ArticleMolecular biology and evolution2015
How evolution of genomes is reflected in exact DNA sequence match statistics.
Article in Molecular biology and evolution, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 17 citations in OpenAlex.
- Reconstructing the Network of Horizontal Gene Exchange in Bacteria to Differentiate Direct and Indirect Transfers.Genome biology and evolution · 2026Article
- Modeling the mosaic structure of bacterial genomes to infer their evolutionary history.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Whole-genome sequencing of Fusarium spp. causing sugarcane root rot on both chewing cane and sugar-making cane.Stress biology · 2024Article
- Article
- Nucleotide spacing distribution analysis for human genome.Mammalian genome : official journal of the International Mammalian Genome Society · 2021Article
- Primary orthologs from local sequence context.BMC bioinformatics · 2020Article
- Article
- Evolutionary dynamics of selfish DNA explains the abundance distribution of genomic subsequences.Scientific reports · 2016Article
- Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genome evolution is shaped by a multitude of mutational processes, including point mutations, insertions, and deletions of DNA sequences, as well as segmental duplications. These mutational processes can leave distinctive qualitative marks in the statistical features of genomic DNA sequences. One such feature is the match length distribution (MLD) of exactly matching sequence segments within an individual genome or between the genomes of related species. These have been observed to exhibit characteristic power law decays in many species. Here, we show that simple dynamical models consisting solely of duplication and mutation processes can already explain the characteristic features of MLDs observed in genomic sequences. Surprisingly, we find that these features are largely insensitive to details of the underlying mutational processes and do not necessarily rely on the action of natural selection. Our results demonstrate how analyzing statistical features of DNA sequences can help us reveal and quantify the different mutational processes that underlie genome evolution.
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