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Transcriptogramer: an R/Bioconductor package for transcriptional analysis based on protein-protein interaction.

Diego A A Morais1, Rita M C Almeida2, Rodrigo J S Dalmolin1,3

  • 1Bioinformatics Multidisciplinary Environment, Federal University of Rio Grande do Norte, Natal, RN, Brazil.

Bioinformatics (Oxford, England)
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Summary

The transcriptogramer R package analyzes gene expression data by focusing on functionally related genes. It identifies altered biological systems in case-control studies, offering a systems-level view of transcriptomic changes.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Existing tools often analyze gene expression variations individually.
  • There is a need for methods that assess expression profiles of entire genetic systems.
  • Understanding functionally associated gene expression is crucial for biological insights.

Purpose of the Study:

  • To introduce the transcriptogramer R package for analyzing functionally associated gene expression.
  • To enable topological analysis, differential expression, and gene ontology enrichment.
  • To provide a systems-level approach for interpreting transcriptomic data in case-control studies.

Main Methods:

  • The transcriptogramer R package utilizes protein-protein interaction networks.
  • It projects gene expression values onto an ordered gene list for topological analysis.
  • The package performs differential expression and gene ontology enrichment analysis.

Main Results:

  • The transcriptogramer package assesses the expression profile of entire genetic systems.
  • It effectively reveals significantly altered biological systems in case-control transcriptome experiments.
  • The analysis is independent of the data platform or operating system.

Conclusions:

  • Transcriptogramer offers a novel approach to analyze differential gene expression within biological systems.
  • The package facilitates a deeper understanding of transcriptomic alterations in complex biological experiments.
  • It provides a versatile tool for systems-level analysis of gene expression data.