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Learning gene regulatory networks from next generation sequencing data.

Bochao Jia1, Suwa Xu1, Guanghua Xiao2

  • 1Department of Biostatistics, University of Florida, Gainesville, Florida, U.S.A.

Biometrics
|March 16, 2017
PubMed
Summary
This summary is machine-generated.

Next generation sequencing (NGS) data is discrete and challenges gene regulatory network reconstruction. This study introduces a novel transformation method to accurately infer these networks from NGS data.

Keywords:
Data-continuized transformationGaussian graphical modelGene regulatory networkPoisson graphical modelRNA-seq

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Next generation sequencing (NGS) is replacing microarrays for gene expression measurement due to higher throughput and lower noise.
  • The discrete nature of NGS data poses challenges for existing statistical methods, particularly for gene regulatory network (GRN) reconstruction.
  • Current methods, like the local Poisson graphical model, have limitations in consistency and inferring global network structures.

Purpose of the Study:

  • To develop a robust statistical method for reconstructing gene regulatory networks using discrete NGS data.
  • To address the limitations of existing methods in handling the discreteness of NGS data.
  • To provide a consistent and accurate approach for GRN inference from NGS data.

Main Methods:

  • A random effect model-based transformation is proposed to convert discrete NGS data into continuous data.
  • A semiparametric transformation is applied to further transform the data to a Gaussian distribution.
  • An equivalent partial correlation selection method is utilized for GRN reconstruction.

Main Results:

  • The proposed method demonstrates consistency in gene regulatory network inference.
  • Numerical results show significantly higher accuracy in GRN reconstruction compared to the local Poisson graphical model and other existing methods.
  • The data-continuization transformation successfully bridges the gap in handling discrete data for NGS analysis.

Conclusions:

  • The developed method provides a theoretically sound and practically effective approach for gene regulatory network reconstruction from NGS data.
  • The data-continuization transformation facilitates accurate NGS data analysis and enables the integration of diverse data types (e.g., microarray, RNA-seq) for GRN inference.
  • This work advances the statistical methodology for analyzing high-throughput sequencing data in systems biology.