{ "version": "https://jsonfeed.org/version/1.1", "title": "chem-bla-ics", "description": "Chemblaics (pronounced chem-bla-ics) is the science that uses open science and computers to solve problems in chemistry, biochemistry and related fields.", "home_page_url": "https://chem-bla-ics.linkedchemistry.info/", "feed_url": "https://chem-bla-ics.linkedchemistry.info/2024/07/31/directed-metabolic-network.json", "icon": "https://chem-bla-ics.linkedchemistry.info/assets/images/chem-bla-ics_logo.png", "language": "en", "authors": [ { "name": "Egon Willighagen", "url": "https://orcid.org/0000-0001-7542-0286", "_orcid": "0000-0001-7542-0286" } ], "items": [ { "id": "https://doi.org/10.59350/32j3a-7ae65", "url": "https://chem-bla-ics.linkedchemistry.info/2024/07/31/directed-metabolic-network.html", "title": "New paper: "Discovering life's directed metabolic (sub)paths to interpret human biochemical markers using the DSMN tool"", "content_html": "

I am still catching up with a lot of work, and found out I actually had forgotten to blog about this cool article\nby Denise Slenter: “Discovering life’s directed metabolic (sub)paths to\ninterpret human biochemical markers using the DSMN tool” (doi:10.1039/D3DD00069A).\nThis paper explains how various open science resources (Wikidata,\nReactome, WikiPathways) are used to visualize\nthe biological story of the data from two metabolomics experiments archived in MetaboLights.

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Using Neo4J and Cytoscape she visualizes the data onto a network created with\nRDF, SPARQL from the above resources:

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The whole approach uses open science, making the work very reproducible. This is essential, as our knowledge\nabout metabolic processes continues to grow, if not only for the human lipids, but also from molecular\nimaging technologies. Moreover, a lot of biological detail is yet to be encoded on pathway databases,\nsuch as cellular location of proteins and metabolites, which proteins are expressed in which tissue, or\nthe kinetics of metabolic reactions. All knowledge that can be pulled it via knowledge graphs becomes\nimmediately available by using this FAIR approach.

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One last note, the reader may notice a focus on the shortest path. Of course, the biological relevant\npath may not be the “shortest” path. But from a network analysis perspective that question is purely\nacademic. Neo4J, like other tools, support finding all paths. But validation which paths (the shorter\nor any of the longer) is biologically most relevant first depends on actually more biological\nknowledge to become FAIR. After this, it is just push button.

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References

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Slenter, D., Kutmon, M., Evelo, C. T., & Willighagen, E. L. (2024). Discovering life’s directed metabolic (sub)paths to interpret human biochemical markers using the DSMN tool. Digital Discovery, 3(1), 81–98. https://doi.org/10.1039/d3dd00069a CrossRef Scholia
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", "summary": "I am still catching up with a lot of work, and found out I actually had forgotten to blog about this cool article by Denise Slenter: “Discovering life’s directed metabolic (sub)paths to interpret human biochemical markers using the DSMN tool” (doi:10.1039/D3DD00069A). This paper explains how various open science resources (Wikidata, Reactome, WikiPathways) are used to visualize the biological story of the data from two metabolomics experiments archived in MetaboLights.", "image": "https://chem-bla-ics.linkedchemistry.info/assets/images/d3dd00069a-f12_hi-res.png", "date_published": "2024-07-31T00:00:00+00:00", "date_modified": "2024-07-31T00:00:00+00:00", "tags": ["wikipathways","metabolomics"], "_references": [ { "url": "https://doi.org/10.1039/D3DD00069A", "doi": "10.1039/D3DD00069A" } ], "authors": [ { "name": "Egon Willighagen", "url": "https://orcid.org/0000-0001-7542-0286" } ] } ] }