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leapR: An R Package for Multiomic Pathway Analysis

  • Vincent Danna
    Vincent Danna
    Computational Biology Group, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
  • Hugh Mitchell
    Hugh Mitchell
    Computational Biology Group, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
  • Lindsey Anderson
    Lindsey Anderson
    Computational Biology Group, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
  • Iobani Godinez
    Iobani Godinez
    Computational Biology Group, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
  • Sara J. C. Gosline
    Sara J. C. Gosline
    Computational Biology Group, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
  • Justin Teeguarden
    Justin Teeguarden
    Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
  • , and 
  • Jason E. McDermott*
    Jason E. McDermott
    Computational Biology Group, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
    Department of Molecular Microbiology and Immunology, Oregon Health & Sciences University, Portland, Oregon 97201, United States
    *Email: [email protected]
Cite this: J. Proteome Res. 2021, 20, 4, 2116–2121
Publication Date (Web):March 11, 2021
https://doi.org/10.1021/acs.jproteome.0c00963
Copyright © 2021 American Chemical Society

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    Abstract

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    A generalized goal of many high-throughput data studies is to identify functional mechanisms that underlie observed biological phenomena, whether they be disease outcomes or metabolic output. Increasingly, studies that rely on multiple sources of high-throughput data (genomic, transcriptomic, proteomic, metabolomic) are faced with a challenge of summarizing the data to generate testable hypotheses. However, this requires a time-consuming process to evaluate numerous statistical methods across numerous data sources. Here, we introduce the leapR package, a framework to rapidly assess biological pathway activity using diverse statistical tests and data sources, allowing facile integration of multisource data. The leapR package with a user manual and example workflow is available for download from GitHub (https://github.com/biodataganache/leapR).

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    Cited By

    This article is cited by 5 publications.

    1. Jason E. McDermott, Jon M. Jacobs, Nathaniel J. Merrill, Hugh D. Mitchell, Osama A. Arshad, Ryan McClure, Justin Teeguarden, Rajendra P. Gajula, Kenneth I. Porter, Brieann C. Satterfield, Kirsie R. Lundholm, Debra J. Skene, Shobhan Gaddameedhi, Hans P. A. Van Dongen. Molecular-Level Dysregulation of Insulin Pathways and Inflammatory Processes in Peripheral Blood Mononuclear Cells by Circadian Misalignment. Journal of Proteome Research 2024, 23 (5) , 1547-1558. https://doi.org/10.1021/acs.jproteome.3c00418
    2. Mukta G. Palshikar, Xiaojun Min, Alexander Crystal, Jiayue Meng, Shannon P. Hilchey, Martin S. Zand, Juilee Thakar. Executable Network Models of Integrated Multiomics Data. Journal of Proteome Research 2023, 22 (5) , 1546-1556. https://doi.org/10.1021/acs.jproteome.2c00730
    3. Sara J.C. Gosline, Marija Veličković, James C. Pino, Le Z. Day, Isaac K. Attah, Adam C. Swensen, Vincent Danna, Camilo Posso, Karin D. Rodland, Jing Chen, Clayton E. Matthews, Martha Campbell-Thompson, Julia Laskin, Kristin Burnum-Johnson, Ying Zhu, Paul D. Piehowski. Proteome Mapping of the Human Pancreatic Islet Microenvironment Reveals Endocrine–Exocrine Signaling Sphere of Influence. Molecular & Cellular Proteomics 2023, 22 (8) , 100592. https://doi.org/10.1016/j.mcpro.2023.100592
    4. Sara J. C. Gosline, Cristina Tognon, Michael Nestor, Sunil Joshi, Rucha Modak, Alisa Damnernsawad, Camilo Posso, Jamie Moon, Joshua R. Hansen, Chelsea Hutchinson-Bunch, James C. Pino, Marina A. Gritsenko, Karl K. Weitz, Elie Traer, Jeffrey Tyner, Brian Druker, Anupriya Agarwal, Paul Piehowski, Jason E. McDermott, Karin Rodland. Proteomic and phosphoproteomic measurements enhance ability to predict ex vivo drug response in AML. Clinical Proteomics 2022, 19 (1) https://doi.org/10.1186/s12014-022-09367-9
    5. Bayan Hassan Banimfreg, Abdulrahim Shamayleh, Hussam Alshraideh. Survey for Computer-Aided Tools and Databases in Metabolomics. Metabolites 2022, 12 (10) , 1002. https://doi.org/10.3390/metabo12101002

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