{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:19:30Z","timestamp":1786493970390,"version":"build-2736575974"},"reference-count":60,"publisher":"American Chemical Society (ACS)","issue":"31","license":[{"start":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T00:00:00Z","timestamp":1785369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/na","name":"Robert A. 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MetaboGraph integrates automated data cleaning, comprehensive multidatabase metabolite and lipid annotation, pathway mapping, and direction-aware pathway inference. A central feature of the platform is its ability to predict pathway direction by integrating metabolite\/lipid-level fold changes with pathway membership structure, supporting biologically interpretable pathway and network analyses beyond conventional enrichment approaches. MetaboGraph supports multiomics integration and comparative analysis, enabling consistent pathway-level interpretation across metabolomics, lipidomics, and multiple studies. We demonstrate the platform using untargeted LC-MS\/MS metabolomics and lipidomics data comparing two breast cancer cell lines with distinct metastatic potential, MCF7 (HTB22; less metastatic) and MDA-MB-453 (HTB131; more metastatic). Relative to HTB22, the HTB131 cells exhibited coordinated metabolic remodeling, including altered amino acid and nitrogen metabolism, increased nucleotide biosynthetic demand, lipid remodeling, and changes in energy-associated pathways. These pathway-level alterations are consistent with established metabolic adaptations associated with increased cancer aggression. MetaboGraph expands the analytical toolbox for small-molecule biology and facilitates reproducible, biologically grounded insights from metabolomics and lipidomics data sets.<\/jats:p>","DOI":"10.1021\/acs.analchem.6c01446","type":"journal-article","created":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T13:23:22Z","timestamp":1785849802000},"page":"22755-22770","source":"Crossref","is-referenced-by-count":1,"title":["MetaboGraph:\nA Framework for Metabolomics and Lipidomics\nAnnotation and Pathway Network Analysis"],"prefix":"10.1021","volume":"98","author":[{"given":"Oluwatosin","family":"Daramola","sequence":"first","affiliation":[{"name":"Texas Tech University , , , ,","place":["Lubbock, Texas, United States, 79409-1061"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Judith","family":"Nwaiwu","sequence":"additional","affiliation":[{"name":"Texas Tech University , , , ,","place":["Lubbock, Texas, United States, 79409-1061"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Odunayo","family":"Oluokun","sequence":"additional","affiliation":[{"name":"Texas Tech University , , , ,","place":["Lubbock, Texas, United States, 79409-1061"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mojibola","family":"Fowowe","sequence":"additional","affiliation":[{"name":"Texas Tech University , , , ,","place":["Lubbock, Texas, United States, 79409-1061"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6661-6073","authenticated-orcid":true,"given":"Yehia","family":"Mechref","sequence":"additional","affiliation":[{"name":"Texas Tech University , , , ,","place":["Lubbock, Texas, United States, 79409-1061"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,7,30]]},"reference":[{"key":"2026081109102744000_cit1","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1007\/978-1-0716-1266-8_27","article-title":"A Protocol for Untargeted\nMetabolomic Analysis: From\nSample Preparation to Data Processing","volume":"2276","author":"Souza","year":"2021","journal-title":"Methods\nMol. 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