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Linear-time computation of minimal absent words using suffix array.

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We introduce a new linear-time and linear-space algorithm for computing minimal absent words, a key measure for sequence comparison. Our implementation is faster than existing methods and publicly available.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • String Algorithms

Background:

  • Minimal absent words (MAWs) are words not present in a sequence, with all their substrings appearing in the sequence.
  • MAWs are valuable for sequence comparison and have been studied across all domains of life.
  • Existing algorithms for MAW computation include a suffix automaton-based method (O(n) time, O(n) space) and a suffix array-based method (O(n) time, O(n) space).

Purpose of the Study:

  • To present a novel linear-time and linear-space algorithm for computing all minimal absent words.
  • To provide an efficient open-source implementation of the proposed algorithm.
  • To address the gap in publicly available, high-performance implementations for MAW computation.

Main Methods:

  • Development of a new algorithm for minimal absent word computation utilizing suffix arrays.
  • Implementation of the algorithm in an open-source software package.
  • Experimental evaluation using both real and synthetic biological data.

Main Results:

  • The proposed algorithm achieves linear time and space complexity for computing minimal absent words.
  • The implemented algorithm demonstrates superior performance compared to the existing suffix array-based method by Pinho et al.
  • The open-source implementation is available for public use and further research.

Conclusions:

  • Minimal absent words offer a powerful alternative to traditional sequence comparison methods.
  • The new algorithm and its implementation provide a significant advancement in the efficient computation of minimal absent words.
  • This work facilitates broader application of MAWs in bioinformatics and related fields.