Distribution of Articles published per year
(2003 - 2018)
(2003 - 2018)
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PREPRINT-CONTENT 0 Reads 0 Citations Deciphering complex metabolite mixtures by unsupervised and supervised substructure discovery and semi-automated annotat... Published: 09 December 2018
bioRxiv, doi: 10.1101/491506
Complex metabolite mixtures are challenging to unravel. Mass spectrometry (MS) is a widely used and sensitive technique to obtain structural information on complex mixtures. However, just knowing the molecular masses of the mixture's constituents is almost always insufficient for confident assignment of the associated chemical structures. Structural information can be augmented through MS fragmentation experiments whereby detected metabolites are fragmented giving rise to MS/MS spectra. However, how can we maximize the structural information we gain from fragmentation spectra? We recently proposed a substructure-based strategy to enhance metabolite annotation for complex mixtures by considering metabolites as the sum of (bio)chemically relevant moieties that we can detect through mass spectrometry fragmentation approaches. Our MS2LDA tool allows us to discover - unsupervised - groups of mass fragments and/or neutral losses termed Mass2Motifs that often correspond to substructures. After manual annotation, these Mass2Motifs can be used in subsequent MS2LDA analyses of new datasets, thereby providing structural annotations for many molecules that are not present in spectral databases. Here, we describe how additional strategies, taking advantage of i) combinatorial in-silico matching of experimental mass features to substructures of candidate molecules, and ii) automated machine learning classification of molecules, can facilitate semi-automated annotation of substructures. We show how our approach accelerates the Mass2Motif annotation process and therefore broadens the chemical space spanned by characterized motifs. Our machine learning model used to classify fragmentation spectra learns the relationships between fragment spectra and chemical features. Classification prediction on these features can be aggregated for all molecules that contribute to a particular Mass2Motif and guide Mass2Motif annotations. To make annotated Mass2Motifs available to the community, we also present motifDB: an open database of Mass2Motifs that can be browsed and accessed programmatically through an API. MotifDB is integrated within ms2lda.org, allowing users to efficiently search for characterized motifs in their own experiments. We expect that with an increasing number of Mass2Motif annotations available through a growing database we can more quickly gain insight in the constituents of complex mixtures. That will allow prioritization towards novel or unexpected chemistries and faster recognition of known biochemical building blocks.
PROCEEDINGS-ARTICLE 17 Reads 0 Citations Integrated metabolome mining and annotation pipeline accelerates elucidation and prioritisation of specialised metabolit... Published: 15 November 2018
Proceedings of 3rd International Electronic Conference on Metabolomics, doi: 10.3390/iecm-3-05843
Microbes and plants produce a gold mine of chemically diverse, high-value molecules like antibiotics. However, chemical structures of many natural products (NPs) remain currently unknown, hampering medicinal applications. A key challenge for natural product discovery is the metabolome complexity in natural extracts, from which mass spectrometry data needs to be coupled to chemical structures. Nevertheless, many NPs share molecular substructures and form structurally related molecular families (MFs), which has inspired metabolome mining tools exploiting these biochemical relationships. Here, we introduce a workflow that combines two existing metabolome mining tools to discover MFs, subfamilies, and subtle structural differences between family members. Where tandem mass spectral Molecular Networking (1) efficiently groups natural products in molecular families, MS2LDA (2) discovers substructures that aid in further recognition of subfamilies and shared modifications. Furthermore, through the combined use of Network Annotation Propagation (3) and ClassyFire (4), we can automatically perform MF chemical classifications. When unexpected MF classifications are observed, they could represent novel chemical scaffolds, thereby guiding follow-up prioritization efforts towards unknown chemistry. Recognition of the smaller building blocks (substructures) that form the basis of molecular families also accelerates data analysis, especially for cases where hardly any reference MS/MS spectra or candidate structures from structural databases are available. We demonstrate how our integrative workflow discovers dozens of MFs in large-scale metabolomics studies of plant and bacterial extracts. For example, Rhamnaceae plants contained triterpenoid chemistries in which several distinct phenolic acid modifications (e.g., vanillate, protocatechuate) were readily recognized. Furthermore, a previously not annotated tryptophan-based MF was uncovered in marine Streptomyces extracts. In Photo/Xenorhabdus strains, following leads from peptidic natural products finding software Dereplicator (5), a Xenoamicin-based peptidic MF was deciphered and Mass2Motifs for both the peptidic ring and tail were easily annotated highlighting ring-related modifications. Our workflow accelerates NP discovery by MF and substructure annotations and classifications on an unprecedented large scale that will aid in future integration with genome mining workflows. Finally, the workflow applications go beyond the natural products field into nutritional, clinical, and exposome metabolomics. References: Wang, M.. et al., “Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking”., Biotech.34(8):828-837, 2016. Van der Hooft, J.J.J. et al., “Topic modeling for untargeted substructure exploration in metabolomics”. N.A.S.113(48):13738-13743, 2016. da Silva, R.R. et al., “Propagating annotations of molecular networks using in silico...
Article 0 Reads 0 Citations Minimally-destructive atmospheric ionisation mass spectrometry authenticates authorship of historical manuscripts Published: 26 July 2018
Scientific Reports, doi: 10.1038/s41598-018-28810-2
Authentic historic manuscripts fetch high sums, but establishing their authenticity is challenging, relies on a host of stylistic clues and requires expert knowledge. High resolution mass spectrometry has not, until now, been applied to guide the authentication of historic manuscripts. Robert Burns is a well-known Scottish poet, whose fame, and the eponymous ‘Burns Night’ are celebrated world-wide. Authenticity of his works is complicated by the ‘industrial’ production of fakes by Alexander Smith in the 1890s, many of which were of good quality and capable of fooling experts. This study represents the first analysis of the inks and paper used in Burns poetry, in a minimally destructive manner that could find application in many areas. Applying direct infusion mass spectrometry to a panel of selected authenticated Burns and Smith manuscripts, we have produced a Support Vector Machine classifier that distinguishes Burns from Smith with a 0.77 AUC. Using contemporary recipes for inks, we were also able to match features of each to the inks used to produce some of Burns’ original manuscripts. We anticipate the method and classifier having broad application in authentication of manuscripts, and our analysis of contemporary inks to provide insights into the production of written works of art.
Article 0 Reads 0 Citations Online Decision Support Tool for Personalized Cancer Symptom Checking in the Community (REACT): Acceptability, Feasibili... Published: 04 July 2018
JMIR Cancer, doi: 10.2196/10073
Article 0 Reads 0 Citations EZ-Root-VIS: A Software Pipeline for the Rapid Analysis and Visual Reconstruction of Root System Architecture Published: 12 June 2018
Plant Physiology, doi: 10.1104/pp.18.00217
Article 0 Reads 0 Citations ShinyKGode: an interactive application for ODE parameter inference using gradient matching Published: 27 February 2018
Bioinformatics, doi: 10.1093/bioinformatics/bty089