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Fast phylogenetic inference from typing data.

João A Carriço1, Maxime Crochemore2, Alexandre P Francisco3,4

  • 11Faculdade de Medicina, Instituto de Microbiologia and Instituto de Medicina Molecular, Universidade de Lisboa, Lisboa, Portugal.

Algorithms for Molecular Biology : AMB
|February 23, 2018
PubMed
Summary
This summary is machine-generated.

We developed a fast algorithm to compute pairwise Hamming distances, crucial for analyzing bacterial strain relatedness and speeding up phylogenetic analysis. This method enhances large-scale microbial typing database queries.

Keywords:
Computational biologyHamming distancePhylogenetic inference

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Microbial Genomics

Background:

  • Microbial typing methods, particularly sequence-based approaches, are vital for epidemiological surveillance and understanding bacterial strain relatedness.
  • Large databases of microbial typing profiles are accumulating rapidly due to High Throughput Sequencing.
  • Calculating genetic evolutionary distances, often relying on pairwise Hamming distances, is computationally intensive and limits phylogenetic inference speed.

Purpose of the Study:

  • To introduce an efficient algorithm for computing pairwise Hamming distances among microbial taxa.
  • To demonstrate the algorithm's integration into phylogenetic inference methods.
  • To accelerate querying local phylogenetic patterns within extensive typing databases.

Main Methods:

  • Development of an average-case linear-time algorithm for computing pairwise Hamming distances.
  • Theoretical analysis of the algorithm's performance.
  • Experimental validation and integration into a known phylogenetic inference method.

Main Results:

  • The proposed algorithm achieves average-case linear time complexity for computing pairwise Hamming distances.
  • Experimental results confirm the algorithm's efficiency and effectiveness.
  • Successful integration into a phylogenetic inference method significantly speeds up analysis.

Conclusions:

  • The novel algorithm offers a substantial improvement in computing pairwise Hamming distances for large datasets.
  • This advancement facilitates faster and more scalable phylogenetic analyses in microbial epidemiology.
  • The method enhances the utility of large microbial typing databases for research and surveillance.