ImmCellFie: A user-friendly web-based platform to infer metabolic function from omics data

Helen O. Masson; David Borland; Jason Reilly; Adrian Telleria; Shalki Shrivastava; Matt Watson; Luthfi Bustillos; Zerong Li; Laura Capps; Benjamin P. Kellman; Zachary A. King; Anne Richelle; Nathan E. Lewis; Kimberly Robasky

STAR Protocols (Mar 2023)

ImmCellFie: A user-friendly web-based platform to infer metabolic function from omics data

Helen O. Masson,
David Borland,
Jason Reilly,
Adrian Telleria,
Shalki Shrivastava,
Matt Watson,
Luthfi Bustillos,
Zerong Li,
Laura Capps,
Benjamin P. Kellman,
Zachary A. King,
Anne Richelle,
Nathan E. Lewis,
Kimberly Robasky

Affiliations

Helen O. Masson: Department of Bioengineering, UC San Diego, La Jolla, CA 92093, USA
David Borland: Renaissance Computing Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA
Jason Reilly: Renaissance Computing Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA
Adrian Telleria: Renaissance Computing Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA
Shalki Shrivastava: Renaissance Computing Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA
Matt Watson: Renaissance Computing Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA
Luthfi Bustillos: Renaissance Computing Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA
Zerong Li: Department of Pediatrics, UC San Diego, La Jolla, CA 92093, USA
Laura Capps: Renaissance Computing Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA
Benjamin P. Kellman: Bioinformatics and Systems Biology Program, UC San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, UC San Diego, La Jolla, CA 92093, USA
Zachary A. King: Department of Bioengineering, UC San Diego, La Jolla, CA 92093, USA
Anne Richelle: Department of Pediatrics, UC San Diego, La Jolla, CA 92093, USA
Nathan E. Lewis: Department of Bioengineering, UC San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, UC San Diego, La Jolla, CA 92093, USA; Corresponding author
Kimberly Robasky: Renaissance Computing Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel H0069ll, NC 27514, USA; School of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Carolina Health and Informatics Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Corresponding author

Journal volume & issue: Vol. 4, no. 1
p. 102069

Abstract

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Summary: Understanding cellular metabolism is important across biotechnology and biomedical research and has critical implications in a broad range of normal and pathological conditions. Here, we introduce the user-friendly web-based platform ImmCellFie, which allows the comprehensive analysis of metabolic functions inferred from transcriptomic or proteomic data. We explain how to set up a run using publicly available omics data and how to visualize the results. The ImmCellFie algorithm pushes beyond conventional statistical enrichment and incorporates complex biological mechanisms to quantify cell activity.For complete details on the use and execution of this protocol, please refer to Richelle et al. (2021).1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Published in STAR Protocols

ISSN: 2666-1667 (Online)
Publisher: Elsevier
Country of publisher: United States
LCC subjects: Science: Science (General)
Website: https://star-protocols.cell.com/

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