Empowering large chemical knowledge bases for exposomics: PubChemLite meets MetFrag

Abstract

Compound (or chemical) databases are an invaluable resource for many scientific disciplines. Exposomics researchers need to find and identify relevant chemicals that cover the entirety of potential (chemical and other) exposures over entire lifetimes. This daunting task, with over 100 million chemicals in the largest chemical databases, coupled with broadly acknowledged knowledge gaps in these resources, leaves researchers faced with too much—yet not enough—information at the same time to perform comprehensive exposomics research. Furthermore, the improvements in analytical technologies and computational mass spectrometry workflows coupled with the rapid growth in databases and increasing demand for high throughput “big data” services from the research community present significant challenges for both data hosts and workflow developers. This article explores how to reduce candidate search spaces in non-target small molecule identification workflows, while increasing content usability in the context of environmental and exposomics analyses, so as to profit from the increasing size and information content of large compound databases, while increasing efficiency at the same time. In this article, these methods are explored using PubChem, the NORMAN Network Suspect List Exchange and the in silico fragmentation approach MetFrag. A subset of the PubChem database relevant for exposomics, PubChemLite, is presented as a database resource that can be (and has been) integrated into current workflows for high resolution mass spectrometry. Benchmarking datasets from earlier publications are used to show how experimental knowledge and existing datasets can be used to detect and fill gaps in compound databases to progressively improve large resources such as PubChem, and topic-specific subsets such as PubChemLite. PubChemLite is a living collection, updating as annotation content in PubChem is updated, and exported to allow direct integration into existing workflows such as MetFrag. The source code and files necessary to recreate or adjust this are jointly hosted between the research parties (see data availability statement). This effort shows that enhancing the FAIRness (Findability, Accessibility, Interoperability and Reusability) of open resources can mutually enhance several resources for whole community benefit. The authors explicitly welcome additional community input on ideas for future developments.

Retrospective non-target analysis to support regulatory water monitoring: from masses of interest to recommendations via in silico workflows

“…In some cases, there may be a lack of candidates matching the identification criteria when using CompTox with MetFrag simply because they may not exist within CompTox itself to begin with due to its limited size and scope. PubChemLite [55,56,58] represents one complementary alternative to these issues, as it is by design essentially a subset of environmentally relevant compounds based on compound classifications. Overall, the ability to subset databases based on usage and classification information of chemicals can be beneficial, as different regulatory bodies may have different mandates, and studies can be designed to align with those mandates accordingly, e.g., focus only on chemicals with (i) known usage in industrial manufacturing, or (ii) agricultural chemicals, or (iii) pharmaceuticals, etc.…”

FAIR chemical structures in the Journal of Cheminformatics

“…By improving the findability and interoperability , the barriers for reuse are lowered (see e.g., [ 7 ]). These author contributions, especially with valuable additional annotation information, are essential to fill gaps in the current chemical knowledge [ 8 ]. Thus, we believe it is time for JCheminform to take these logical next steps towards Open Science and help authors provide Open and FAIR er chemical data.…”

“…Her research combines cheminformatics and computational (high resolution) mass spectrometry approaches to elucidate the unknowns in complex samples and relate these to environmental causes of disease. She is involved in and organizes several European and worldwide activities to improve the exchange of data, information and ideas between scientists, including NORMAN-SLE, MassBank, MetFrag and PubChemLite for Exposomics [ 8 ].…”

Reply to “FAIR chemical structure in the Journal of Cheminformatics”

“…Schymanski and Bolton [ 1 ] requests the adoption of a FAIR approach to improve the FAIRness (findability, accessibility, interoperability, reusability) of chemical structures in the Journal of Cheminformatics. Currently, our journal encourages open science practices, but at the same time does not provide much guidance on how to implement these practices.…”

“…As a result, rather than require, we strongly recommend that authors include structure data in open, machine readable formats and identifiers, and point towards Schymanski and Bolton [ 1 ] as a means of doing so.…”