Topic modeling for untargeted substructure exploration in metabolomics
Topic modeling for untargeted substructure exploration in metabolomics
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DOI：
10.1073/pnas.1608041113
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发表时间：
2016-11-29
影响因子：
11.1
通讯作者：
Rogers, Simon
中科院分区：
文献类型：
--
作者：
van der Hooft, Justin Johan Jozias;Wandy, Joe;Rogers, Simon
由于缺乏能够提取关键生物化学相关信息的计算工具，非靶向代谢组学回答生命科学中重要问题的潜力受到了阻碍。现有的工具专注于使用质谱仪碎片光谱来识别其行为表明它们与正在研究的系统相关的分子。不幸的是，碎裂光谱不能孤立地识别分子，但需要可靠的标准或已知碎裂分子的数据库。然而，碎裂光谱充满了与目前存在的生化过程有关的信息，其中大部分目前被忽略了。在这里，我们提出了一种分析工作流程，它利用给定实验的所有碎片数据以无监督的方式提取与生物化学相关的特征。我们证明了一种最初用于文本挖掘的算法-潜在狄利克雷分配-可以适用于处理代谢组学数据集。我们的方法从光谱中提取生物化学相关的分子亚结构(“Mass2Motif”)，作为一组共生的分子片段和中性损失。这种分析使我们能够分离分子子结构，它的存在使得分子可以根据共享子结构进行分组，而不考虑经典的光谱相似性。这些亚结构反过来又支持分子的假定从头结构注释。将这种光谱连通性与正交相关性(例如，系统扰动下常见的丰度变化)相结合，显著增强了我们为生物行为提供机械解释的能力。
The potential of untargeted metabolomics to answer important questions across the life sciences is hindered because of a paucity of computational tools that enable extraction of key biochemically relevant information. Available tools focus on using mass spectrometry fragmentation spectra to identify molecules whose behavior suggests they are relevant to the system under study. Unfortunately, fragmentation spectra cannot identify molecules in isolation but require authentic standards or databases of known fragmented molecules. Fragmentation spectra are, however, replete with information pertaining to the biochemical processes present, much of which is currently neglected. Here, we present an analytical workflow that exploits all fragmentation data from a given experiment to extract biochemically relevant features in an unsupervised manner. We demonstrate that an algorithm originally used for text mining, latent Dirichlet allocation, can be adapted to handle metabolomics datasets. Our approach extracts biochemically relevant molecular substructures ("Mass2Motifs") from spectra as sets of co-occurring molecular fragments and neutral losses. The analysis allows us to isolate molecular substructures, whose presence allows molecules to be grouped based on shared substructures regardless of classical spectral similarity. These substructures, in turn, support putative de novo structural annotation of molecules. Combining this spectral connectivity to orthogonal correlations (e.g., common abundance changes under system perturbation) significantly enhances our ability to provide mechanistic explanations for biological behavior.