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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Strategies for Comparing Metabolic Profiles: Implications for the Inference of Biochemical Mechanisms from Metabolomics Data
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Strategies for Comparing Metabolic Profiles: Implications for the Inference of Biochemical Mechanisms from Metabolomics Data

机译：代谢谱比较策略：从代谢组学数据推断生化机制的意义

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Background: Large amounts of metabolomics data have been accumulated in recent years and await analysis. Previously, we had developed a systems biology approach to infer biochemical mechanisms underlying metabolic alterations observed in cancers and other diseases. The method utilized the typical Euclidean distance for comparing metabolic profiles. Here, we ask whether any of the numerous alternative metrics might serve this purpose better. Methods and Findings: We used enzymatic alterations in purine metabolism that were measured in human renal cell carcinoma to test various metrics with the goal of identifying the best metrics for discerning metabolic profiles of healthy and diseased individuals. The results showed that several metrics have similarly good performance, but that some are unsuited for comparisons of metabolic profiles. Furthermore, the results suggest that relative changes in metabolite levels, which reduce bias toward large metabolite concentrations, are better suited for comparisons of metabolic profiles than absolute changes. Finally, we demonstrate that a sequential search for enzymatic alterations, ranked by importance, is not always valid. Conclusions: We identified metrics that are appropriate for comparisons of metabolic profiles. In addition, we constructed strategic guidelines for the algorithmic identification of biochemical mechanisms from metabolomics data.
机译：背景：近年来已积累了大量代谢组学数据，等待分析。以前，我们已经开发出一种系统生物学方法来推断在癌症和其他疾病中观察到的代谢改变背后的生化机制。该方法利用典型的欧几里得距离来比较代谢谱。在这里，我们问众多替代指标中的任何一个是否可以更好地达到这一目的。方法和发现：我们使用嘌呤代谢中的酶促变化（在人肾细胞癌中进行了测量）来测试各种指标，目的是确定最佳指标以识别健康和患病个体的代谢状况。结果表明，几种指标具有相似的良好性能，但有些指标不适合比较代谢谱。此外，结果表明，与绝对变化相比，代谢物水平的相对变化减少了对大代谢物浓度的偏倚，更适合于比较代谢谱。最后，我们证明了按重要性排序的酶促改变的顺序搜索并不总是有效的。结论：我们确定了适合比较代谢谱的指标。此外，我们构建了从代谢组学数据对生物化学机制进行算法识别的战略指导方针。

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