Integrated Probabilistic Annotation: a Bayesian-based annotation method for metabolomic profiles integrating biochemical connections, isotope patterns and adduct relationships

Del Carratore, F., Schmidt, K., Vinaixa, M., Hollywood, K. A., Greenland-Bews, C., Takano, E., Rogers, S. and Breitling, R. (2019) Integrated Probabilistic Annotation: a Bayesian-based annotation method for metabolomic profiles integrating biochemical connections, isotope patterns and adduct relationships. Analytical Chemistry, 91(20), pp. 12799-12807. (doi: 10.1021/acs.analchem.9b02354) (PMID:31509381)

Abstract

In a typical untargeted metabolomics experiment, the huge amount of complex data generated by mass spectrometry necessitates automated tools for the extraction of useful biological information. Each metabolite generates numerous mass spectrometry features. The association of these experimental features to the underlying metabolites still represents one of the major bottlenecks in metabolomics data processing. While certain identification (e.g., by comparison to authentic standards) is always desirable, it is usually achievable only for a limited number of compounds, and scientist often deal with a significant amount of putatively annotated metabolites. The confidence in a specific annotation is usually assessed by considering different sources of information (e.g., isotope patterns, adduct formation, chromatographic retention times, fragmentation patterns). IPA (Integrated Probabilistic Annotation) offers a rigorous and reproducible method to automatically annotate metabolite profiles and evaluate the resulting confidence of the putative annotations. It is able to provide a rigorous measure of our confidence in any putative annotation and is also able to update and refine our beliefs (i.e., background prior knowledge) by incorporating different sources of information in the annotation process, such as isotope patterns, adduct formation and biochemical relations. The IPA package is freely available on GitHub (https://github.com/francescodc87/IPA) together with the related extensive documentation.

• Item Type: Articles
• Additional Information: This is a contribution from the Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM) and acknowledges the Biotechnology and Biological Sciences Research Council (BBSRC) and Engineering and Physical Sciences Research Council (EPSRC) for financial support (Grant No. BB/M017702/1). This project has received funding from the European Union Horizon 2020 Research and Innovation Programme under Grant Agreement No. 720793 TOPCAPI–Thoroughly Optimised Production Chassis for Advanced Pharmaceutical Ingredients and No. 814408 SHIKIFACTORY100–Modular cell factories for the production of 100 compounds from the shikimate pathway.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Rogers, Dr Simon
• Authors: Del Carratore, F., Schmidt, K., Vinaixa, M., Hollywood, K. A., Greenland-Bews, C., Takano, E., Rogers, S., and Breitling, R.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Analytical Chemistry
• Publisher: American Chemical Society
• ISSN: 0003-2700
• ISSN (Online): 1520-6882
• Published Online: 11 September 2019
• Copyright Holders: Copyright © 2019 American Chemical Society
• First Published: First published in Analytical Chemistry 91(20):12799–12807
• Publisher Policy: Reproduced under a Creative Commons License