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Fast parallel construction of variable-length Markov chains.

Joel Gustafsson1, Peter Norberg2, Jan R Qvick-Wester3

  • 1Institute of Biomedicine, Department of Infectious Diseases, University of Gothenburg, Gothenburg, Sweden. joel.gustafsson@gu.se.

BMC Bioinformatics
|October 10, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a faster parallel implementation for constructing variable-length Markov chains (VLMCs) for large genome comparisons. The new method significantly outperforms existing tools, offering practical advantages for molecular biology research.

Keywords:
Alignment-freeParallel algorithmsSequence analysisVariable-length Markov chain

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Alignment-free sequence comparison methods are crucial for analyzing large biological datasets.
  • Variable-length Markov chains (VLMCs) offer an efficient approach to sequence analysis by adapting model depth to sequence context.
  • Existing VLMC software lacks speed and parallel processing capabilities, hindering analysis of large genomes.

Purpose of the Study:

  • To develop a fast and parallelizable software tool for constructing VLMCs.
  • To improve the efficiency of comparing large biological sequences using VLMCs.
  • To provide guidance on parameter selection for VLMC model building.

Main Methods:

  • Developed a parallel implementation of VLMCs utilizing lazy suffix trees and a hash-based approach.
  • Evaluated performance on genomes ranging from 12 Mbp to 22 Gbp.
  • Employed the Bayesian Information Criterion (BIC) for selecting optimal learning parameters to prevent over-fitting.

Main Results:

  • The parallel VLMC implementation demonstrates significant speed improvements over state-of-the-art methods, even in serial execution.
  • Achieved excellent parallel scaling, with speed-ups approaching theoretical limits (Amdahl's Law) on multi-threaded systems.
  • Successfully constructed VLMCs for very large genomes, a task previously computationally prohibitive.

Conclusions:

  • The open-source parallel VLMC tool offers a practical and significantly faster alternative for large-scale genome analysis.
  • The BIC-guided parameter selection provides valuable assistance for researchers comparing genomes.
  • The developed software facilitates advanced sequence analysis in molecular biology.