Predicting functional associations from metabolism using bi-partite network algorithms.
Predicting functional associations from metabolism using bi-partite network algorithms.
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DOI：
10.1186/1752-0509-4-95
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发表时间：
2010-07-14
影响因子：
--
通讯作者：
Bader JS
中科院分区：
文献类型：
--
作者：
Veeramani B;Bader JS
代谢重建包含有关代谢酶及其反应物和产物的详细信息。这些网络可用于推断代谢酶之间的功能关联。许多方法是基于两种酶共享的代谢物的数量，或两种酶之间的最短路径。代谢物共享可能会错过串联途径中非连续酶之间的关联，而最短路径算法对水和ATP等高度代谢物敏感，这些代谢物在功能相似性很小的酶之间建立了联系。我们提出了新的，快速的方法来推断代谢网络中的功能关联。一个本地的方法，度校正的泊松分数，是只基于由两个酶共享的代谢物，但使用已知的代谢物度分布。一个全球性的方法，基于图形扩散内核，预测酶之间的关联，不共享代谢物。这两种方法对高度代谢物均具有稳健性。它们在预测共享基因本体（GO）注释和预测实验观察到的合成致死遗传相互作用方面优于以前的方法。包括细胞区室信息改进了GO注释预测，但降低了合成致死相互作用预测。这些新方法的性能几乎与基于通量平衡分析的计算要求高的方法一样好。我们提出了快速，准确的方法来预测代谢网络的功能关联。通过鉴定其强代谢相关性被GO中的常规注释遗漏的酶来证明生物学意义，最常见的酶涉及相同代谢物的运输与合成或共享代谢物但被常规途径边界分开的其他酶对。更一般地说，这里描述的方法可能对分析具有长尾度分布和高度枢纽的其他类型的网络有价值。
Metabolic reconstructions contain detailed information about metabolic enzymes and their reactants and products. These networks can be used to infer functional associations between metabolic enzymes. Many methods are based on the number of metabolites shared by two enzymes, or the shortest path between two enzymes. Metabolite sharing can miss associations between non-consecutive enzymes in a serial pathway, and shortest-path algorithms are sensitive to high-degree metabolites such as water and ATP that create connections between enzymes with little functional similarity. We present new, fast methods to infer functional associations in metabolic networks. A local method, the degree-corrected Poisson score, is based only on the metabolites shared by two enzymes, but uses the known metabolite degree distribution. A global method, based on graph diffusion kernels, predicts associations between enzymes that do not share metabolites. Both methods are robust to high-degree metabolites. They out-perform previous methods in predicting shared Gene Ontology (GO) annotations and in predicting experimentally observed synthetic lethal genetic interactions. Including cellular compartment information improves GO annotation predictions but degrades synthetic lethal interaction prediction. These new methods perform nearly as well as computationally demanding methods based on flux balance analysis. We present fast, accurate methods to predict functional associations from metabolic networks. Biological significance is demonstrated by identifying enzymes whose strong metabolic correlations are missed by conventional annotations in GO, most often enzymes involved in transport vs. synthesis of the same metabolite or other enzyme pairs that share a metabolite but are separated by conventional pathway boundaries. More generally, the methods described here may be valuable for analyzing other types of networks with long-tailed degree distributions and high-degree hubs.
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影响因子：
14.8
作者：
Becker, Scott A.;Feist, Adam M.;Herrgard, Markus J.
通讯作者：
Herrgard, Markus J.
影响因子：
7
作者：
Qi, Yan;Suhail, Yasir;Bader, Joel S.
通讯作者：
Bader, Joel S.
影响因子：
3
作者：
Schellenberger J;Park JO;Conrad TM;Palsson BØ
通讯作者：
Palsson BØ
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
5.8
作者：
Tsuda, Koji;Noble, William Stafford
通讯作者：
Noble, William Stafford