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AI3SD Video: Finding Small Molecules in Big Data

AI3SD Video: Finding Small Molecules in Big Data
AI3SD Video: Finding Small Molecules in Big Data
The environment and the chemicals to which we are exposed is incredibly complex, with over 111 million chemicals in the largest open chemical databases, 300,000 estimated in global inventories of high use, and over 70,000 in household use alone. Detectable molecules in environmental samples, metabolomics and exposomics can now be captured using high resolution mass spectrometry (HRMS), which provides a “snapshot” of all chemicals present in a sample and allows for retrospective data analysis through digital archiving. However, there is no “one size fits all” analytical method, and scientists cannot yet identify most of the tens of thousands of features in each sample, let alone associate them with health or disease, leading to critical bottlenecks in identification and data interpretation. Defining the chemical space to search is a huge challenge, especially considering that chemicals transform in both organisms (metabolism) and the environment (both biotic and abiotic processes). This talk will cover European and worldwide community initiatives and resources to help find and identify small molecules and their metabolites (transformation products) - from compound databases to spectral libraries, from literature mining to transformation prediction. It will show how FAIR and Open interdisciplinary efforts and data sharing can facilitate research in many areas of small molecule research. Various contributors to this massive collaborative effort will be acknowledged throughout the talk.
AI, AI3SD Event, Big Data, Data Science, Molecules
Schymanski, Emma
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Frey, Jeremy G.
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Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
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Schymanski, Emma
5f0b498b-516b-4032-b8d8-a105c9841175
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

Schymanski, Emma (2021) AI3SD Video: Finding Small Molecules in Big Data. Frey, Jeremy G., Kanza, Samantha and Niranjan, Mahesan (eds.) AI3SD Autumn Seminar Series 2021. 13 Oct - 15 Dec 2021. (doi:10.5258/SOTON/AI3SD0175).

Record type: Conference or Workshop Item (Other)

Abstract

The environment and the chemicals to which we are exposed is incredibly complex, with over 111 million chemicals in the largest open chemical databases, 300,000 estimated in global inventories of high use, and over 70,000 in household use alone. Detectable molecules in environmental samples, metabolomics and exposomics can now be captured using high resolution mass spectrometry (HRMS), which provides a “snapshot” of all chemicals present in a sample and allows for retrospective data analysis through digital archiving. However, there is no “one size fits all” analytical method, and scientists cannot yet identify most of the tens of thousands of features in each sample, let alone associate them with health or disease, leading to critical bottlenecks in identification and data interpretation. Defining the chemical space to search is a huge challenge, especially considering that chemicals transform in both organisms (metabolism) and the environment (both biotic and abiotic processes). This talk will cover European and worldwide community initiatives and resources to help find and identify small molecules and their metabolites (transformation products) - from compound databases to spectral libraries, from literature mining to transformation prediction. It will show how FAIR and Open interdisciplinary efforts and data sharing can facilitate research in many areas of small molecule research. Various contributors to this massive collaborative effort will be acknowledged throughout the talk.

Video
AI3SDAutumnSeminar-151221-EmmaSchymanski - Version of Record
Available under License Creative Commons Attribution.
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More information

Published date: 15 December 2021
Additional Information: Associate Professor Emma Schymanski is head of the Environmental Cheminformatics (ECI) group at the Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg. In 2018 she received a Luxembourg National Research Fund (FNR) ATTRACT Fellowship to establish her group in Luxembourg, following a 6 year postdoc at Eawag, the Swiss Federal Institute of Aquatic Science and Technology and a PhD at the Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany. Before undertaking her PhD, she worked as a consulting environmental engineer in Perth, Australia. She has over 90 publications and a book, and is involved in many collaborative software efforts. Her research combines cheminformatics and computational (high resolution) mass spectrometry approaches to elucidate the unknowns in complex samples, primarily with non-target screening, and relating these to environmental causes of disease. An advocate for open science, she is involved in and organizes several European and worldwide activities to improve the exchange of data, information and ideas between scientists to push progress in this field, including NORMAN Network activities (e.g. NORMAN-SLE https://www.norman-network.com/nds/SLE/), MassBank (https://massbank.eu/MassBank/), MetFrag (https://msbi.ipb-halle.de/MetFrag/) and PubChemLite for Exposomics (https://doi.org/10.1186/s13321-021-00489-0).
Venue - Dates: AI3SD Autumn Seminar Series 2021, 2021-10-13 - 2021-12-15
Keywords: AI, AI3SD Event, Big Data, Data Science, Molecules

Identifiers

Local EPrints ID: 453352
URI: http://eprints.soton.ac.uk/id/eprint/453352
PURE UUID: 2c80f20c-5bc1-43c5-9861-99033cbdb15b
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302
ORCID for Samantha Kanza: ORCID iD orcid.org/0000-0002-4831-9489
ORCID for Mahesan Niranjan: ORCID iD orcid.org/0000-0001-7021-140X

Catalogue record

Date deposited: 13 Jan 2022 18:11
Last modified: 14 Jan 2022 02:53

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Contributors

Author: Emma Schymanski
Editor: Jeremy G. Frey ORCID iD
Editor: Samantha Kanza ORCID iD
Editor: Mahesan Niranjan ORCID iD

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