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PMID: 2116531 Published · ppublish English Journal Article Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.

Compositional statistics: an improvement of evolutionary parsimony and its application to deep branches in the tree of life.

Journal of molecular evolution ·Vol. 31 ·No. 1 ·1990-07-00 ·Pages 51-68

Sidow A, Wilson AC

Abstract

We present compositional statistics, a new method of phylogenetic inference, which is an extension of evolutionary parsimony. Compositional statistics takes account of the base composition of the compared sequences by using nucleotide positions that evolutionary parsimony ignores. It shares with evolutionary parsimony the features of rate invariance and the fundamental distinction between transitions and transversions. Of the presently available methods of phylogenetic inference, compositional statistics is based on the fewest and mildest assumptions about the mode of DNA sequence evolution. It is therefore applicable to phylogenetic studies of the most distantly related organisms or molecules. This was illustrated by analyzing conservative positions in the DNA sequences of the large subunit of RNA polymerase from three archaebacterial groups, a eubacterium, a chloroplast, and the three eukaryotic polymerases. Internally consistent results, which are in accord with our knowledge of organelle origin and archaebacterial physiology, were achieved.

MeSH Terms
Amino Acid Sequence Archaea/genetics Base Composition Base Sequence Biological Evolution DNA-Directed RNA Polymerases/genetics Eukaryotic Cells/metabolism Models, Statistical Molecular Sequence Data Phylogeny Symbiosis
Chemicals
DNA-Directed RNA Polymerases
Authors & Affiliations
2 authors, click to expand affiliations / ORCID
Sidow A
Division of Biochemistry and Molecular Biology, University of California, Berkeley 94720.
Wilson A C
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Article Info
Journal
Journal of molecular evolution
Abbr.
J Mol Evol
ISSN
0022-2844
DOI
10.1007/BF02101792
Published
1990-07-00
Pages
51-68
Language
English
Region
Germany
NLM ID
0360051
Subset
IM
External Links
PubMed source DOI: 10.1007/BF02101792 View journal details
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