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A Method for Cross-Species Visualization and Analysis of RNA-Sequence Data.

Stephen A Ramsey1

  • 1Oregon State University, 106 Dryden Hall, Corvallis, OR, 97331, USA. stephen.ramsey@oregonstate.edu.

Methods in Molecular Biology (Clifton, N.J.)
|November 10, 2017
PubMed
Summary

This article presents a computational workflow for comparing transcriptome data across species, using gene set variation analysis (GSVA) in R. It enables cross-species visualization and analysis of mRNA sequencing data, demonstrated with canine and human bladder cancer examples.

Keywords:
BioinformaticsCross-speciesGene functionTranscriptomemRNA-seq

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Transcriptome profiling generates vast datasets requiring robust analytical methods.
  • Comparing gene expression patterns across species is crucial for understanding conserved biological processes and disease mechanisms.
  • Existing methods may lack specific tools for cross-species transcriptome comparison and visualization.

Purpose of the Study:

  • To introduce a novel computational workflow for cross-species analysis of mRNA sequencing (mRNA-seq) data.
  • To facilitate the visualization and comparison of transcriptome profiles between different species.
  • To provide a practical, step-by-step guide for implementing this workflow using the R programming language.

Main Methods:

  • The workflow utilizes Gene Set Variation Analysis (GSVA) for transcriptome profiling.
  • Implementation is demonstrated using commands in the R programming language.
  • The procedure is exemplified with mRNA-seq data from human and canine bladder cancer.

Main Results:

  • A reproducible computational workflow for cross-species transcriptome comparison is detailed.
  • The method allows for effective visualization of gene expression patterns across species.
  • The workflow is validated using real-world cancer datasets.

Conclusions:

  • This workflow offers a valuable tool for researchers studying comparative transcriptomics.
  • It enhances the ability to identify conserved and species-specific molecular signatures.
  • The R-based implementation ensures accessibility and adaptability for diverse research applications.