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Justin Van der Hooft   Dr.  Post Doctoral Researcher 
Affiliations
Bioinformatics Group, Wageningen University, Wageningen, The Netherlands
About

Relevant Links: LinkedIn profile: http://www.linkedin.com/pub/justin-van-der-hooft/35/a93/9aa Google Citations: https://scholar.google.nl/citations?user=zv9seLwAAAAJ&hl=en MS2LDA tool: http://www.ms2lda.org MAGMa tool: https://www.emetabolomics.org/ WUR-Bioinformatics: http://www.wur.nl/en/Expertise-Services/Chair-groups/Plant-Sciences/Bioinformatics.htm Pieter Dorrestein group at UCSD: http://dorresteinlab.ucsd.edu/Dorrestein_Lab/Research.html Glasgow Polyomics: http://www.polyomics.gla.ac.uk/

Timeline See timeline
Justin Van der Hooft published an article in February 2019.
Research Keywords & Expertise
0 Mass Spectrometry
0 Metabolite Identification
0 Metabolomics
0 NMR Spectroscopy
Top co-authors See all
Pieter C. Dorrestein

250 shared publications

University of California, San Diego

Nina Rønsted

83 shared publications

Natural History Museum of Denmark, University of Copenhagen, Denmark

Marnix H Medema

69 shared publications

Bioinformatics Group, Wageningen University, Wageningen, Netherlands

Manimozhiyan Arumugam

54 shared publications

University of Copenhagen

Simon Rogers

40 shared publications

School of Computing Science, University of Glasgow, Glasgow, UK

22
Publications
10
Reads
2
Downloads
215
Citations
Publication Record
Distribution of Articles published per year 
(2009 - 2019)
Total number of journals
published in
 
14
 
Publications See all
PREPRINT-CONTENT 1 Read 0 Citations Implementations of the chemical structural and compositional similarity metric in R and Python Asker Daniel Brejnrod, Madeleine Ernst, Piotr Dworzynski, La... Published: 11 February 2019
bioRxiv, doi: 10.1101/546150
DOI See at publisher website ABS Show/hide abstract
Tandem mass spectrometry (MS/MS) has the potential to substantially improve metabolomics by acquiring spectra of fragmented ions. These fragmentation spectra can be represented as a molecular network, by measuring cosine distances between them, thus identifying signals from the same or similar molecules. Metrics that enable comparison between pairs of samples based on their metabolite profiles are in great need. Taking inspiration from the successful phylogeny-aware beta-diversity measures used in microbiome research, integrating chemical similarity information about the features in addition to their abundances could lead to better insights when comparing metabolite profiles. Chemical Structural and Compositional Similarity (CSCS) is a recently published similarity metric comparing the full set of signals and their chemical similarity between two samples. Efficient, scalable and easily accessible implementations of this algorithm is currently lacking. Here, we present an easily accessible and scalable implementation of CSCS in both python and R, including a version not weighted by intensity information. We provide a new implementation of the CSCS algorithm that is over 300 times faster than the published implementation in R, making the algorithm suitable for large-scale metabolomics applications. We also show that adding chemical information enriches existing methods. Furthermore, the R implementation includes functions for exporting molecular networks directly from the mass spectral molecular networking platform GNPS for ease of use for downstream applications.
PREPRINT-CONTENT 0 Reads 0 Citations Deciphering complex metabolite mixtures by unsupervised and supervised substructure discovery and semi-automated annotat... Simon Rogers, Cher Wei Ong, Joe Wandy, Madeleine Ernst, Lars... Published: 09 December 2018
bioRxiv, doi: 10.1101/491506
DOI See at publisher website ABS Show/hide abstract
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.
PREPRINT-CONTENT 0 Reads 0 Citations QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A B... Published: 03 December 2018
doi: 10.7287/peerj.preprints.27295
DOI See at publisher website ABS Show/hide abstract
We present QIIME 2, an open-source microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.
PREPRINT-CONTENT 0 Reads 0 Citations QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science Evan Bolyen, Jai Ram Rideout, Matthew R Dillon, Nicholas A B... Published: 03 December 2018
doi: 10.7287/peerj.preprints.27295v2
DOI See at publisher website ABS Show/hide abstract
We present QIIME 2, an open-source microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.
Article 0 Reads 0 Citations Accelerating Metabolite Identification in Natural Product Research: Toward an Ideal Combination of Liquid Chromatography... Jean-Luc Wolfender, Jean-Marc Nuzillard, Justin J. J. Van De... Published: 19 November 2018
Analytical Chemistry, doi: 10.1021/acs.analchem.8b05112
DOI See at publisher website
PREPRINT-CONTENT 0 Reads 0 Citations Comprehensive mass spectrometry-guided plant specialized metabolite phenotyping reveals metabolic diversity in the cosmo... Kyo Bin Kang, Madeleine Ernst, Justin J. J. Van Der Hooft, R... Published: 07 November 2018
bioRxiv, doi: 10.1101/463620
DOI See at publisher website ABS Show/hide abstract
Plants produce a myriad of specialized metabolites to overcome their sessile habit and combat biotic as well as abiotic stresses. Evolution has shaped specialized metabolite diversity, which drives many other aspects of plant biodiversity. However, until recently, large-scale studies investigating specialized metabolite diversity in an evolutionary context have been limited by the impossibility to identify chemical structures of hundreds to thousands of compounds in a time-feasible manner. Here, we introduce a workflow for large-scale, semi-automated annotation of specialized metabolites, and apply it for over 1000 metabolites of the cosmopolitan plant family Rhamnaceae. We enhance the putative annotation coverage dramatically, from 2.5 % based on spectral library matches alone to 42.6 % of total MS/MS molecular features extending annotations from well-known plant compound classes into the dark plant metabolomics matter. To gain insights in substructural diversity within the plant family, we also extract patterns of co-occurring fragments and neutral losses, so-called Mass2Motifs, from the dataset; for example, only the Ziziphoid clade developed the triterpenoid biosynthetic pathway, whereas the Rhamnoid clade predominantly developed diversity in flavonoid glycosides, including 7-O-methyltransferase activity. Our workflow provides the foundations towards the automated, high-throughput chemical identification of massive metabolite spaces, and we expect it to revolutionize our understanding of plant chemoevolutionary mechanisms.
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