Integration of computational mass spectrometry and risk-based datamining in food safety analysis
Wageningen University Bioinformatics Group & Wageningen Food Safety Research, Wageningen, NL
Wageningen University Bioinformatics Group & Wageningen Food Safety Research, Wageningen, NL
General info: www.wur.nl/nl/vacature/phd-candidate-in-computational-mass-spectrometry-for-food-safety-1.htm
Brief Description:
High-resolution mass spectrometry generates extensive data sets contain much more information than we can currently extract from them. Complementary to looking for single chemical compounds, identifying patterns in mass spectra that can be associated with chemical risks might give additional insights about potential hazards in our food and feed. To do so, the candidate will set up a computational mass spectrometry workflow to find yet unknown food safety hazards in high-resolution mass spectrometry data. In addition, using toxicity prediction tools and sample meta-data, the most relevant food toxicants will be filtered from 1000s of annotated compounds. Various new, exciting high end mass spectrometers are available to obtain part of the input data. Tasks include:
- setting up a pipeline to structurally annotate high-resolution mass spectrometry data
- designing a toxicity prediction tool based on chemical structure databases
- developing a toxicity-based data mining workflow based on both spectral and structure input
- applying the entire workflow to a real-life analytical data set for food safety
Qualifications:
Candidates should hold a successfully completed MSc in (bio)informatics, metabolomics, (bio)analytical chemistry or related area, with a strong interest and demonstrable skills in programming (in Python), (computational) metabolomics, and/or analytical chemistry, preferably high-resolution mass spectrometry. We are specifically seeking candidate with excellent communication skills, both oral and written, who like to work in a multidisciplinary setting, and are critical, accurate and efficient way.
Start date:
High-resolution mass spectrometry generates extensive data sets contain much more information than we can currently extract from them. Complementary to looking for single chemical compounds, identifying patterns in mass spectra that can be associated with chemical risks might give additional insights about potential hazards in our food and feed. To do so, the candidate will set up a computational mass spectrometry workflow to find yet unknown food safety hazards in high-resolution mass spectrometry data. In addition, using toxicity prediction tools and sample meta-data, the most relevant food toxicants will be filtered from 1000s of annotated compounds. Various new, exciting high end mass spectrometers are available to obtain part of the input data. Tasks include:
- setting up a pipeline to structurally annotate high-resolution mass spectrometry data
- designing a toxicity prediction tool based on chemical structure databases
- developing a toxicity-based data mining workflow based on both spectral and structure input
- applying the entire workflow to a real-life analytical data set for food safety
Qualifications:
Candidates should hold a successfully completed MSc in (bio)informatics, metabolomics, (bio)analytical chemistry or related area, with a strong interest and demonstrable skills in programming (in Python), (computational) metabolomics, and/or analytical chemistry, preferably high-resolution mass spectrometry. We are specifically seeking candidate with excellent communication skills, both oral and written, who like to work in a multidisciplinary setting, and are critical, accurate and efficient way.
Start date:
As soon as possible
How to Apply
Please fill out the application form on our website, www.wur.nl/nl/vacature/phd-candidate-in-computational-mass-spectrometry-for-food-safety-1.htm.
Please fill out the application form on our website, www.wur.nl/nl/vacature/phd-candidate-in-computational-mass-spectrometry-for-food-safety-1.htm.
Applications are dealt with in order, so please do not defer submission.
Bioinformatics Group, Department of Plant Sciences, Wageningen University
justin.vanderhooft@wur.nl
Tel: +31317483061
Building.room 107/W2.Aa.083, Droevendaalsesteeg 1, 6708PB Wageningen - NL
Post: PO Box 633, 6700AP Wageningen, NL
http://www.wur.nl/en/Expertise-Services/Chair-groups/Plant-Sciences/Bioinformatics.htm
Working with genomics and/or metabolomics data and wondering how to turn it into structure and function?! Consider joining the NPLinker eScience Genome & Metabolome Mining workshop organised in March 2023 in Wageningen, NL. Find more information here:
https://www.wur.nl/en/research-results/chair-groups/plant-sciences/bioinformatics/teaching/nplinker_workshop.htm
Working with mass spectrometry and/or NMR data to discover novel and known chemistry in complex natural mixtures? Have a look at our most recent review on how computational metabolomics approaches support the mapping of chemical space based on networking and substructure-based analyses:
And thinking of linking genome mining to metabolome mining analysis? Please find our tutorial review here highlighting opportunities and pitfalls: https://pubs.rsc.org/en/content/articlelanding/2020/CS/D0CS00162G#!divAbstract
——
Dr Justin J.J. van der Hooft
Assistant Professor in Computational Metabolomics
Assistant Professor in Computational Metabolomics
Visiting Professor at the Biochemistry Group, University of Johannesburg, South Africa
Member of Wageningen Young Academy | Member of Young NMC & Netherlands Metabolomics Center | Member of Metabolomics Society
Follow me on Twitter for the latest science updates: @jjjvanderhooft
Our
most recent group effort is a preprint on Good Practices and Recommendations for Using and Benchmarking Computational Metabolomics Metabolite Annotation Tools:
https://www.researchsquare.com/article/rs-1662223/v1 - let us know what you think! :-)
Group Website: https://vdhooftcompmet.github.io
Follow us on Twitter! —> @vdHooft_CompMet
Bioinformatics Group, Department of Plant Sciences, Wageningen University
justin.vanderhooft@wur.nl
Tel: +31317483061
Building.room 107/W2.Aa.083, Droevendaalsesteeg 1, 6708PB Wageningen - NL
Post: PO Box 633, 6700AP Wageningen, NL
http://www.wur.nl/en/Expertise-Services/Chair-groups/Plant-Sciences/Bioinformatics.htm
Working with genomics and/or metabolomics data and wondering how to turn it into structure and function?! Consider joining the NPLinker eScience Genome & Metabolome Mining workshop organised in March 2023 in Wageningen, NL. Find more information here:
https://www.wur.nl/en/research-results/chair-groups/plant-sciences/bioinformatics/teaching/nplinker_workshop.htm
Working with mass spectrometry and/or NMR data to discover novel and known chemistry in complex natural mixtures? Have a look at our most recent review on how computational metabolomics approaches support the mapping of chemical space based on networking and substructure-based analyses:
Working
with metabolomics data? Without realizing, you are using spectral similarity scoring all the time. For example, during library matching to annotate structures in your profile. Spec2Vec similarity scoring is the first machine learning based spectral
similarity score outperforming classical scores! Find our paper in Plos Computational Biology here:
Spec2Vec’s
supervised cousin MS2DeepScore was published in 2021 in Journal of ChemInformatics: https://jcheminf.biomedcentral.com/articles/10.1186/s13321-021-00558-4
Do you have metabolomics data of sequenced organisms or of communities or complex samples uploaded in open databases? Please consider to record their links in our just in Nature Chemical Biology published platform:
https://www.nature.com/articles/s41589-020-00724-z
And thinking of linking genome mining to metabolome mining analysis? Please find our tutorial review here highlighting opportunities and pitfalls: https://pubs.rsc.org/en/content/articlelanding/2020/CS/D0CS00162G#!divAbstract
Do you want to streamline your metabolomics analyses even more? Use ReDU and FBMN! ReDU offers consistent metadata across many public mass spectrometry fragmentation data files. FBMN brings together qualitative and quantitative analyses!
Did
you hear about molecular networking? But you don’t know how to create them? Look no further and start with our protocol that is describing how to for various instruments and platforms: https://rdcu.be/b4Qo6
——