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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
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Subgrouping Automata: automatic sequence subgrouping using phylogenetic tree-based optimum subgrouping algorithm.

Joo-Hyun Seo1, Jihyang Park2, Eun-Mi Kim2

  • 1School of Chemical and Biological Engineering, Seoul National University, Seoul 151-742, Republic of Korea; School of Computational Sciences, Korea Institute of Advanced Study, Seoul 130-722, Republic of Korea.

Computational Biology and Chemistry
|January 1, 2014
PubMed
Summary

This study introduces Subgrouping Automata (SA), a novel algorithm for automatic sequence subgrouping. SA identifies optimal identity thresholds to accurately group sequences, preventing under- and over-subgrouping in phylogenetic analysis.

Keywords:
Optimum subgrouping nodePhylogenetic treeProtein family discriminationStatistical analysisSubgrouping

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Sequence subgrouping is crucial for functional analysis and inference.
  • Existing methods lack robustness due to varying optimal identity thresholds.
  • A need exists for automated algorithms to determine optimal subgrouping parameters.

Purpose of the Study:

  • To develop an automated sequence subgrouping method, 'Subgrouping Automata' (SA).
  • To enable robust identification of optimal identity thresholds for sequence datasets.
  • To generate accurate sequence subgroups and prevent common subgrouping errors.

Main Methods:

  • Utilized a tree analysis module to identify potential subgroups.
  • Employed a sequence similarity analysis module to calculate average similarities.
  • Integrated a representative sequence generation module using profile analysis.
  • Applied Student's t-value to identify the node with maximum similarity increase for optimal subgrouping.

Main Results:

  • Subgrouping Automata successfully identified optimal subgrouping nodes in phylogenetic trees.
  • The method demonstrated prevention of both under-subgrouping and over-subgrouping.
  • Statistically significant increases in sequence similarity were used to define optimal subgroups.

Conclusions:

  • Subgrouping Automata provides a robust and automated approach to sequence subgrouping.
  • The algorithm effectively determines optimal identity thresholds, improving accuracy in functional analysis.
  • SA enhances the reliability of sequence classification and inference tasks.