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ML or NJ-MCL? A comparison between two robust phylogenetic methods

Paper ID Volume ID Publish Year Pages File Format Full-Text
15242 1396 2009 6 PDF Available
Title
ML or NJ-MCL? A comparison between two robust phylogenetic methods
Abstract

Large-scale gene sequencing gives an opportunity to reconstruct the tree of life and histories of multigene species phylogenies from very large datasets. A primary need for reconstructing large-scale phylogenies is a computationally efficient and accurate method. Current efforts to achieve such a goal include NJ-MCL2 described by Tamura et al. (2004; 2007), an algorithm based on maximum likelihood (ML) and neighbor joining (NJ) algorithms. Although it has been reported that the NJ-MCL method performs better than the NJ method, studies comparing the accuracy of the ML and NJ-MCL methods are lacking. Here, accuracy of the NJ-MCL and the ML methods are examined. The concatenation approach (by progressive addition of genes) is used in a biologically realistic computer simulation to infer the accuracy of the methods. Simulation results clearly show that although NJ-MCL is computationally efficient and outperforms NJ method, the ML method is clearly much more accurate than the NJ-MCL method. The results encourage the use of the ML algorithm where datasets include up to several hundred species, but for reconstructing grand-scale phylogenies (i.e., where several thousand of taxa are included) NJ-MCL is preferred.

Keywords
Phylogenetics; Maximum likelihood; Maximum composite likelihood; Concatenation approach; Topological distance; Accuracy
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ML or NJ-MCL? A comparison between two robust phylogenetic methods
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 33, Issue 5, October 2009, Pages 373–378
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us