MolNetEnhancer: enhanced molecular networks by integrating metabolome mining and annotation tools

Abstract

Metabolomics has started to embrace computational approaches for chemical interpretation of large data sets. Yet, metabolite annotation remains a key challenge. Recently, molecular networking and MS2LDA emerged as molecular mining tools that find molecular families and substructures in mass spectrometry fragmentation data. Moreover, in silico annotation tools obtain and rank candidate molecules for fragmentation spectra. Ideally, all structural information obtained and inferred from these computational tools could be combined to increase the resulting chemical insight one can obtain from a data set. However, integration is currently hampered as each tool has its own output format and efficient matching of data across these tools is lacking. Here, we introduce MolNetEnhancer, a workflow that combines the outputs from molecular networking, MS2LDA, in silico annotation tools (such as Network Annotation Propagation or DEREPLICATOR) and the automated chemical classification through ClassyFire to provide a more comprehensive chemical overview of metabolomics data whilst at the same time illuminating structural details for each fragmentation spectrum. We present examples from four plant and bacterial case studies and show how MolNetEnhancer enables the chemical annotation, visualization, and discovery of the subtle substructural diversity within molecular families. We conclude that MolNetEnhancer is a useful tool that greatly assists the metabolomics researcher in deciphering the metabolome through combination of multiple independent in silico pipelines.

Feature-based Molecular Networking in the GNPS Analysis Environment

“…The use of these tools with the MS 2 spectral summary file enables subsequent direct mapping of these annotations to the molecular networks produced by the feature-based molecular networking method. These tools include SIRIUS 22 , DEREPLICATOR, 39,40 NAP, 19 MS2LDA 20 , MolNetEnhancer 21 (see Supplementary Note 5), as well as other software such as MetWork 41 , CFM-ID 42 , MetGem 43 , MetFrag 44 .…”

Classes for the masses: Systematic classification of unknowns using fragmentation spectra

“…Furthermore, propagation of compound classes to the full subnetwork, as proposed in ref. 16 , can lead to partial or imprecise annotations. As an example, see the molecular subnetwork containing the compound daidzein ( Fig.…”

“…At present, three strategies for structural classification exist: a) Cluster compounds based on spectral similarity, then propagate compound class annotations from database search in a semiautomated manner [14][15][16] b) Search for the query compound in a spectral library 17,18 or a structure database 19,20 ; consider the top k hits for assigning compound classes. c) Use machine learning methods to directly predict compound classes from the MS/MS spectrum 19,21 .…”

Algorithmic Learning for Auto-deconvolution of GC-MS Data to Enable Molecular Networking within GNPS

“…The annotation is usually done by ranking potential matches according to a similarity measure (forward match, reverse match, and probability 42,43 ) and when possible, filtering by retention index then reporting the top match. Molecular networking can further guide the annotation at the family level by utilizing information from connected nodes ( Figure S5 ) rather than focusing on individual annotations 44 . The global network can be colored by metadata such as sample type (Figure 3c), derivatized vs. non-derivatized, instrument type or other metadata ( Figure S6 ) to reveal interpretable patterns.…”