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petal: Co-expression network modelling in R.

Juli Petereit1, Sebastian Smith2, Frederick C Harris2

  • 1University of Nevada, Reno, 1664 N. Virginia Street, Reno, 89557, USA. julipetereit@gmail.com.

BMC Systems Biology
|August 5, 2016
PubMed
Summary
This summary is machine-generated.

We developed petal, a new tool for gene co-expression network analysis that works with RNA-seq data without assuming normality. This approach generates biologically meaningful networks for systems biology research.

Keywords:
Parameter-free algorithmRScale-freeSmall-worldWhole omics-approach

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Area of Science:

  • Systems Biology
  • Bioinformatics
  • Genomics

Background:

  • Gene co-expression network analysis is crucial for understanding complex biological systems.
  • High-throughput sequencing technologies generate large datasets requiring user-friendly analysis tools.
  • Existing tools often make assumptions not suitable for RNA-seq data.

Purpose of the Study:

  • To construct statistically strong and biologically meaningful gene co-expression networks.
  • To identify research-dependent subnetworks within large datasets.
  • To provide a user-friendly, whole-system approach to biological network analysis.

Main Methods:

  • Developed 'petal,' a novel approach for gene co-expression network modeling.
  • Focused on statistical, mathematical, and biological properties of data and models.
  • Constructed networks adhering to scale-free and small-world characteristics.
  • Ensured no user input is required beyond experimental data for reproducibility.

Main Results:

  • 'petal' does not assume data normality, making it suitable for RNA-seq data.
  • Generated biologically meaningful networks by incorporating scale-free and small-world properties.
  • Enabled reproducible results and simplified network analysis.
  • Facilitated a whole-system approach for large, high-throughput datasets.

Conclusions:

  • 'petal' is a novel R library for generating co-expression networks from whole-genome experiments.
  • Its application yields meaningful results and facilitates new hypothesis generation.
  • Provides a powerful tool for systems biology research and biological investigation.