A rapid and simple method for assessing and representing genome sequence relatedness
Résumé
Coherent genomic groups are frequently used as a proxy for bacterial species delineation
through computation of overall genome relatedness indices (OGRI). Average nucleotide
identity (ANI) is a widely employed method for estimating relatedness between genomic
sequences. However, pairwise comparisons of genome sequences based on ANI is rela-
tively computationally intensive and therefore precludes analyses of large datasets com-
posed of thousands of genome sequences.In this work we proposed a workflow to com-
pute and visualize relationships between genomic sequences. A dataset containing more
than 3,500 Pseudomonas genome sequences was successfully classified with an alter-
native OGRI based on k-mer counts in few hours with the same precision as ANI. A
new visualization method based on zoomable circle packing was employed for assess-
ing relationships among the 350 groups generated. Amendment of databases with these
Pseudomonas groups greatly improved the classification of metagenomic read sets with
k-mer-based classifier.
Domaines
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