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Title: Cognitive analysis of metabolomics data for systems biology

Abstract

Cognitive computing is revolutionizing the way big data are processed and integrated, with artificial intelligence (AI) natural language processing (NLP) platforms helping researchers to efficiently search and digest the vast scientific literature. Most available platforms have been developed for biomedical researchers, but new NLP tools are emerging for biologists in other fields and an important example is metabolomics. NLP provides literature-based contextualization of metabolic features that decreases the time and expert-level subject knowledge required during the prioritization, identification and interpretation steps in the metabolomics data analysis pipeline. Here, we describe and demonstrate four workflows that combine metabolomics data with NLP-based literature searches of scientific databases to aid in the analysis of metabolomics data and their biological interpretation. Additionally, the four procedures can be used in isolation or consecutively, depending on the research questions. The first, used for initial metabolite annotation and prioritization, creates a list of metabolites that would be interesting for follow-up. The second workflow finds literature evidence of the activity of metabolites and metabolic pathways in governing the biological condition on a systems biology level. The third is used to identify candidate biomarkers, and the fourth looks for metabolic conditions or drug-repurposing targets that the two diseases havemore » in common. The protocol can take 1–4 h or more to complete, depending on the processing time of the various software used.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2];  [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1];  [2]; ORCiD logo [3]; ORCiD logo [1]
  1. The Scripps Research Inst., La Jolla, CA (United States). Center for Mass Spectrometry and Metabolomics
  2. IBM Watson Health, Cambridge, MA (United States)
  3. Waters Corp., Milford, MA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER); National Institutes of Health (NIH)
OSTI Identifier:
1774918
Grant/Contract Number:  
AC02-05CH11231; RI2811/1-1; R35 GM130385; P30 MH062261; P01 DA026146; U01 CA235493
Resource Type:
Accepted Manuscript
Journal Name:
Nature Protocols
Additional Journal Information:
Journal Volume: 16; Journal Issue: 3; Journal ID: ISSN 1754-2189
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; Literature mining; predictive markers; systems analysis

Citation Formats

Majumder, Erica L.-W., Billings, Elizabeth M., Benton, H. Paul, Martin, Richard L., Palermo, Amelia, Guijas, Carlos, Rinschen, Markus M., Domingo-Almenara, Xavier, Montenegro-Burke, J. Rafael, Tagtow, Bradley A., Plumb, Robert S., and Siuzdak, Gary. Cognitive analysis of metabolomics data for systems biology. United States: N. p., 2021. Web. doi:10.1038/s41596-020-00455-4.
Majumder, Erica L.-W., Billings, Elizabeth M., Benton, H. Paul, Martin, Richard L., Palermo, Amelia, Guijas, Carlos, Rinschen, Markus M., Domingo-Almenara, Xavier, Montenegro-Burke, J. Rafael, Tagtow, Bradley A., Plumb, Robert S., & Siuzdak, Gary. Cognitive analysis of metabolomics data for systems biology. United States. https://doi.org/10.1038/s41596-020-00455-4
Majumder, Erica L.-W., Billings, Elizabeth M., Benton, H. Paul, Martin, Richard L., Palermo, Amelia, Guijas, Carlos, Rinschen, Markus M., Domingo-Almenara, Xavier, Montenegro-Burke, J. Rafael, Tagtow, Bradley A., Plumb, Robert S., and Siuzdak, Gary. Fri . "Cognitive analysis of metabolomics data for systems biology". United States. https://doi.org/10.1038/s41596-020-00455-4. https://www.osti.gov/servlets/purl/1774918.
@article{osti_1774918,
title = {Cognitive analysis of metabolomics data for systems biology},
author = {Majumder, Erica L.-W. and Billings, Elizabeth M. and Benton, H. Paul and Martin, Richard L. and Palermo, Amelia and Guijas, Carlos and Rinschen, Markus M. and Domingo-Almenara, Xavier and Montenegro-Burke, J. Rafael and Tagtow, Bradley A. and Plumb, Robert S. and Siuzdak, Gary},
abstractNote = {Cognitive computing is revolutionizing the way big data are processed and integrated, with artificial intelligence (AI) natural language processing (NLP) platforms helping researchers to efficiently search and digest the vast scientific literature. Most available platforms have been developed for biomedical researchers, but new NLP tools are emerging for biologists in other fields and an important example is metabolomics. NLP provides literature-based contextualization of metabolic features that decreases the time and expert-level subject knowledge required during the prioritization, identification and interpretation steps in the metabolomics data analysis pipeline. Here, we describe and demonstrate four workflows that combine metabolomics data with NLP-based literature searches of scientific databases to aid in the analysis of metabolomics data and their biological interpretation. Additionally, the four procedures can be used in isolation or consecutively, depending on the research questions. The first, used for initial metabolite annotation and prioritization, creates a list of metabolites that would be interesting for follow-up. The second workflow finds literature evidence of the activity of metabolites and metabolic pathways in governing the biological condition on a systems biology level. The third is used to identify candidate biomarkers, and the fourth looks for metabolic conditions or drug-repurposing targets that the two diseases have in common. The protocol can take 1–4 h or more to complete, depending on the processing time of the various software used.},
doi = {10.1038/s41596-020-00455-4},
journal = {Nature Protocols},
number = 3,
volume = 16,
place = {United States},
year = {Fri Jan 22 00:00:00 EST 2021},
month = {Fri Jan 22 00:00:00 EST 2021}
}

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