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A flexible protocol for targeted gene co-expression network analysis.

Diana Coman1, Philipp Rütimann, Wilhelm Gruissem

  • 1Department of Biology, Plant Biotechnology, ETH Zurich, Universitätstrasse 2, 8092, Zurich, Switzerland.

Methods in Molecular Biology (Clifton, N.J.)
|April 30, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a versatile workflow for building gene co-expression networks from transcriptomic data. It enables the prediction of functionally related gene modules using publicly available datasets, aiding experimental research.

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Gene co-expression network inference is crucial for generating hypotheses in biological research.
  • High-throughput transcript profiling, including next-generation sequencing (NGS), provides abundant data for in silico network reconstruction.
  • Similar gene expression patterns often indicate shared functions and involvement in the same biological pathways.

Purpose of the Study:

  • To present a detailed, platform-independent workflow for analyzing gene co-expression networks.
  • To guide researchers through critical steps from experimental design and data analysis to predicting functionally related gene modules.
  • To enable the inference of statistically significant gene co-expression networks from diverse transcriptomic datasets.

Main Methods:

  • Utilizing large-scale gene expression data from various sources like microarrays and RNA sequencing.
  • Implementing a detailed protocol for analyzing transcriptomic data to identify co-expressed gene modules.
  • A platform-independent approach applicable to any organism and data type.

Main Results:

  • A robust workflow for inferring statistically significant gene co-expression networks.
  • The ability to predict functionally related gene modules from large-scale transcriptomic datasets.
  • A protocol that is independent of specific hardware or data generation platforms.

Conclusions:

  • The described workflow provides a valuable resource for hypothesis generation in experimental biology.
  • This method facilitates the reconstruction of regulatory networks using readily available transcriptomic data.
  • The protocol is broadly applicable, requiring no specialized hardware and supporting various data types.