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MAMMLE: A Framework for Phylogeny Estimation Based on Multiobjective Application-aware Multiple Sequence Alignment

Muhammad Ali Nayeem1, Naser Anjum Samudro1, M Saifur Rahman1

  • 1Department of CSE, BUET, Dhaka, Bangladesh.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|January 27, 2023
PubMed
Summary
This summary is machine-generated.

We developed a new framework, Multiobjective Application-aware Multiple Sequence Alignment and Maximum Likelihood Ensemble (MAMMLE), to improve phylogenetic tree accuracy. MAMMLE enhances multiple sequence alignment (MSA) by combining tools and strategies for more reliable evolutionary inferences.

Keywords:
multiple alignmentphylogenetic treessoftware framework

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Phylogenetics

Background:

  • Phylogenetic tree accuracy heavily depends on the quality of multiple sequence alignment (MSA).
  • Optimizing MSA tools with application-specific objectives can significantly improve downstream phylogenetic inference.

Purpose of the Study:

  • To introduce a novel framework, MAMMLE, for enhanced phylogenetic tree reconstruction.
  • To improve the accuracy of phylogenetic trees inferred from unaligned sequences.

Main Methods:

  • Hybridizing two leading MSA tools: MUSCLE and MAFFT.
  • Employing a multiobjective optimization strategy.
  • Leveraging multiple maximum likelihood hypotheses for robust inference.

Main Results:

  • MAMMLE demonstrated a median improvement of 5.57% over MUSCLE in specific instances.
  • MAMMLE showed a median deterioration of 4.77% over MUSCLE in 37.41% of instances, indicating nuanced performance.

Conclusions:

  • The MAMMLE framework offers a promising approach to improve phylogenetic tree accuracy.
  • Multiobjective optimization and ensemble methods in MSA can lead to more reliable evolutionary analyses.