Phylogenetic Tree Construction Using K-Mer Forest- Based Distance Calculation

Gihan Gamage, Nadeeshan Gimhana, Indika Perera, Shanaka Bandara, Thilina Pathirana, Anuradha Wickramarachchi, Vijini Mallawaarachchi

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
5 Downloads (Pure)

Abstract

Phylogenetics is one of the dominant data engineering research disciplines based on biological information. More particularly here, we consider raw DNA sequences and do comparative analysis in order to come up with meaningful conclusions. When representing evolutionary relationships among different organisms in a concise manner, the phylogenetic tree helps significantly. When constructing phylogenetic trees, the elementary step is to calculate the genetic distance among species. Alignment-based sequencing and alignment-free sequencing are the two leading distance computation methods that are used to find genetic relatedness of different species. In this paper, we propose a novel alignment-free, pairwise, distance calculation method based on k-mers and a state of art machine learning-based phylogenetic tree construction mechanism. With the proposed approach, we can convert longer DNA sequences into compendious k-mer forests which gear up the efficiency of comparison. Later we construct the phylogenetic tree based on calculated distances with the help of an algorithm build upon k-medoid clustering, which guaranteed significant efficiency and accuracy compared to traditional phylogenetic tree construction methods.

Original languageEnglish
Pages (from-to)4-20
Number of pages17
JournalInternational journal of online and biomedical engineering
Volume16
Issue number7
DOIs
Publication statusPublished - 19 Jun 2020
Externally publishedYes

Keywords

  • Genetic distance
  • Genetic relatedness
  • K-medoid clustering
  • K-mer forest
  • Phylogenetics

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