Superti-Furga, G