Cao Mingshu, Fraser Karl, Rasmussen Susanne
AgResearch Grasslands Research Centre, Palmerston North 4442, New Zealand.
Metabolites. 2013 Oct 31;3(4):1036-50. doi: 10.3390/metabo3041036.
Mass spectrometry coupled with chromatography has become the major technical platform in metabolomics. Aided by peak detection algorithms, the detected signals are characterized by mass-over-charge ratio (m/z) and retention time. Chemical identities often remain elusive for the majority of the signals. Multi-stage mass spectrometry based on electrospray ionization (ESI) allows collision-induced dissociation (CID) fragmentation of selected precursor ions. These fragment ions can assist in structural inference for metabolites of low molecular weight. Computational investigations of fragmentation spectra have increasingly received attention in metabolomics and various public databases house such data. We have developed an R package "iontree" that can capture, store and analyze MS2 and MS3 mass spectral data from high throughput metabolomics experiments. The package includes functions for ion tree construction, an algorithm (distMS2) for MS2 spectral comparison, and tools for building platform-independent ion tree (MS2/MS3) libraries. We have demonstrated the utilization of the package for the systematic analysis and annotation of fragmentation spectra collected in various metabolomics platforms, including direct infusion mass spectrometry, and liquid chromatography coupled with either low resolution or high resolution mass spectrometry. Assisted by the developed computational tools, we have demonstrated that spectral trees can provide informative evidence complementary to retention time and accurate mass to aid with annotating unknown peaks. These experimental spectral trees once subjected to a quality control process, can be used for querying public MS2 databases or de novo interpretation. The putatively annotated spectral trees can be readily incorporated into reference libraries for routine identification of metabolites.
质谱联用色谱法已成为代谢组学中的主要技术平台。在峰检测算法的辅助下，检测到的信号通过质荷比（m/z）和保留时间进行表征。对于大多数信号，其化学身份往往难以确定。基于电喷雾电离（ESI）的多级质谱法允许对选定的前体离子进行碰撞诱导解离（CID）碎片化。这些碎片离子有助于推断低分子量代谢物的结构。碎片化谱的计算研究在代谢组学中越来越受到关注，各种公共数据库都存储了此类数据。我们开发了一个R包“iontree”，它可以捕获、存储和分析来自高通量代谢组学实验的MS2和MS3质谱数据。该包包括离子树构建函数、用于MS2谱比较的算法（distMS2）以及用于构建独立于平台的离子树（MS2/MS3）库的工具。我们已经证明了该包可用于系统分析和注释在各种代谢组学平台上收集的碎片化谱，包括直接进样质谱法以及与低分辨率或高分辨率质谱联用的液相色谱法。在开发的计算工具的辅助下，我们已经证明光谱树可以提供与保留时间和精确质量互补的信息证据，以帮助注释未知峰。这些实验光谱树一旦经过质量控制过程，就可用于查询公共MS2数据库或从头解释。推定注释的光谱树可以很容易地纳入参考库，用于代谢物的常规鉴定。
