Data compression and genomes: a two dimensional life domain map

TL;DR

This paper introduces a method to classify genomes into Archaea, Bacteria, and Eukarya by analyzing the statistical distribution of DNA sequence complexity using data compression metrics.

Contribution

It presents a novel approach to distinguish life domains based on the skewness and kurtosis of DNA complexity distributions, using Lempel-Ziv compression.

Findings

Different domains occupy separate regions in the skewness-kurtosis space.

The method successfully differentiates among the three domains with preliminary data.

Complexity distribution metrics can serve as a genomic classification tool.

Abstract

We define the complexity of DNA sequences as the information content per nucleotide, calculated by means of some Lempel-Ziv data compression algorithm. It is possible to use the statistics of the complexity values of the functional regions of different complete genomes to distinguish among genomes of different domains of life (Archaea, Bacteria and Eukarya). We shall focus on the distribution function of the complexity of noncoding regions. We show that the three domains may be plotted in separate regions within the two-dimensional space where the axes are the skewness coefficient and the curtosis coefficient of the aforementioned distribution. Preliminary results on 15 genomes are introduced.