Annotation: A Computational Solution for Streamlining Metabolomics Analysis.
Annotation: A Computational Solution for Streamlining Metabolomics Analysis.
复制标题
DOI：
10.1021/acs.analchem.7b03929
复制
发表时间：
2018-01-02
影响因子：
7.4
通讯作者：
Siuzdak G
中科院分区：
文献类型：
--
作者：
Domingo-Almenara X;Montenegro-Burke JR;Benton HP;Siuzdak G
代谢物鉴定仍然被认为是液相色谱/质谱（LC/MS）非靶向代谢组学中的一个瓶颈。鉴别工作流程通常从通过峰拾取算法检测相关LC/MS峰开始，并基于准确的质量搜索检索推定的身份。然而，仅精确的质量搜索为代谢物鉴定提供的证据不足。出于这个原因，使用计算注释来揭示潜在的代谢物单一同位素质量，除了用串联质谱法确认之外，还改进了推定的鉴定。本文从计算和分析的角度研究了LC/MS数据，重点关注中性损失和源内碎片的发生，以了解计算注释方法的挑战。本文中，我们研究了用于计算注释的最新策略，包括：（i）峰分组或全扫描（MS 1）伪谱提取，即，聚类来自每种代谢物的所有质谱信号;（ii）使用离子加合和离子峰之间的质量距离进行注释;（iii）结合生物学知识，如生物转化或途径;（iv）串联MS数据;以及（v）代谢物保留时间校准，通常通过分子描述符预测实现。每一个这些策略的优点和缺陷进行了讨论，以及预期的未来趋势计算注释。
Metabolite identification is still considered an imposing bottleneck in liquid chromatography mass spectrometry (LC/MS) untargeted metabolomics. The identification workflow usually begins with detecting relevant LC/MS peaks via peak-picking algorithms and retrieving putative identities based on accurate mass searching. However, accurate mass search alone provides poor evidence for metabolite identification. For this reason, computational annotation is used to reveal the underlying metabolites monoisotopic masses, improving putative identification in addition to confirmation with tandem mass spectrometry. This review examines LC/MS data from a computational and analytical perspective, focusing on the occurrence of neutral losses and in-source fragments, to understand the challenges in computational annotation methodologies. Herein, we examine the state-of-the-art strategies for computational annotation including: (i) peak grouping or full scan (MS1) pseudo-spectra extraction, i.e., clustering all mass spectral signals stemming from each metabolite; (ii) annotation using ion adduction and mass distance among ion peaks; (iii) incorporation of biological knowledge such as biotransformations or pathways; (iv) tandem MS data; and (v) metabolite retention time calibration, usually achieved by prediction from molecular descriptors. Advantages and pitfalls of each of these strategies are discussed, as well as expected future trends in computational annotation.
登录
查看更多内容
影响因子：
8.6
作者：
Blaženović I;Kind T;Torbašinović H;Obrenović S;Mehta SS;Tsugawa H;Wermuth T;Schauer N;Jahn M;Biedendieck R;Jahn D;Fiehn O
通讯作者：
Fiehn O
影响因子：
14.9
作者：
Caspi R;Billington R;Ferrer L;Foerster H;Fulcher CA;Keseler IM;Kothari A;Krummenacker M;Latendresse M;Mueller LA;Ong Q;Paley S;Subhraveti P;Weaver DS;Karp PD
通讯作者：
Karp PD
影响因子：
7.4
作者：
Baran, Richard;Northen, Trent R.
通讯作者：
Northen, Trent R.
DOI：
10.1093/bioinformatics/btu370
发表时间：
2014-10
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Daly R;Rogers S;Wandy J;Jankevics A;Burgess KE;Breitling R
通讯作者：
Breitling R
影响因子：
14.9
作者：
Allen F;Pon A;Wilson M;Greiner R;Wishart D
通讯作者：
Wishart D