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Updated: Jun 2, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Multiple sequence alignment based on dynamic weighted guidance tree.

Ken D Nguyen1, Yi Pan

  • 1Department of Computer Science, Georgia State University, Atlanta, GA 30303-3994, USA. knguyen@cs.gsu.edu

International Journal of Bioinformatics Research and Applications
|May 18, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multiple sequence alignment strategy that improves accuracy by up to 10%. The method uses sequence information and similarities to dynamically reorder alignment trees for better results.

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Last Updated: Jun 2, 2026

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Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
07:49

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Multiple sequence alignment (MSA) is crucial for identifying biological functions, structures, and evolutionary relationships across species.
  • Existing MSA tools face challenges in accurately reflecting sequence information and similarities.

Purpose of the Study:

  • To develop an advanced multiple sequence alignment strategy that enhances alignment accuracy.
  • To introduce a dynamic alignment guiding tree that optimizes alignment scores.

Main Methods:

  • A novel strategy was developed to extract sequence information, global, and local similarities.
  • Input sequences are assigned weights based on extracted information.
  • A weighted pair-wise distance matrix is computed to construct a dynamic alignment guiding tree.
  • The tree dynamically reorders branches based on lower-level alignment results to maximize scores.

Main Results:

  • The proposed technique demonstrated improved alignment accuracy, achieving up to a 10% increase on tested benchmarks.
  • Performance was evaluated against established tools like CLUSTALW, DIALIGN, T-COFFEE, MUSCLE, and PROBCONS.
  • The dynamic tree reordering significantly contributed to higher alignment scores.

Conclusions:

  • The new weighted multiple sequence alignment strategy offers a significant improvement in accuracy.
  • This method provides a more robust approach to analyzing sequence data in bioinformatics.
  • The dynamic alignment guiding tree is a key innovation for optimizing MSA results.