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leapR: An R Package for Multiomic Pathway Analysis.

Vincent Danna1, Hugh Mitchell1, Lindsey Anderson1

  • 1Computational Biology Group, Pacific Northwest National Laboratory, Richland, Washington 99352, United States.

Journal of Proteome Research
|March 11, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces leapR, a novel R package designed to streamline the analysis of high-throughput biological data. leapR facilitates rapid assessment of biological pathway activity by integrating diverse statistical tests and multi-omics data sources for hypothesis generation.

Keywords:
data integrationpathway analysisphosphoproteomicsproteomics

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • High-throughput data studies aim to uncover functional mechanisms behind biological phenomena.
  • Integrating multi-omics data (genomics, transcriptomics, proteomics, metabolomics) presents challenges in data summarization and hypothesis generation.
  • Evaluating numerous statistical methods across diverse data sources is time-consuming.

Purpose of the Study:

  • To introduce the leapR package, a framework for rapid assessment of biological pathway activity.
  • To enable facile integration of multisource high-throughput data.
  • To accelerate hypothesis generation in biological studies.

Main Methods:

  • Development of the leapR R package.
  • Implementation of a framework for assessing biological pathway activity.
  • Utilizing diverse statistical tests and integrating multisource data.

Main Results:

  • The leapR package provides a rapid and efficient method for analyzing high-throughput biological data.
  • Facilitates the integration of genomic, transcriptomic, proteomic, and metabolomic data.
  • Enables quicker generation of testable hypotheses from complex datasets.

Conclusions:

  • leapR simplifies and accelerates the process of identifying functional mechanisms from multi-omics data.
  • The package offers a valuable tool for researchers working with high-throughput biological studies.
  • leapR is available on GitHub with a user manual and example workflow.