ArticlePloS one2015
Mapping the space of genomic signatures.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
18 citing papers in PubMed.
- Genome-wide pervasiveness and localized variation of [Formula: see text]-mer-based genomic signatures in eukaryotes.Scientific reports · 2026Article
- A composite universal DNA signature for the tree of life.Nature ecology & evolution · 2025Article
- CGRclust: Chaos Game Representation for twin contrastive clustering of unlabelled DNA sequences.BMC genomics · 2024Article
- Exploring geometry of genome space via Grassmann manifolds.Innovation (Cambridge (Mass.)) · 2024Article
- Automated classification of giant virus genomes using a random forest model built on trademark protein families.Npj viruses · 2024Article
- Genomic Signature in Evolutionary Biology: A Review.Biology · 2023Review
- Visualization of cross-resistance between antimicrobial agents by asymmetric multidimensional scaling.Journal of clinical pharmacy and therapeutics · 2022Article
- COVID-DeepPredictor: Recurrent Neural Network to Predict SARS-CoV-2 and Other Pathogenic Viruses.Frontiers in genetics · 2021Article
- Classification of Long Noncoding RNAs by k-mer Content.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Similarity Studies of Corona Viruses through Chaos Game Representation.Computational molecular bioscience · 2020Article
- Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study.PloS one · 2020Article
- Multimodal Long Noncoding RNA Interaction Networks: Control Panels for Cell Fate Specification.Genetics · 2019Review
- ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels.BMC genomics · 2019Article
- An open-source k-mer based machine learning tool for fast and accurate subtyping of HIV-1 genomes.PloS one · 2018Article
- Formalin-fixed paraffin-embedded sample conditions for deep next generation sequencing.The Journal of surgical research · 2017Article
- Additive methods for genomic signatures.BMC bioinformatics · 2016Article
- An investigation into inter- and intragenomic variations of graphic genomic signatures.BMC bioinformatics · 2015Article
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Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We propose a computational method to measure and visualize interrelationships among any number of DNA sequences allowing, for example, the examination of hundreds or thousands of complete mitochondrial genomes. An "image distance" is computed for each pair of graphical representations of DNA sequences, and the distances are visualized as a Molecular Distance Map: Each point on the map represents a DNA sequence, and the spatial proximity between any two points reflects the degree of structural similarity between the corresponding sequences. The graphical representation of DNA sequences utilized, Chaos Game Representation (CGR), is genome- and species-specific and can thus act as a genomic signature. Consequently, Molecular Distance Maps could inform species identification, taxonomic classifications and, to a certain extent, evolutionary history. The image distance employed, Structural Dissimilarity Index (DSSIM), implicitly compares the occurrences of oligomers of length up to k (herein k = 9) in DNA sequences. We computed DSSIM distances for more than 5 million pairs of complete mitochondrial genomes, and used Multi-Dimensional Scaling (MDS) to obtain Molecular Distance Maps that visually display the sequence relatedness in various subsets, at different taxonomic levels. This general-purpose method does not require DNA sequence alignment and can thus be used to compare similar or vastly different DNA sequences, genomic or computer-generated, of the same or different lengths. We illustrate potential uses of this approach by applying it to several taxonomic subsets: phylum Vertebrata, (super)kingdom Protista, classes Amphibia-Insecta-Mammalia, class Amphibia, and order Primates. This analysis of an extensive dataset confirms that the oligomer composition of full mtDNA sequences can be a source of taxonomic information. This method also correctly finds the mtDNA sequences most closely related to that of the anatomically modern human (the Neanderthal, the Denisovan, and the chimp), and that the sequence most different from it in this dataset belongs to a cucumber.
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Registered trials
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