The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Methods for computing the standard errors of branching points in an evolutionary tree and their application to molecular data from humans and apes.
Open accessAbstract
Statistical methods for computing the standard errors of the branching points of an evolutionary tree are developed. These methods are for the unweighted pair-group method-determined (UPGMA) trees reconstructed from molecular data such as amino acid sequences, nucleotide sequences, restriction-sites data, and electrophoretic distances. They were applied to data for the human, chimpanzee, gorilla, orangutan, and gibbon species. Among the four different sets of data used, DNA sequences for an 895-nucleotide segment of mitochondrial DNA (Brown et al. 1982) gave the most reliable tree, whereas electrophoretic data (Bruce and Ayala 1979) gave the least reliable one. The DNA sequence data suggested that the chimpanzee is the closest and that the gorilla is the next closest to the human species. The orangutan and gibbon are more distantly related to man than is the gorilla. This topology of the tree is in agreement with that for the tree obtained from chromosomal studies and DNA-hybridization experiments. However, the difference between the branching point for the human and the chimpanzee species and that for the gorilla species and the human-chimpanzee group is not statistically significant. In addition to this analysis, various factors that affect the accuracy of an estimated tree are discussed.
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Cites
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Weighted Neighbor Joining: A Likelihood-Based Approach to Distance-Based Phylogeny Reconstruction
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Metadata
- Venue
- Molecular Biology and Evolution
- Publisher
- Oxford University Press (OUP)
- Published
- 1985-01
- Volume
- 2
- Issue
- 1
- Pages
- 66-85
- ISSN
- 0737-4038 (ISSN-L)
- 1537-1719
- OpenAlex
- W2295397294
- Cited by
- 309
- References
- 37
- FWCI
- 5.11
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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