Q.18 Which one of the following is NOT an algorithm for building phylogenetic trees?
(A) Maximum parsimony (B) Neighbor joining
(C) Maximum likelihood (D) Bootstrap
Bootstrap is not an algorithm for building phylogenetic trees. Maximum parsimony, Neighbor joining, and Maximum likelihood are established tree-building methods, while Bootstrap serves as a statistical resampling technique to assess tree reliability.
Question Breakdown
The query asks which option does not qualify as a phylogenetic tree construction algorithm. Phylogenetic trees depict evolutionary relationships among species or taxa based on genetic or morphological data.
Option Analysis
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Maximum Parsimony (A): Seeks the tree requiring the fewest evolutionary changes, or “parsimonious” explanation, by minimizing substitutions across sites.
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Neighbor Joining (B): Distance-based method that iteratively clusters taxa with smallest adjusted distances, ideal for large datasets and additive distance matrices.
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Maximum Likelihood (C): Model-based approach optimizing the probability of observing data under specific evolutionary models, accounting for substitution rates.
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Bootstrap (D): Resampling technique generating pseudoreplicates from original data alignment to compute node support values (e.g., percentages), not constructing trees itself.
Phylogenetic tree construction remains a cornerstone of evolutionary biology and bioinformatics, helping researchers infer relationships among species using algorithms like Maximum parsimony, Neighbor joining, and Maximum likelihood. However, one common option—Bootstrap—is NOT an algorithm for building phylogenetic trees, as it evaluates tree robustness instead.
Core Phylogenetic Tree Algorithms
Maximum parsimony prioritizes simplicity by selecting trees with minimal character state changes. Neighbor joining excels in speed for distance matrices, iteratively joining closest taxa. Maximum likelihood provides statistical rigor by maximizing data likelihood under evolutionary models.
Why Bootstrap Stands Apart
Bootstrap generates 100–1000 resampled datasets from the original alignment, rebuilding trees per replicate to derive confidence values (e.g., 95% support). It enhances any tree-building algorithm but does not create trees de novo.
Comparison Table
| Method | Type | Key Strength | Builds Trees? |
|---|---|---|---|
| Maximum Parsimony | Character-based | Minimizes changes | Yes |
| Neighbor Joining | Distance-based | Fast for large data | Yes |
| Maximum Likelihood | Model-based | Accounts for rates | Yes |
| Bootstrap | Resampling | Assesses support | No |


