ArticleFrontiers in genetics2020
Combinatorial and Computational Investigations of Neighbor-Joining Bias.
Article in Frontiers in genetics, 2020. 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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9 citing papers in PubMed.
- Identification ofPlants (Basel, Switzerland) · 2026Article
- Genome-wide identification of maize G protein genes and regulatory roles of the ZmGG1 subfamily in saline-alkali stress response.BMC plant biology · 2026Article
- Common Methods for Phylogenetic Tree Construction and Their Implementation in R.Bioengineering (Basel, Switzerland) · 2024Review
- Molecular characterization of Chikungunya virus recovered from patients in the Maranhão state, Brazil.Molecular biology reports · 2024Article
- Insights into the Bioinformatics and Transcriptional Analysis of the Elongator Complexes (Plants (Basel, Switzerland) · 2023Article
- Whole genome sequence ofFrontiers in microbiology · 2023Article
- Genome-wide identification, evolutionary and functional analyses of KFB family members in potato.BMC plant biology · 2022Article
- Isolation and Genetic Characterization of Canine Parvovirus in a Malayan Tiger.Frontiers in veterinary science · 2021Article
- Combinatorial and Computational Investigations of Neighbor-Joining Bias.Frontiers in genetics · 2020Article
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2 authors.
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Abstract
The Neighbor-Joining algorithm is a popular distance-based phylogenetic method that computes a tree metric from a dissimilarity map arising from biological data. Realizing dissimilarity maps as points in Euclidean space, the algorithm partitions the input space into polyhedral regions indexed by the combinatorial type of the trees returned. A full combinatorial description of these regions has not been found yet; different sequences of Neighbor-Joining agglomeration events can produce the same combinatorial tree, therefore associating multiple geometric regions to the same algorithmic output. We resolve this confusion by defining agglomeration orders on trees, leading to a bijection between distinct regions of the output space and weighted Motzkin paths. As a result, we give a formula for the number of polyhedral regions depending only on the number of taxa. We conclude with a computational comparison between these polyhedral regions, to unveil biases introduced in any implementation of the algorithm.
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