Antonov Alexey V, Dietmann Sabine, Wong Philip, Mewes Hans W
Helmholtz Zentrum München, Institute for Bioinformatics and Systems Biology, Neuherberg, Germany.
FEBS J. 2009 Apr;276(7):2084-94. doi: 10.1111/j.1742-4658.2009.06943.x.
High-throughput metabolomics is a dynamically developing technology that enables the mass separation of complex mixtures at very high resolution. Metabolic profiling has begun to be widely used in clinical research to study the molecular mechanisms of complex cell disorders. Similar to transcriptomics, which is capable of detecting genes at differential states, metabolomics is able to deliver a list of compounds differentially present between explored cell physiological conditions. The bioinformatics challenge lies in a statistically valid interpretation of the functional context for identified sets of metabolites. Here, we present TICL, a web tool for the automatic interpretation of lists of compounds. The major advance of TICL is that it not only provides a model of possible compound transformations related to the input list, but also implements a robust statistical framework to estimate the significance of the inferred model. The TICL web tool is freely accessible at http://mips.helmholtz-muenchen.de/proj/cmp.
高通量代谢组学是一项动态发展的技术,能够以非常高的分辨率对复杂混合物进行大规模分离。代谢谱分析已开始广泛应用于临床研究,以研究复杂细胞疾病的分子机制。与能够检测处于不同状态基因的转录组学类似,代谢组学能够提供在探索的细胞生理条件之间差异存在的化合物列表。生物信息学面临的挑战在于对已鉴定代谢物集的功能背景进行统计学上有效的解释。在此,我们展示了TICL,这是一种用于自动解释化合物列表的网络工具。TICL的主要进步在于它不仅提供了与输入列表相关的可能化合物转化模型,还实现了一个强大的统计框架来估计推断模型的显著性。TICL网络工具可通过http://mips.helmholtz-muenchen.de/proj/cmp免费访问。