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emMAW: computing minimal absent words in external memory.

Alice Héliou1, Solon P Pissis2, Simon J Puglisi3

  • 1LIX, École Polytechnique, CNRS, INRIA, Université Paris-Saclay, Palaiseau, France.

Bioinformatics (Oxford, England)
|April 14, 2017
PubMed
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We developed emMAW, an external-memory algorithm for finding minimal absent words in large genomes. This tool efficiently processes massive datasets, overcoming previous RAM limitations for genomic analysis.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Minimal absent words hold biological significance across life's domains.
  • Existing algorithms for minimal absent words require substantial RAM, limiting their application to large datasets.
  • Previous methods struggle with the computational demands of analyzing extensive genomic sequences.

Purpose of the Study:

  • To introduce the first external-memory algorithm for computing minimal absent words.
  • To enable the analysis of significantly larger biological datasets than previously feasible.
  • To provide an efficient and accessible tool for researchers studying genomic sequences.

Main Methods:

  • Developed emMAW, an innovative external-memory algorithm.
  • Created a free, open-source implementation of the emMAW algorithm.

Related Experiment Videos

  • Utilized suffix arrays for efficient sequence analysis.

    • Main Results:

    • The emMAW algorithm successfully computes minimal absent words on large datasets, including the full human genome.
    • The implementation requires minimal RAM (1 GB) and processes the human genome in under 3 hours on a standard workstation.
    • Despite using external memory, the algorithm maintains competitive speed, performing comparably to internal-memory methods on smaller datasets.

    Conclusions:

    • emMAW significantly advances the computational feasibility of analyzing minimal absent words in large-scale genomic data.
    • The open-source availability of emMAW democratizes access to powerful genomic analysis tools.
    • This work overcomes previous memory constraints, paving the way for deeper insights into genomic structures and functions.