Bayesian methods for small molecule identification

Confident identification of small molecules remains a major challenge in untargeted metabolomics, natural product research and related fields. Liquid chromatography-tandem mass spectrometry is a predominant technique for the high-throughput analysis of small molecules and can detect thousands of different compounds in a biological sample. The automated interpretation of the resulting tandem mass spectra is highly non-trivial and many studies are limited to re-discovering known compounds by searching mass spectra in spectral reference libraries. But these libraries are vastly incomplete and a large portion of measured compounds remains unidentified. This constitutes a major bottleneck in the comprehensive, high-throughput analysis of metabolomics data. In this thesis, we present two computational methods that address different steps in the identification process of small molecules from tandem mass spectra. ZODIAC is a novel method for de novo that is, database-independent molecular formula annotation in complete datasets. It exploits similarities of compounds co-occurring in a sample to find the most likely molecular formula for each individual compound. ZODIAC improves on the currently best-performing method SIRIUS; on one dataset by 16.5 fold. We show that de novo molecular formula annotation is not just a theoretical advantage: We discover multiple novel molecular formulas absent from PubChem, one of the biggest structure databases. Furthermore, we introduce a novel scoring for CSI:FingerID, a state-of-the-art method for searching tandem mass spectra in a structure database. This scoring models dependencies between different molecular properties in a predicted molecular fingerprint via Bayesian networks. This problem has the unusual property, that the marginal probabilities differ for each predicted query fingerprint. Thus, we need to apply Bayesian networks in a novel, non-standard fashion. Modeling dependencies improves on the currently best scoring

Updates in metabolomics tools and resources: 2014-2015

Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources—in the form of tools, software, and databases—is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table

Computational methods for small molecules

Metabolism is the system of chemical reactions sustaining life in the cells of living organisms. It is responsible for cellular processes that break down nutrients for energy and produce building blocks for necessary molecules. The study of metabolism is vital to many disciplines in medicine and pharmacy. Chemical reactions operate on small molecules called metabolites, which form the core of metabolism. In this thesis we propose efficient computational methods for small molecules in metabolic applications. In this thesis we discuss four distinctive studies covering two major themes: the atom-level description of biochemical reactions, and analysis of tandem mass spectrometric measurements of metabolites. In the first part we study atom-level descriptions of organic reactions. We begin by proposing an optimal algorithm for determining the atom-to-atom correspondences between the reactant and product metabolites of organic reactions. In addition, we introduce a graph edit distance based cost as the mathematical formalism to determine optimality of atom mappings. We continue by proposing a compact single-graph representation of reactions using the atom mappings. We investigate the utility of the new representation in a reaction function classification task, where a descriptive category of the reaction's function is predicted. To facilitate the prediction, we introduce the first feasible path-based graph kernel, which describes the reactions as path sequences to high classification accuracy. In the second part we turn our focus on analysing tandem mass spectrometric measurements of metabolites. In a tandem mass spectrometer, an input molecule structure is fragmented into substructures or fragments, whose masses are observed. We begin by studying the fragment identification problem. A combinatorial algorithm is presented to enumerate candidate substructures based on the given masses. We also demonstrate the usefulness of utilising approximated bond energies as a cost function to rank the candidate structures according to their chemical feasibility. We propose fragmentation tree models to describe the dependencies between fragments for higher identification accuracy. We continue by studying a closely related problem where an unknown metabolite is elucidated based on its tandem mass spectrometric fragment signals. This metabolite identification task is an important problem in metabolomics, underpinning the subsequent modelling and analysis efforts. We propose an automatic machine learning framework to predict a set of structural properties of the unknown metabolite. The properties are turned into candidate structures by a novel statistical model. We introduce the first mass spectral kernels and explore three feature classes to facilitate the prediction. The kernels introduce support for high-accuracy mass spectrometric measurements for enhanced predictive accuracy.Tässä väitöskirjassa esitetään tehokkaita laskennallisia menetelmiä pienille molekyyleille aineenvaihduntasovelluksissa. Aineenvaihdunta on kemiallisten reaktioiden järjestelmä, joka ylläpitää elämää solutasolla. Aineenvaihduntaprosessit hajottavat ravinteita energiaksi ja rakennusaineiksi soluille tarpeellisten molekyylien valmistamiseen. Kemiallisten reaktioiden muokkaamia pieniä molekyylejä kutsutaan metaboliiteiksi. Tämä väitöskirja sisältää neljä itsenäistä tutkimusta, jotka jakautuvat teemallisesti biokemiallisten reaktioiden atomitason kuvaamiseen ja metaboliittien massaspektrometriamittausten analysointiin. Väitöskirjan ensimmäisessä osassa käsitellään biokemiallisten reaktioiden atomitason kuvauksia. Väitöskirjassa esitellään optimaalinen algoritmi reaktioiden lähtö- ja tuoteaineiden välisten atomikuvausten määrittämiseen. Optimaalisuus määrittyy verkkojen editointietäisyyteen perustuvalla kustannusfunktiolla. Optimaalinen atomikuvaus mahdollistaa reaktion kuvaamisen yksikäsitteisesti yhdellä verkolla. Uutta reaktiokuvausta hyödynnetään reaktion funktion ennustustehtävässä, jossa pyritään määrittämään reaktiota sanallisesti kuvaava kategoria automaattisesti. Väitöskirjassa esitetään polku-perustainen verkkokerneli, joka kuvaa reaktiot atomien polkusekvensseinä verrattuna aiempiin kulkusekvensseihin saavuttaen paremman ennustustarkkuuden. Väitöskirjan toisessa osassa analysoidaan metaboliittien tandem-massaspektrometriamittauksia. Tandem-massaspektrometri hajottaa analysoitavan syötemolekyylin fragmenteiksi ja mittaa niiden massa-varaus suhteet. Väitöskirjassa esitetään perusteellinen kombinatorinen algoritmi fragmenttien tunnistamiseen. Menetelmän kustannusfunktio perustuu fragmenttien sidosenergioiden vertailuun. Lopuksi väitöskirjassa esitetään fragmentaatiopuut, joiden avulla voidaan mallintaa fragmenttien välisiä suhteita ja saavuttaa parempi tunnistustarkkuus. Fragmenttien tunnistuksen ohella voidaan tunnistaa myös analysoitavia metaboliitteja. Ongelma on merkittävä ja edellytys aineenvaihdunnun analyyseille. Väitöskirjassa esitetään koneoppimismenetelmä, joka ennustaa tuntemattoman metaboliitin rakennetta kuvaavia piirteitä ja muodostaa niiden perusteella rakenne-ennusteita tilastollisesti. Menetelmä esittelee ensimmäiset erityisesti massaspektrometriadataan soveltuvat kernel-funktiot ja saavuttaa hyvän ennustustarkkuuden

Towards data-driven mass spectrometry in atmospheric science

Aerosols found in the atmosphere affect the climate and worsen air quality. To mitigate these adverse impacts, aerosol formation and aerosol chemistry in the atmosphere need to be better mapped out and understood. Currently, mass spectrometry is the single most important analytical technique in atmospheric chemistry and is used to track and identify compounds and processes. Vast amounts of data are collected in each measurement of current time-of-flight and orbitrap mass spectrometers using modern rapid data acquisition practices. However, compound identification remains as a major bottleneck during data analysis due to lacking reference libraries and analysis tools. Data-driven compound identification approaches could alleviate the problem, yet remain rare to non-existent in atmospheric science. In this perspective, we review the current state of data-driven compound identification with mass spectrometry in atmospheric science, and discuss current challenges and possible future steps towards a digital mass spectrometry era in atmospheric science

Identification of metabolites from tandem mass spectra with a machine learning approach utilizing structural features

MOTIVATION: Untargeted mass spectrometry is a powerful method for detecting metabolites in biological samples. However, fast and accurate identification of the metabolites' structures from MS/MS spectra is still a great challenge.RESULTS: We present a new analysis method, called SF-Matching, that is based on the hypothesis that molecules with similar structural features will exhibit similar fragmentation patterns. We combine information on fragmentation patterns of molecules with shared substructures and then use random forest models to predict whether a given structure can yield a certain fragmentation pattern. These models can then be used to score candidate molecules for a given mass spectrum. For rapid identification, we pre-compute such scores for common biological molecular structure databases. Using benchmarking datasets, we find that our method has similar performance to CSI:FingerID and that very high accuracies can be achieved by combining our method with CSI:FingerID. Rarefaction analysis of the training dataset shows that the performance of our method will increase as more experimental data become available. AVAILABILITY: SF-Matching is available from http://www.bork.embl.de/Docu/sf_matching. CONTACT: [email protected] (M.K.), [email protected] (P.B.

MassFormer: Tandem Mass Spectrum Prediction with Graph Transformers

Mass spectrometry is a key tool in the study of small molecules, playing an important role in metabolomics, drug discovery, and environmental chemistry. Tandem mass spectra capture fragmentation patterns that provide key structural information about a molecule and help with its identification. Practitioners often rely on spectral library searches to match unknown spectra with known compounds. However, such search-based methods are limited by availability of reference experimental data. In this work we show that graph transformers can be used to accurately predict tandem mass spectra. Our model, MassFormer, outperforms competing deep learning approaches for spectrum prediction, and includes an interpretable attention mechanism to help explain predictions. We demonstrate that our model can be used to improve reference library coverage on a synthetic molecule identification task. Through quantitative analysis and visual inspection, we verify that our model recovers prior knowledge about the effect of collision energy on the generated spectrum. We evaluate our model on different types of mass spectra from two independent MS datasets and show that its performance generalizes. Code available at github.com/Roestlab/massformer.Comment: 14 pages (10 without bibliography), 5 figures, 3 table

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub