Date of Original Version

1-1-2000

Type

Article

PubMed ID

11108472

Rights Management

This is a copy of an article published in the Journal of Computational Biology © 2000 Mary Ann Liebert, Inc.; Journal of Computational Biology is available online at: http://online.liebertpub.com

Abstract or Description

Large scale gene duplication is a major force driving the evolution of genetic functional innovation. Whole genome duplications are widely believed to have played an important role in the evolution of the maize, yeast, and vertebrate genomes. The use of evolutionary trees to analyze the history of gene duplication and estimate duplication times provides a powerful tool for studying this process. Many studies in the molecular evolution literature have used this approach on small data sets, using analyses performed by hand. The rapid growth of genetic sequence data will soon allow similar studies on a genomic scale, but such studies will be limited unless the analysis can be automated. Even existing data sets admit alternative hypotheses that would be too tedious to consider without automation. In this paper, we describe a program called NOTUNG that facilitates large scale analysis, using both rooted and unrooted trees. When tested on trees analyzed in the literature, NOTUNG consistently yielded results that agree with the assessments in the original publications. Thus, NOTUNG provides a basic building block for inferring duplication dates from gene trees automatically and can also be used as an exploratory analysis tool for evaluating alternative hypotheses.

DOI

10.1089/106652700750050871

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Published In

Journal of computational biology, 7, 3-4, 429-447.