Kullback Leibler divergence in complete bacterial and phage genomes

Abstract

The amino acid content of the proteins encoded by a genome may predict the coding potential of that genome and may reflect lifestyle restrictions of the organism. Here, we calculated the Kullback–Leibler divergence from the mean amino acid content as a metric to compare the amino acid composition for a large set of bacterial and phage genome sequences. Using these data, we demonstrate that (i) there is a significant difference between amino acid utilization in different phylogenetic groups of bacteria and phages; (ii) many of the bacteria with the most skewed amino acid utilization profiles, or the bacteria that host phages with the most skewed profiles, are endosymbionts or parasites; (iii) the skews in the distribution are not restricted to certain metabolic processes but are common across all bacterial genomic subsystems; (iv) amino acid utilization profiles strongly correlate with GC content in bacterial genomes but very weakly correlate with the G+C percent in phage genomes. These findings might be exploited to distinguish coding from non-coding sequences in large data sets, such as metagenomic sequence libraries, to help in prioritizing subsequent analyses.

Unsupervised identification of significant lineages of SARS-CoV-2 through scalable machine learning methods

“…3mc(S)+3mc(N)+3mc(M)+3mc(E) ) or applying other characterisation methods such as “protein- nv ” ( 37 ), graphical representation ( 38 ), and Fourier power spectrum ( 39 ). Furthermore, previous methods for phylogenetic reconstruction have used non-Euclidean distances such as Wasserstein ( 40 ), Kullback–Leibler ( 41 ), Yau–Hausdorff ( 38 ), or Structural Similarity Index Measure ( 42 ). Thus, applying these to dimensionality reduction algorithms might generate better representations of the genetic landscape.…”