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Using R and Bioconductor in Clinical Genomics and Transcriptomics.

Jorge L Sepulveda1

  • 1Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, New York; Informatics Subdivision Leadership, Association for Molecular Pathology, Bethesda, Maryland.

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R and Bioconductor offer robust tools for next-generation sequencing (NGS) data analysis. This review highlights their advantages and limitations for clinical bioinformatics, providing examples for various NGS analysis steps.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Transcriptomics

Background:

  • Next-generation sequencing (NGS) generates vast genomic and transcriptomic data requiring robust analysis pipelines.
  • Clinical use of NGS data necessitates rigorous validation, reproducibility, and quality assessment of bioinformatics pipelines.
  • The R statistical language and Bioconductor repository are widely adopted in research bioinformatics.

Purpose of the Study:

  • To introduce R and Bioconductor software for clinical bioinformatics.
  • To discuss the advantages and limitations of R and Bioconductor for clinical applications.
  • To provide examples of R/Bioconductor tools for various NGS data analysis steps.

Main Methods:

  • Review of R and Bioconductor software functionalities.
  • Discussion of software advantages for clinical bioinformatics: tool availability, modularity, installation ease, interoperability, version control, and learning curve.
  • Illustrative examples of tools for different stages of NGS analysis.

Main Results:

  • R and Bioconductor provide a powerful, flexible, and accessible framework for bioinformatics.
  • These tools offer significant benefits for clinical bioinformatics workflows.
  • Specific tools are available to address various analytical needs in NGS data processing.

Conclusions:

  • R and Bioconductor are well-suited for clinical bioinformatics due to their features and extensive toolset.
  • Adoption of these frameworks can enhance the quality and efficiency of clinical genomic and transcriptomic data analysis.
  • Further exploration and application of R/Bioconductor tools are encouraged for advancing clinical bioinformatics.