Propagating annotations of molecular networks using in silico fragmentation.
Propagating annotations of molecular networks using in silico fragmentation.
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DOI：
10.1371/journal.pcbi.1006089
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发表时间：
2018-04
影响因子：
4.3
通讯作者：
Dorrestein PC
中科院分区：
文献类型：
--
作者：
da Silva RR;Wang M;Nothias LF;van der Hooft JJJ;Caraballo-Rodríguez AM;Fox E;Balunas MJ;Klassen JL;Lopes NP;Dorrestein PC
小分子的注释是非靶向质谱分析中最具挑战性和最重要的步骤之一，因为我们的大多数生物学解释依赖于结构注释。分子网络已经成为一种结构化的方式来组织和挖掘来自非靶向串联质谱（MS/MS）实验的数据，并已被广泛应用于传播注释。然而，传播是通过手动检查光谱网络中连接的MS/MS光谱来完成的，并且只有在参考库光谱可用时才可能。用于注释未知裂解质谱的替代方法之一是通过使用计算机模拟预测。计算机注释的挑战之一是预测候选列表中正确结构的不确定性。在这里，我们展示了分子网络如何通过传播结构注释来提高计算机预测的准确性，即使在光谱库中没有与MS/MS光谱匹配的情况下。这是通过使用分子网络拓扑结构和结构相似性创建重新排序的结构候选物的网络共识来实现的，以改进计算机注释。网络注释传播工具可通过GNPS网络平台https://gnps.ucsd.edu/ProteoSAFe/static/gnps-theoretical.jsp访问。对于基因组分析，人们普遍认为可以使用注释的参考序列基于序列相似性来假设基因的功能。一旦同源性假设已经基于参考注释做出，它允许人们根据功能建立假设，并最终理解潜在的生物学。相比之下，质谱（MS）可以检测许多分子，如离子，但我们通常不能将MS信号与分子联系起来。用于注释片段化分子数据的参考文库仅覆盖已知分子空间的一小部分。使用计算（在硅片上）碎片预测结构库提供了一个有前途的替代方案。使用这种计算机模拟方法的分子注释的弱点之一是它们目前单独注释分子。然而，基于光谱相似性的分子关系可用于增强从质谱法检测到的分子的注释推断的结构假设。我们介绍了一种名为“网络注释传播”的在线工具，该工具使用分子网络的组合，基于光谱相似性，我们从中推断分子相似性，以及计算机碎片，使科学界能够加强他们的MS注释。
The annotation of small molecules is one of the most challenging and important steps in untargeted mass spectrometry analysis, as most of our biological interpretations rely on structural annotations. Molecular networking has emerged as a structured way to organize and mine data from untargeted tandem mass spectrometry (MS/MS) experiments and has been widely applied to propagate annotations. However, propagation is done through manual inspection of MS/MS spectra connected in the spectral networks and is only possible when a reference library spectrum is available. One of the alternative approaches used to annotate an unknown fragmentation mass spectrum is through the use of in silico predictions. One of the challenges of in silico annotation is the uncertainty around the correct structure among the predicted candidate lists. Here we show how molecular networking can be used to improve the accuracy of in silico predictions through propagation of structural annotations, even when there is no match to a MS/MS spectrum in spectral libraries. This is accomplished through creating a network consensus of re-ranked structural candidates using the molecular network topology and structural similarity to improve in silico annotations. The Network Annotation Propagation (NAP) tool is accessible through the GNPS web-platform https://gnps.ucsd.edu/ProteoSAFe/static/gnps-theoretical.jsp. For genome analysis it is commonly accepted that one can hypothesize the function of genes based on sequence similarity, using annotated reference sequences. Once a homology hypothesis has been made based on reference annotations, it allows one to build hypothesis in terms of function and ultimately understand the underlying biology. In contrast, mass spectrometry (MS) can detect many molecules, as ions, yet we often cannot link a MS signal to a molecule. The reference libraries to annotate fragmented molecular data only cover a small portion of the known molecular space. The use of computational (in silico) fragmentation predictions from structural libraries offers a promising alternative. One of the weaknesses of the molecular annotation using such in silico approaches is that they currently annotate the molecules individually. However, molecular relationships, based on spectral similarity, can be used to enhance the structural hypothesis inferred from the annotation of molecules detected by mass spectrometry. We introduce an online tool called “Network Annotation Propagation” that uses a combination of molecular networks, based on spectral similarity, from which we infer molecular similarity, together with in silico fragmentation, to enable the scientific community to strengthen their MS annotations.
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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：
Hastings J;de Matos P;Dekker A;Ennis M;Harsha B;Kale N;Muthukrishnan V;Owen G;Turner S;Williams M;Steinbeck C
通讯作者：
Steinbeck C
DOI：
10.1073/pnas.1424409112
发表时间：
2015-04-28
影响因子：
11.1
作者：
Bouslimani, Amina;Porto, Carla;Dorrestein, Pieter C.
通讯作者：
Dorrestein, Pieter C.
影响因子：
5.1
作者：
Esposito, Melissa;Nim, Shweta;Litaudon, Marc
通讯作者：
Litaudon, Marc
影响因子：
14.9
作者：
Banerjee P;Erehman J;Gohlke BO;Wilhelm T;Preissner R;Dunkel M
通讯作者：
Dunkel M