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Scalable Genomics with R and Bioconductor.

Michael Lawrence1, Martin Morgan2

  • 1Genentech, 1 DNA Way, South San Francisco, California 94080, USA michafla@gene.com.

Statistical Science : a Review Journal of the Institute of Mathematical Statistics
|December 27, 2016
PubMed
Summary
This summary is machine-generated.

This study reviews scalable strategies for analyzing large genomic datasets using R and Bioconductor packages. These methods enable efficient processing, summarization, and visualization of big genomic data for genetic variant analysis.

Keywords:
BioconductorRbig databiologygenomics

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Analyzing large genomic datasets presents significant computational challenges.
  • Scalable processing, summarization, and visualization are crucial for extracting meaningful insights from big genomic data.

Purpose of the Study:

  • To review and demonstrate strategies for overcoming challenges in large-scale genomic data analysis.
  • To showcase the implementation of these strategies using R and Bioconductor packages.

Main Methods:

  • Review of established computational strategies including restrictive queries, data compression, iteration, and parallel computing.
  • Application of Bioconductor packages in R for the analysis of genetic variants.
  • Utilizing whole genome sequencing data for demonstration.

Main Results:

  • Demonstrated effective implementation of scalable strategies for big genomic data.
  • Successfully applied Bioconductor packages for genetic variant detection and analysis.
  • Showcased efficient processing, summarization, and visualization techniques.

Conclusions:

  • Bioconductor packages provide a robust framework for scalable analysis of large genomic datasets.
  • The reviewed strategies are effective for handling big genomic data challenges in genetic variant analysis.
  • R and Bioconductor offer powerful tools for modern genomic research.