scholarly journals Mass spectral similarity for untargeted metabolomics data analysis of complex mixtures

2015 ◽  
Vol 377 ◽  
pp. 719-727 ◽  
Author(s):  
Neha Garg ◽  
Clifford A. Kapono ◽  
Yan Wei Lim ◽  
Nobuhiro Koyama ◽  
Mark J.A. Vermeij ◽  
...  
Keyword(s):  
Data Analysis ◽  
Complex Mixtures ◽  
Untargeted Metabolomics ◽  
Metabolomics Data ◽  
Spectral Similarity ◽  
Mass Spectral

scholarly journals Metabolomics-Based Screening of Inborn Errors of Metabolism: Enhancing Clinical Application with a Robust Computational Pipeline

Metabolites ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 568
Author(s):  
Brechtje Hoegen ◽  
Alan Zammit ◽  
Albert Gerritsen ◽  
Udo F. H. Engelke ◽  
Steven Castelein ◽  
...  
Keyword(s):  
Data Analysis ◽  
Clinical Application ◽  
Inborn Errors Of Metabolism ◽  
Large Scale ◽  
Clinical Diagnostics ◽  
Untargeted Metabolomics ◽  
Computational Pipeline ◽  
Inborn Errors ◽  
Metabolomics Data ◽  
Metabolic Fingerprint

Inborn errors of metabolism (IEM) are inherited conditions caused by genetic defects in enzymes or cofactors. These defects result in a specific metabolic fingerprint in patient body fluids, showing accumulation of substrate or lack of an end-product of the defective enzymatic step. Untargeted metabolomics has evolved as a high throughput methodology offering a comprehensive readout of this metabolic fingerprint. This makes it a promising tool for diagnostic screening of IEM patients. However, the size and complexity of metabolomics data have posed a challenge in translating this avalanche of information into knowledge, particularly for clinical application. We have previously established next-generation metabolic screening (NGMS) as a metabolomics-based diagnostic tool for analyzing plasma of individual IEM-suspected patients. To fully exploit the clinical potential of NGMS, we present a computational pipeline to streamline the analysis of untargeted metabolomics data. This pipeline allows for time-efficient and reproducible data analysis, compatible with ISO:15189 accredited clinical diagnostics. The pipeline implements a combination of tools embedded in a workflow environment for large-scale clinical metabolomics data analysis. The accompanying graphical user interface aids end-users from a diagnostic laboratory for efficient data interpretation and reporting. We also demonstrate the application of this pipeline with a case study and discuss future prospects.

scholarly journals Using Expert Driven Machine Learning to Enhance Dynamic Metabolomics Data Analysis

Metabolites ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 54 ◽  
Author(s):  
Charlie Beirnaert ◽  
Laura Peeters ◽  
Pieter Meysman ◽  
Wout Bittremieux ◽  
Kenn Foubert ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Time Series Data ◽  
Expert Knowledge ◽  
Ground Truth ◽  
Simulated Dataset ◽  
Untargeted Metabolomics ◽  
Series Data ◽  
Metabolomics Data ◽  
Shiny App

Data analysis for metabolomics is undergoing rapid progress thanks to the proliferation of novel tools and the standardization of existing workflows. As untargeted metabolomics datasets and experiments continue to increase in size and complexity, standardized workflows are often not sufficiently sophisticated. In addition, the ground truth for untargeted metabolomics experiments is intrinsically unknown and the performance of tools is difficult to evaluate. Here, the problem of dynamic multi-class metabolomics experiments was investigated using a simulated dataset with a known ground truth. This simulated dataset was used to evaluate the performance of tinderesting, a new and intuitive tool based on gathering expert knowledge to be used in machine learning. The results were compared to EDGE, a statistical method for time series data. This paper presents three novel outcomes. The first is a way to simulate dynamic metabolomics data with a known ground truth based on ordinary differential equations. This method is made available through the MetaboLouise R package. Second, the EDGE tool, originally developed for genomics data analysis, is highly performant in analyzing dynamic case vs. control metabolomics data. Third, the tinderesting method is introduced to analyse more complex dynamic metabolomics experiments. This tool consists of a Shiny app for collecting expert knowledge, which in turn is used to train a machine learning model to emulate the decision process of the expert. This approach does not replace traditional data analysis workflows for metabolomics, but can provide additional information, improved performance or easier interpretation of results. The advantage is that the tool is agnostic to the complexity of the experiment, and thus is easier to use in advanced setups. All code for the presented analysis, MetaboLouise and tinderesting are freely available.

scholarly journals MS2DeepScore - a novel deep learning similarity measure for mass fragmentation spectrum comparisons

2021 ◽  
Author(s):  
Florian Huber ◽  
Sven van der Burg ◽  
Justin J.J. van der Hooft ◽  
Lars Ridder
Keyword(s):  
Mean Squared Error ◽  
Similarity Measures ◽  
Structural Similarity ◽  
Metabolomics Data ◽  
Spectral Similarity ◽  
Mass Spectral ◽  
Root Mean Squared Error ◽  
Prediction Uncertainty ◽  
Squared Error ◽  
Mass Fragmentation

Mass spectrometry data is one of the key sources of information in many workflows in medicine and across the life sciences. Mass fragmentation spectra are considered characteristic signatures of the chemical compound they originate from, yet the chemical structure itself usually cannot be easily deduced from the spectrum. Often, spectral similarity measures are used as a proxy for structural similarity but this approach is strongly limited by a generally poor correlation between both metrics. Here, we propose MS2DeepScore: a novel Siamese neural network to predict the structural similarity between two chemical structures solely based on their MS/MS fragmentation spectra. Using a cleaned dataset of >100,000 mass spectra of about 15,000 unique known compounds, MS2DeepScore learns to predict structural similarity scores for spectrum pairs with high accuracy. In addition, sampling different model varieties through Monte-Carlo Dropout is used to further improve the predictions and assess the model's prediction uncertainty. On 3,600 spectra of 500 unseen compounds, MS2DeepScore is able to identify highly-reliable structural matches and predicts Tanimoto scores with a root mean squared error of about 0.15. The prediction uncertainty estimate can be used to select a subset of predictions with a root mean squared error of about 0.1. We demonstrate that MS2DeepScore outperforms classical spectral similarity measures in retrieving chemically related compound pairs from large mass spectral datasets, thereby illustrating its potential for spectral library matching. Finally, MS2DeepScore can also be used to create chemically meaningful mass spectral embeddings that could be used to cluster large numbers of spectra. Added to the recently introduced unsupervised Spec2Vec metric, we believe that machine learning-supported mass spectral similarity metrics have great potential for a range of metabolomics data processing pipelines.

scholarly journals MS2DeepScore: a novel deep learning similarity measure to compare tandem mass spectra

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Florian Huber ◽  
Sven van der Burg ◽  
Justin J. J. van der Hooft ◽  
Lars Ridder
Keyword(s):  
Mass Spectra ◽  
Mean Squared Error ◽  
Similarity Measures ◽  
Structural Similarity ◽  
Metabolomics Data ◽  
Spectral Similarity ◽  
Mass Spectral ◽  
Root Mean Squared Error ◽  
Prediction Uncertainty ◽  
Squared Error

AbstractMass spectrometry data is one of the key sources of information in many workflows in medicine and across the life sciences. Mass fragmentation spectra are generally considered to be characteristic signatures of the chemical compound they originate from, yet the chemical structure itself usually cannot be easily deduced from the spectrum. Often, spectral similarity measures are used as a proxy for structural similarity but this approach is strongly limited by a generally poor correlation between both metrics. Here, we propose MS2DeepScore: a novel Siamese neural network to predict the structural similarity between two chemical structures solely based on their MS/MS fragmentation spectra. Using a cleaned dataset of > 100,000 mass spectra of about 15,000 unique known compounds, we trained MS2DeepScore to predict structural similarity scores for spectrum pairs with high accuracy. In addition, sampling different model varieties through Monte-Carlo Dropout is used to further improve the predictions and assess the model’s prediction uncertainty. On 3600 spectra of 500 unseen compounds, MS2DeepScore is able to identify highly-reliable structural matches and to predict Tanimoto scores for pairs of molecules based on their fragment spectra with a root mean squared error of about 0.15. Furthermore, the prediction uncertainty estimate can be used to select a subset of predictions with a root mean squared error of about 0.1. Furthermore, we demonstrate that MS2DeepScore outperforms classical spectral similarity measures in retrieving chemically related compound pairs from large mass spectral datasets, thereby illustrating its potential for spectral library matching. Finally, MS2DeepScore can also be used to create chemically meaningful mass spectral embeddings that could be used to cluster large numbers of spectra. Added to the recently introduced unsupervised Spec2Vec metric, we believe that machine learning-supported mass spectral similarity measures have great potential for a range of metabolomics data processing pipelines.

scholarly journals MetaboShiny – interactive processing, analysis and annotation of direct infusion metabolomics data

2019 ◽  
Author(s):  
Joanna C. Wolthuis ◽  
Stefania Magnusdottir ◽  
Mia Pras-Raves ◽  
Maryam Moshiri ◽  
Judith J.M. Jans ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Data Analysis ◽  
Lung Cancer Patient ◽  
Untargeted Metabolomics ◽  
Large Array ◽  
Direct Infusion ◽  
Rigorous Analysis ◽  
Metabolomics Data ◽  
Bioinformatics Study ◽  
Insight Into

AbstractDirect infusion untargeted metabolomics, as mass-over-charge values and intensity of ions, allows for rapid insight into a sample’s metabolic activity. However, analysis is often complicated by the large array of detected m/z values and the difficulty to prioritize important m/z and simultaneously annotate their putative identities. To address this challenge, we developed MetaboShiny, a novel R/RShiny-based metabolomics package featuring data analysis, database- and formula-prediction-based annotation and visualization. To demonstrate this, we reproduce and further explore a MetaboLights metabolomics bioinformatics study on lung cancer patient urine samples. MetaboShiny enables rapid and rigorous analysis and interpretation of direct infusion untargeted metabolomics data.

scholarly journals Repository scale classification and decomposition of tandem mass spectral data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mihir Mongia ◽  
Hosein Mohimani
Keyword(s):  
High Accuracy ◽  
Mass Spectral Data ◽  
Metabolomics Data ◽  
Mass Spectral ◽  
Molecular Features ◽  
Oriented Samples ◽  
Ingredient Composition ◽  
Single Phenotype ◽  
Tandem Mass Spectral Data ◽  
Scale Classification

AbstractVarious studies have shown associations between molecular features and phenotypes of biological samples. These studies, however, focus on a single phenotype per study and are not applicable to repository scale metabolomics data. Here we report MetSummarizer, a method for predicting (i) the biological phenotypes of environmental and host-oriented samples, and (ii) the raw ingredient composition of complex mixtures. We show that the aggregation of various metabolomic datasets can improve the accuracy of predictions. Since these datasets have been collected using different standards at various laboratories, in order to get unbiased results it is crucial to detect and discard standard-specific features during the classification step. We further report high accuracy in prediction of the raw ingredient composition of complex foods from the Global Foodomics Project.

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