ArticleBioinformatics (Oxford, England)2023
Scaling neighbor joining to one million taxa with dynamic and heuristic neighbor joining.
Article in Bioinformatics (Oxford, England), 2023. 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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Who cites it
9 citing papers in PubMed.
- Deep Learning-Enhanced Raman Microspectroscopy Enables Rapid Microbial Classification and Captures Phylogenetic Relationships.Microorganisms · 2026Article
- High-resolution spatial mapping of cell state and lineage dynamics in vivo with PEtracer.Science (New York, N.Y.) · 2025Article
- Sparse Neighbor Joining: rapid phylogenetic inference using a sparse distance matrix.Bioinformatics (Oxford, England) · 2024Article
- Article
- Kssdtree: an interactive Python package for phylogenetic analysis based on sketching technique.Bioinformatics (Oxford, England) · 2024Article
- SpanSeq: similarity-based sequence data splitting method for improved development and assessment of deep learning projects.NAR genomics and bioinformatics · 2024Article
- Genetic diversity analysis and core germplasm bank construction in cold resistant germplasm of rubber trees (Hevea brasiliensis).Scientific reports · 2024Article
- ARGprofiler-a pipeline for large-scale analysis of antimicrobial resistance genes and their flanking regions in metagenomic datasets.Bioinformatics (Oxford, England) · 2024Article
- Mobilisation and analyses of publicly available SARS-CoV-2 data for pandemic responses.Microbial genomics · 2024Article
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Authors and funding
1 author.
Funding
Abstract
motivationThe neighbor-joining (NJ) algorithm is a widely used method to perform iterative clustering and forms the basis for phylogenetic reconstruction in several bioinformatic pipelines. Although NJ is considered to be a computationally efficient algorithm, it does not scale well for datasets exceeding several thousand taxa (>100 000). Optimizations to the canonical NJ algorithm have been proposed; these optimizations are, however, achieved through approximations or extensive memory usage, which is not feasible for large datasets.
resultsIn this article, two new algorithms, dynamic neighbor joining (DNJ) and heuristic neighbor joining (HNJ), are presented, which optimize the canonical NJ method to scale to millions of taxa without increasing the memory requirements. Both DNJ and HNJ outperform the current gold standard methods to construct NJ trees, while DNJ is guaranteed to produce exact NJ trees. AVAILABILITY AND IMPLEMENTATION: https://bitbucket.org/genomicepidemiology/ccphylo.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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