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Fast randomization of large genomic datasets while preserving alteration counts.

Andrea Gobbi1, Francesco Iorio2, Kevin J Dawson1

  • 1Fondazione Bruno Kessler, I-38100 Povo (Trento), Italy, European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge CB10 1SD, UK, Wellcome Trust Sanger Institute, Cambridge CB10 1SD, UK and Universitat Pompeu Fabra, Barcelona 08003, Spain.

Bioinformatics (Oxford, England)
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Summary

This study introduces BiRewire, an R package that significantly speeds up the analysis of cancer genomic data by optimizing the switching-algorithm for bipartite network analysis. This enables more efficient identification of cancer driver networks.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Combinatorial patterns in cancer genomics aid in identifying novel cancer driver networks.
  • Statistical significance of these patterns is often assessed using P-values under null models.
  • The switching-algorithm, a Monte Carlo method, is computationally expensive for simulating genomic datasets.

Purpose of the Study:

  • To develop a computationally efficient method for analyzing combinatorial patterns in cancer genomic data.
  • To improve the sampling process within the switching-algorithm for bipartite network analysis.
  • To provide a tool for faster and more accurate identification of cancer driver networks.

Main Methods:

  • Analytically derived a novel approximate lower bound for the number of switching-steps.
  • Developed the R package BiRewire with efficient implementations of the switching-algorithm.
  • Applied BiRewire to large-scale cancer genomics datasets.

Main Results:

  • Achieved significant reductions in computation time compared to existing methods.
  • Maintained equivalent P-value computations for statistical significance.
  • Demonstrated the package's performance on real-world cancer genomics data.

Conclusions:

  • BiRewire offers a computationally efficient solution for studying statistical properties in genomic datasets.
  • The package facilitates the identification of cancer driver networks through improved bipartite network analysis.
  • BiRewire is a valuable tool for researchers working with large genomic datasets and bipartite network models.