Genomic Signature in Evolutionary Biology: A Review

Fuente, Rebeca de la; Dı́az-Villanueva, Wladimiro; Arnau, Vicente; Moya, Andrés

doi:10.3390/biology12020322

Cited by 8 publications

(5 citation statements)

References 154 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The selection of a genomic fragment s to represent the genome of an organism is a process that has to consider several factors, including fragment length, taxonomic level, and computational complexity of the algorithms used. For methods that rely on k -mer frequency for sequence classification, some studies 34 , 35 suggest the relation , where is the minimum length of sequence s that is necessary, in theory, to obtain statistical significance. However, in practice, longer sequences are needed.…”

Section: Methodsmentioning

confidence: 99%

Environment and taxonomy shape the genomic signature of prokaryotic extremophiles

Arias,

Butler,

Randhawa

et al. 2023

Sci Rep

View full text Add to dashboard Cite

This study provides comprehensive quantitative evidence suggesting that adaptations to extreme temperatures and pH imprint a discernible environmental component in the genomic signature of microbial extremophiles. Both supervised and unsupervised machine learning algorithms were used to analyze genomic signatures, each computed as the k-mer frequency vector of a 500 kbp DNA fragment arbitrarily selected to represent a genome. Computational experiments classified/clustered genomic signatures extracted from a curated dataset of $$\sim 700$$ ∼ 700 extremophile (temperature, pH) bacteria and archaea genomes, at multiple scales of analysis, $$1\le k \le 6$$ 1 ≤ k ≤ 6 . The supervised learning resulted in high accuracies for taxonomic classifications at $$2\le k \le 6$$ 2 ≤ k ≤ 6 , and medium to medium-high accuracies for environment category classifications of the same datasets at $$3\le k \le 6$$ 3 ≤ k ≤ 6 . For $$k=3$$ k = 3 , our findings were largely consistent with amino acid compositional biases and codon usage patterns in coding regions, previously attributed to extreme environment adaptations. The unsupervised learning of unlabelled sequences identified several exemplars of hyperthermophilic organisms with large similarities in their genomic signatures, in spite of belonging to different domains in the Tree of Life.

show abstract

Section: Methodsmentioning

confidence: 99%

Environment and taxonomy shape the genomic signature of prokaryotic extremophiles

Arias,

Butler,

Randhawa

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The selection of a genomic fragment s to represent the genome of an organism is a process that has to consider several factors, including fragment length, taxonomic level, and computational complexity of the algorithms used. For methods that rely on k-mer frequency for sequence classification, some studies 34,35 suggest the relation k = log 4 (| s |), where | s | is the minimum length of sequence s that is necessary, in theory, to obtain statistical significance. However, in practice, longer sequences are needed.…”

Section: Datasetsmentioning

confidence: 99%

Environment and taxonomy shape the genomic signature of prokaryotic extremophiles

Arias

Butler

Randhawa

et al. 2023

Preprint

View full text Add to dashboard Cite

This study presents a machine learning-based analysis supporting the hypothesis that microbial adapta- tions to extreme temperatures and pH conditions can result in a pervasive environmental component within their genomic signatures. To this end, an alignment-free method was used in conjunction with both supervised and unsupervised machine learning, to analyze genomic signatures extracted from a curated dataset of approximately 700 extremophilic (temperature, pH) bacteria and archaea genomes. The dataset analyzed in this paper is, to the best of our knowledge, the largest and most comprehensive to date for the study of the genomic signatures of extremophilic organisms. The supervised learning identified specific sets of k-mers that are relevant for the identification of the environmental components in the genomic signatures of extremophiles. For k = 3, our findings are consistent with amino acid compositional biases and codon usage patterns in coding regions that were previously attributed to extreme environment adaptations. The unsupervised learning of unlabelled sequences, by multiple algorithms, identified some hyperthermophilic organisms with large genomic signature similarities in spite of belonging to different domains This computational finding was confirmed by supervised learning experiments under challenging training scenarios, and corroborated by the presence of shared phenotypic traits and characteristics of the isolating environments.

show abstract

“…Sometimes, genomic signatures can differentiate single individuals, as in forensic science, that are usually compared to tumor cells in medicine, etc. In comparative genomics and evolutionary biology where the objective is to carry out comparisons among different species, genome signatures are based on statistical properties of DNA sequences that are species-specific; for example, the distribution of k -words along the genome is widely used [ 29 ].…”

Section: Segment Compositional Signature ( D Js...mentioning

confidence: 99%

“…Several measures of genome complexity are now available [ 25 , 26 , 27 , 28 ]; we refer the reader to [ 29 ] for a recent review. Some of these measures rely on the frequencies of k -words or k -mers.…”

Section: Introductionmentioning

confidence: 99%

Compositional Structure of the Genome: A Review

Bernaola‐Galván

Carpena

Gómez-Martín

et al. 2023

Biology

View full text Add to dashboard Cite

As the genome carries the historical information of a species’ biotic and environmental interactions, analyzing changes in genome structure over time by using powerful statistical physics methods (such as entropic segmentation algorithms, fluctuation analysis in DNA walks, or measures of compositional complexity) provides valuable insights into genome evolution. Nucleotide frequencies tend to vary along the DNA chain, resulting in a hierarchically patchy chromosome structure with heterogeneities at different length scales that range from a few nucleotides to tens of millions of them. Fluctuation analysis reveals that these compositional structures can be classified into three main categories: (1) short-range heterogeneities (below a few kilobase pairs (Kbp)) primarily attributed to the alternation of coding and noncoding regions, interspersed or tandem repeats densities, etc.; (2) isochores, spanning tens to hundreds of tens of Kbp; and (3) superstructures, reaching sizes of tens of megabase pairs (Mbp) or even larger. The obtained isochore and superstructure coordinates in the first complete T2T human sequence are now shared in a public database. In this way, interested researchers can use T2T isochore data, as well as the annotations for different genome elements, to check a specific hypothesis about genome structure. Similarly to other levels of biological organization, a hierarchical compositional structure is prevalent in the genome. Once the compositional structure of a genome is identified, various measures can be derived to quantify the heterogeneity of such structure. The distribution of segment G+C content has recently been proposed as a new genome signature that proves to be useful for comparing complete genomes. Another meaningful measure is the sequence compositional complexity (SCC), which has been used for genome structure comparisons. Lastly, we review the recent genome comparisons in species of the ancient phylum Cyanobacteria, conducted by phylogenetic regression of SCC against time, which have revealed positive trends towards higher genome complexity. These findings provide the first evidence for a driven progressive evolution of genome compositional structure.

show abstract

Genomic Signature in Evolutionary Biology: A Review

Cited by 8 publications

References 154 publications

Environment and taxonomy shape the genomic signature of prokaryotic extremophiles

Environment and taxonomy shape the genomic signature of prokaryotic extremophiles

Environment and taxonomy shape the genomic signature of prokaryotic extremophiles

Compositional Structure of the Genome: A Review

Contact Info

Product

Resources

About