scholarly journals Mining molecular structure databases: Identification of small molecules based on fragmentation mass spectrometry data

2016 ◽  
Vol 36 (5) ◽  
pp. 624-633 ◽  
Author(s):  
Franziska Hufsky ◽  
Sebastian Böcker
Keyword(s):  
Molecular Structure ◽  
Mass Spectrometry ◽  
Small Molecules ◽  
Mass Spectrometry Data ◽  
Structure Databases

scholarly journals Dereplikace látek a de novo charakterizace malých molekul z hmotnostních spekter

2022 ◽  
Vol 116 (1) ◽  
pp. 11-19
Author(s):  
Jiří Novák ◽  
Vladimír Havlíček
Keyword(s):  
Mass Spectrometry ◽  
Small Molecules ◽  
Mass Spectra ◽  
De Novo ◽  
Imaging Mass Spectrometry ◽  
Mass Spectrometry Data ◽  
Data Sets ◽  
Liquid Chromatography Mass Spectrometry ◽  
Imaging Mass Spectrometry Data

We describe the molecular dereplication principles and de novo characterization of small molecules obtained from liquid-chromatography mass spectrometry and imaging mass spectrometry data sets. Our methodology aims at supporting chemists and computer programmers to understand the hidden computing algorithms used for metabolomics mass spectrometry data processing. The approaches have been made available in the open-source tool CycloBranch. The presented tutorial extends the interpretation of mass spectra portfolia described in a series of papers published in Chemicke Listy, issues 2/2020 and 3/2020.

scholarly journals Reactomics: using mass spectrometry as a reaction detector

2019 ◽  
Author(s):  
Miao Yu ◽  
Lauren Petrick
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
Small Molecules ◽  
High Resolution Mass Spectrometry ◽  
A Priori ◽  
Mass Spectrometry Data ◽  
Biological Impact ◽  
Reaction Discovery ◽  
Reaction Detector ◽  
Biological Fate

AbstractUntargeted metabolomics analysis captures chemical reactions among small molecules. Common mass spectrometry-based metabolomics workflows first identify the small molecules significantly associated with the outcome of interest, then begin exploring their biochemical relationships to understand biological fate (environmental studies) or biological impact (physiological response). We suggest an alternative by which biochemical relationships can be directly retrieved through untargeted high-resolution paired mass distance (PMD) analysis without a priori knowledge of the identities of participating compounds. Retrieval is done using high resolution mass spectrometry as a chemical reaction detector, where PMDs calculated from the mass spectrometry data are linked to biochemical reactions obtained via data mining of small molecule and reaction databases, i.e. ‘Reactomics’. We demonstrate applications of reactomics including PMD network analysis, source appointment of unknown compounds, and biomarker reaction discovery as a complement to compound discovery analyses used in traditional untargeted workflows. An R implementation of reactomics analysis and the reaction/PMD databases is available as the pmd package (https://yufree.github.io/pmd/).

scholarly journals Chemically-informed Analyses of Metabolomics Mass Spectrometry Data with Qemistree

2020 ◽  
Author(s):  
Anupriya Tripathi ◽  
Yoshiki Vázquez-Baeza ◽  
Julia M. Gauglitz ◽  
Mingxun Wang ◽  
Kai Dührkop ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Chemical Composition ◽  
Small Molecules ◽  
Dna Sequences ◽  
Hierarchical Organization ◽  
Data Exploration ◽  
Mass Spectrometry Data ◽  
Molecular Fingerprints ◽  
Molecular Relationships ◽  
Chemical Ontologies

AbstractUntargeted mass spectrometry is employed to detect small molecules in complex biospecimens, generating data that are difficult to interpret. We developed Qemistree, a data exploration strategy based on hierarchical organization of molecular fingerprints predicted from fragmentation spectra, represented in the context of sample metadata and chemical ontologies. By expressing molecular relationships as a tree, we can apply ecological tools, designed around the relatedness of DNA sequences, to study chemical composition.

scholarly journals A Metabolome- and Metagenome-Wide Association Network Reveals Microbial Natural Products and Microbial Biotransformation Products from the Human Microbiota

mSystems ◽  
2019 ◽  
Vol 4 (4) ◽  
Author(s):  
Liu Cao ◽  
Egor Shcherbin ◽  
Hosein Mohimani
Keyword(s):  
Mass Spectrometry ◽  
Natural Products ◽  
Tandem Mass Spectrometry ◽  
Small Molecules ◽  
Human Microbiome ◽  
Mass Spectrometry Data ◽  
Tandem Mass ◽  
Microbial Species ◽  
Molecular Features ◽  
Microbial Natural Products

ABSTRACT The human microbiome consists of thousands of different microbial species, and tens of thousands of bioactive small molecules are associated with them. These associated molecules include the biosynthetic products of microbiota and the products of microbial transformation of host molecules, dietary components, and pharmaceuticals. The existing methods for characterization of these small molecules are currently time consuming and expensive, and they are limited to the cultivable bacteria. Here, we propose a method for detecting microbiota-associated small molecules based on the patterns of cooccurrence of molecular and microbial features across multiple microbiomes. We further map each molecule to the clade in a phylogenetic tree that is responsible for its production/transformation. We applied our proposed method to the tandem mass spectrometry and metagenomics data sets collected by the American Gut Project and to microbiome isolates from cystic fibrosis patients and discovered the genes in the human microbiome responsible for the production of corynomycolenic acid, which serves as a ligand for human T cells and induces a specific immune response against infection. Moreover, our method correctly associated pseudomonas quinolone signals, tyrvalin, and phevalin with their known biosynthetic gene clusters. IMPORTANCE Experimental advances have enabled the acquisition of tandem mass spectrometry and metagenomics sequencing data from tens of thousands of environmental/host-oriented microbial communities. Each of these communities contains hundreds of microbial features (corresponding to microbial species) and thousands of molecular features (corresponding to microbial natural products). However, with the current technology, it is very difficult to identify the microbial species responsible for the production/biotransformation of each molecular feature. Here, we develop association networks, a new approach for identifying the microbial producer/biotransformer of natural products through cooccurrence analysis of metagenomics and mass spectrometry data collected on multiple microbiomes.

scholarly journals Untargeted high-resolution paired mass distance data mining for retrieving general chemical relationships

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Miao Yu ◽  
Lauren Petrick
Keyword(s):  
Mass Spectrometry ◽  
Data Mining ◽  
High Resolution ◽  
Small Molecules ◽  
Chemical Reactions ◽  
A Priori ◽  
Mass Spectrometry Data ◽  
Reaction Discovery ◽  
Biological Fate ◽  
General Chemical

Abstract Untargeted metabolomics analysis captures chemical reactions among small molecules. Common mass spectrometry-based metabolomics workflows first identify the small molecules significantly associated with the outcome of interest, then begin exploring their biochemical relationships to understand biological fate or impact. We suggest an alternative by which general chemical relationships including abiotic reactions can be directly retrieved through untargeted high-resolution paired mass distance (PMD) analysis without a priori knowledge of the identities of participating compounds. PMDs calculated from the mass spectrometry data are linked to chemical reactions obtained via data mining of small molecule and reaction databases, i.e. ‘PMD-based reactomics’. We demonstrate applications of PMD-based reactomics including PMD network analysis, source appointment of unknown compounds, and biomarker reaction discovery as complements to compound discovery analyses used in traditional untargeted workflows. An R implementation of reactomics analysis and the reaction/PMD databases is available as the pmd package.

154: Integration of TPSA and High-Throughput Mass Spectrometry Data Improves Prostate Cancer Prediction

2007 ◽  
Vol 177 (4S) ◽  
pp. 52-53
Author(s):  
Stefano Ongarello ◽  
Eberhard Steiner ◽  
Regina Achleitner ◽  
Isabel Feuerstein ◽  
Birgit Stenzel ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Mass Spectrometry ◽  
High Throughput ◽  
Mass Spectrometry Data ◽  
Cancer Prediction

Nonparametric Pre-Processing Methods and Inference Tools for Analyzing Time-of-Flight Mass Spectrometry Data.

2007 ◽  
Vol 3 (2) ◽  
pp. 127-147 ◽  
Author(s):  
Anestis Antoniadis ◽  
Jeremie Bigot ◽  
Sophie Lambert-Lacroix ◽  
Frederique Letue
Keyword(s):  
Mass Spectrometry ◽  
Time Of Flight ◽  
Mass Spectrometry Data ◽  
Processing Methods ◽  
Flight Mass Spectrometry
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