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Rediscover: an R package to identify mutually exclusive mutations.

Juan A Ferrer-Bonsoms1, Laura Jareno1, Angel Rubio1

  • 1Department of Biomedical Engineering and Sciences, TECNUN, University of Navarra, San Sebastian, Spain.

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Summary

Rediscover significantly speeds up the identification of mutually exclusive genomic events using optimized Poisson-Binomial calculations. This new R package offers faster, more efficient analysis for large datasets, improving genomic research capabilities.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Identifying mutually exclusive genomic events is crucial for understanding cancer development.
  • Existing algorithms like Discover, while effective, face computational challenges with large datasets due to the complexity of the Poisson-Binomial distribution.

Purpose of the Study:

  • To develop a faster and more efficient computational tool for identifying mutually exclusive genomic events.
  • To improve upon the performance of the Discover algorithm by optimizing Poisson-Binomial calculations.

Main Methods:

  • Implementation of both exact and approximate computational methods for the Poisson-Binomial distribution.
  • Development of the Rediscover R package, available on CRAN.
  • Integration capabilities with other R packages such as maftools and TCGAbiolinks.

Main Results:

  • Rediscover offers a slight speed improvement over Discover for large and medium datasets with its exact computation.
  • The approximate computation in Rediscover is 100-1000 times faster, enabling analysis in under a minute on standard hardware.
  • Rediscover demonstrates a reduced memory footprint compared to Discover.

Conclusions:

  • Rediscover provides a computationally efficient solution for identifying mutually exclusive genomic events.
  • The package enhances the feasibility of analyzing large-scale genomic datasets for discovering critical biological insights.
  • Rediscover is a valuable addition to the bioinformatics toolkit for cancer genomics research.