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Likelihood-mapping: A simple method to visualize phylogenetic content of a sequence alignment

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Strimmer, Korbinian, von Haeseler, Arndt·1997·Proceedings of the National Academy of Sciences

Abstract

We introduce a graphical method, likelihood-mapping, to visualize the phylogenetic content of a set of aligned sequences. The method is based on an analysis of the maximum likelihoods for the three fully resolved tree topologies that can be computed for four sequences. The three likelihoods are represented as one point inside an equilateral triangle. The triangle is partitioned in different regions. One region represents star-like evolution, three regions represent a well-resolved phylogeny, and three regions reflect the situation where it is difficult to distinguish between two of the three trees. The location of the likelihoods in the triangle defines the mode of sequence evolution. If n sequences are analyzed, then the likelihoods for each subset of four sequences are mapped onto the triangle. The resulting distribution of points shows whether the data are suitable for a phylogenetic reconstruction or not.

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Crossref · 10.1073/pnas.94.13.6815

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Metadata

Authors
Strimmer, K., & von Haeseler, A.
Venue
Proceedings of the National Academy of Sciences
Published
1997-06-24
Volume
94
Issue
13
Pages
6815-6819
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2140689424
Cited by
941
References
1
FWCI
7.31
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Likelihood-mapping: A simple method to visualize phylogenetic content of a sequence alignment. https://app.cochat.ai/papers/10.1073/pnas.94.13.6815
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Strimmer, K., & von Haeseler, A. (1997). Likelihood-mapping: A simple method to visualize phylogenetic content of a sequence alignment. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.94.13.6815

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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.