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Updated: Mar 8, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
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Sequence comparison and essential gene identification with new inter-nucleotide distance sequences.

Yushuang Li1, Yanfen Lv1, Xiaonan Li2

  • 1School of Science, Yanshan University, Qinhuangdao 066004, PR China.

Journal of Theoretical Biology
|February 1, 2017
PubMed
Summary

New DNA sequence analysis methods define novel inter-nucleotide distances for improved sequence comparison and essential gene identification. These techniques enhance phylogenetic tree accuracy and bacterial gene classification.

Keywords:
Essential gene identificationFeature vectorInter-nucleotide distance sequenceSequence comparisonStatistical quantitySupport vector machine

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Existing methods for analyzing DNA sequences have limitations, particularly in capturing global inter-nucleotide distance properties.
  • The development of robust sequence descriptors is crucial for accurate genomic comparisons and functional gene identification.

Purpose of the Study:

  • To introduce four novel inter-nucleotide distance sequences for DNA analysis.
  • To develop a simple yet effective mathematical descriptor for DNA sequences.
  • To apply this descriptor for comparing mitochondrial genomes, ribosomal RNA sequences, and identifying essential bacterial genes.

Main Methods:

  • Definition of four new inter-nucleotide distance sequences, addressing limitations of previous methods.
  • Extraction of five statistical quantities from ordered inter-nucleotide distance sequences to create a 20-dimensional feature vector.
  • Utilizing Euclidean distance for comparing whole mitochondrial genomes and 16S ribosomal RNA sequences.
  • Employing a support vector machine (SVM) model with feature vectors to identify essential and non-essential genes in bacteria.

Main Results:

  • The new inter-nucleotide distance sequences overcome defects in prior global sequence descriptors.
  • Phylogenetic trees derived from feature vector comparisons show strong agreement with established studies.
  • The SVM-based method achieved high AUC values (0.7971-0.8751) for essential gene identification in bacteria, outperforming some existing methods.

Conclusions:

  • The proposed inter-nucleotide distance sequences and feature vector provide a powerful tool for DNA sequence analysis.
  • This approach facilitates accurate genomic comparisons and robust essential gene identification.
  • The method demonstrates significant potential for advancing bioinformatics and genomics research.