An investigation into inter- and intragenomic variations of graphic genomic signatures

Abstract

BackgroundMotivated by the general need to identify and classify species based on molecular evidence, genome comparisons have been proposed that are based on measuring mostly Euclidean distances between Chaos Game Representation (CGR) patterns of genomic DNA sequences.ResultsWe provide, on an extensive dataset and using several different distances, confirmation of the hypothesis that CGR patterns are preserved along a genomic DNA sequence, and are different for DNA sequences originating from genomes of different species. This finding lends support to the theory that CGRs of genomic sequences can act as graphic genomic signatures. In particular, we compare the CGR patterns of over five hundred different 150,000 bp genomic sequences spanning one complete chromosome from each of six organisms, representing all kingdoms of life: H. sapiens (Animalia; chromosome 21), S. cerevisiae (Fungi; chromosome 4), A. thaliana (Plantae; chromosome 1), P. falciparum (Protista; chromosome 14), E. coli (Bacteria - full genome), and P. furiosus (Archaea - full genome). To maximize the diversity within each species, we also analyze the interrelationships within a set of over five hundred 150,000 bp genomic sequences sampled from the entire aforementioned genomes. Lastly, we provide some preliminary evidence of this method’s ability to classify genomic DNA sequences at lower taxonomic levels by comparing sequences sampled from the entire genome of H. sapiens (class Mammalia, order Primates) and of M. musculus (class Mammalia, order Rodentia), for a total length of approximately 174 million basepairs analyzed. We compute pairwise distances between CGRs of these genomic sequences using six different distances, and construct Molecular Distance Maps, which visualize all sequences as points in a two-dimensional or three-dimensional space, to simultaneously display their interrelationships.ConclusionOur analysis confirms, for this dataset, that CGR patterns of DNA sequences from the same genome are in general quantitatively similar, while being different for DNA sequences from genomes of different species. Our assessment of the performance of the six distances analyzed uses three different quality measures and suggests that several distances outperform the Euclidean distance, which has so far been almost exclusively used for such studies.

INNUENDO: A cross‐sectoral platform for the integration of genomics in the surveillance of food‐borne pathogens

“…In most cases, very short oligonucleotides were searched (often tetranucleotides). Similar frequencies were observed in phylogenetically related genomes and this similarity was especially important for genomes within the same species, genera or family (Wang et al, 2005, Karamichalis et al 2015.…”

Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study

“…phylogenetic analyses [39]. Within the COVID-19 strains, over 99% sequence similarity 49 and a lack of diversity within these strains suggest a common lineage and source, with Sarbecovirus strains, the hypothesis that COVID-19 originated from bats is deemed very 56 likely [12,34,36,39,[42][43][44][45]. 57 All analyses performed thus far have been alignment-based and rely on the 58 annotations of the viral genes.…”

“…Moreover, the alignment 62 demands the sequences to be continuously homologous which is not always the case. 63 Alignment-free methods [48][49][50][51][52] have been proposed in the past as an alternative to 64 address the limitations of the alignment-based methods. Comparative genomics beyond 65 alignment-based approaches have benefited from the computational power of machine 66 learning.…”

“…MLDSP-GUI [52] provides an option to 74 use 2D Chaos Game Representation (CGR) [53] as numerical representation of DNA 75 sequences. CGR's have a longstanding use in species classification with identification of 76 biases in sequence composition [49,52,53]. MLDSP-GUI has shown 100% classification 77 accuracy for Flavivirus genus to species classification using 2D CGR as numerical 78 representation [52].…”

“…It is challenging (and sometimes impossible) for alignment-based 307 methods to compare a large number of sequences that are too different in their 308 composition. Alignment-free methods have been used successfully in the past to address 309 the limitations of the alignment-based methods [49][50][51][52]. The alignment-free approach is 310 quick and can handle a large number of sequences.…”