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Cited by 948

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Estimation of evolutionary distance between nucleotide sequences.

Open access
·1984·Molecular Biology and Evolution

Abstract

A mathematical formula for estimating the average number of nucleotide substitutions per site (delta) between two homologous DNA sequences is developed by taking into account unequal rates of substitution among different nucleotide pairs. Although this formula is obtained for the equal-input model of nucleotide substitution, computer simulations have shown that it gives a reasonably good estimate for a wide range of nucleotide substitution patterns as long as delta is equal to or smaller than 1. Furthermore, the frequency of cases to which the formula is inapplicable is much lower than that for other similar methods recently proposed. This point is illustrated using insulin genes. A statistical method for estimating the number of nucleotide changes due to deletion and insertion is also developed. Application of this method to globin gene data indicates that the number of nucleotide changes per site increases with evolutionary time but the pattern of the increase is quite irregular.

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Crossref · 10.1093/oxfordjournals.molbev.a040317

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Metadata

Venue
Molecular Biology and Evolution
Published
1984-04
Volume
1
Issue
3
Pages
269-85
ISSN
  • 0737-4038 (ISSN-L)
  • 1537-1719
OpenAlex
W2109580010
Cited by
948
References
2
FWCI
1.72
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Estimation of evolutionary distance between nucleotide sequences. https://app.cochat.ai/papers/10.1093/oxfordjournals.molbev.a040317
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Estimation of evolutionary distance between nucleotide sequences. (1984). Molecular Biology and Evolution. https://doi.org/10.1093/oxfordjournals.molbev.a040317

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.