Exploring the Application of Models of DNA Evolution to Normalized Compression Distance (NCD) Matrices

Damian Moreno, Hongzhi Hu, Thiruvarangan Ramaraj () and John Rogers ()
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Damian Moreno: School of Computing, Jarvis College of Computing and Digital Media, DePaul University, Chicago, IL 60614, USA
Hongzhi Hu: School of Computing, Jarvis College of Computing and Digital Media, DePaul University, Chicago, IL 60614, USA
Thiruvarangan Ramaraj: School of Computing, Jarvis College of Computing and Digital Media, DePaul University, Chicago, IL 60614, USA
John Rogers: School of Computing, Jarvis College of Computing and Digital Media, DePaul University, Chicago, IL 60614, USA

Mathematics, 2025, vol. 13, issue 21, 1-15

Abstract: Recent developments of Normalized Compressed Distance (NCD) matrices show potential to become a widely used method to develop phylogenetic trees among a group of organisms. However, such NCD matrices lack the biological and evolutionary context that is important to the development of phylogenetic trees. This study applied mathematical models of DNA evolution to NCD matrices with the goal of applying evolutionary context to improve the results of NCD methods and thus create better phylogenetic trees. Mammalian mitochondrial , Arabidopsis thaliana , and Tomato Solanum lycopersicum genomes were used in this study as benchmarks and underwent validation techniques to analyze whether the NCD matrix models were of better quality than their original NCD. The mathematical models applied were not sufficient in implementing a biological context towards NCD compression algorithms for phylogenetic trees, resulting in no successful improvement of them. However, this opens opportunities to explore ways to integrate biological context internally to NCD compression algorithms, rather than external methods of correcting NCD matrices after compression has been carried out.

Keywords: phylogenetics; NCD; whole genomes; bioinformatics; DNA evolution models (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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