AI Article Synopsis
- The traditional neighbor-joining programs often miss ambiguities in data due to their single-tree design and may produce misleading bootstrap frequencies.
- Resampling techniques can help uncover these ambiguities, but existing methods are limited by issues like zero-length branches and terminal order sensitivity.
- The new parsimony jackknifing approach addresses these concerns, running significantly faster than previous methods while effectively filtering out poorly-supported groupings in large datasets.
Article Abstract
Abstract- Because they are designed to produced just one tree, neighbor-joining programs can obscure ambiguities in data. Ambiguities can be uncovered by resampling, but existing neighbor-joining programs may give misleading bootstrap frequencies because they do not suppress zero-length branches and/or are sensitive to the order of terminals in the data. A new procedure, parsimony jackknifing, overcomes these problems while running hundreds of times faster than existing programs for neighbor-joining bootstrapping. For analysis of large matrices, parsimony jackknifing is hundreds of thousands of times faster than extensive branch-swapping, yet is better able to screen out poorly-supported groups.
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| http://dx.doi.org/10.1111/j.1096-0031.1996.tb00196.x | DOI Listing |
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