In this article we address the problem of phylogenetic inference from nucleic acid data containing missing bases. We introduce a new effective approach, called “Probabilistic estimation of missing values” (PEMV), allowing one to estimate unknown nucleotides prior to computing the evolutionary distances between them. We show that the new method improves the accuracy of phylogenetic inference compared to the existing methods “Ignoring Missing Sites” (IMS), “Proportional Distribution of Missing and Ambiguous Bases” (PDMAB) included in the PAUP software . The proposed strategy for estimating missing nucleotides is based on probabilistic formulae developed in the framework of the Jukes-Cantor  and Kimura 2-parameter  models. The relative performances of the new method were assessed through simulations carried out with the SeqGen program , for data generation, and the Bio NJ method , for inferring phylogenies. We also compared the new method to the DNAML program  and “Matrix Representation using Parsimony” (MRP) ,  considering an example of 66 eutherian mammals originally analyzed in 
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.