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A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools
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BABAR: an R package to simplify the normalisation of common reference design microarray-based transcriptomic

Mark J Alston1, John Seers, Jay C D Hinton

  • 1Foodborne Bacterial Pathogens, Institute of Food Research, Norwich Research Park, Norwich, NR4 7UA, UK. mark.alston@bbsrc.ac.uk

BMC Bioinformatics
|February 5, 2010
PubMed
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The Batch Anti-Banana Algorithm in R (BABAR) normalizes heterogeneous microarray datasets, enabling accurate gene expression analysis and network inference for systems-level biological studies.

Area of Science:

  • Transcriptomics
  • Bioinformatics
  • Systems Biology

Background:

  • DNA microarrays generate vast transcriptomic datasets, ideal for systems-level analyses.
  • Heterogeneous datasets from different years, formats, and experimental conditions pose normalization challenges.
  • Existing normalization methods struggle with heterogeneous microarray data, limiting re-analysis.

Purpose of the Study:

  • To develop a robust algorithm for normalizing heterogeneous microarray datasets.
  • To enable accurate comparison and re-analysis of transcriptomic data.
  • To facilitate gene expression and network inference studies.

Main Methods:

  • Developed the Batch Anti-Banana Algorithm in R (BABAR) software package.
  • Utilized cyclic loess for across-dataset normalization.

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Last Updated: Jun 16, 2026

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools
09:29

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

Published on: August 21, 2019

  • Input processed unprocessed GenePix or BlueFuse microarray data files.

    • Main Results:

    • BABAR successfully normalized heterogeneous microarray datasets, achieving comparable scaling.
    • Normalized data showed excellent agreement with RT-PCR analysis.
    • BABAR demonstrated benefits over standard techniques for identifying differentially expressed genes in simulated and real datasets.

    Conclusions:

    • BABAR is an easy-to-use tool for normalizing heterogeneous two-colour cDNA microarray datasets.
    • BABAR transforms data for correct interpretation, aiding gene expression identification.
    • BABAR is ideal for network inference analysis from transcriptomic datasets.