NDLI: An Experimental Evaluation of Corrected Inversion and DCJ Distance Metric through Simulations

Content Provider	IEEE Xplore Digital Library
Author	Jian Shi Jijun Tang
Copyright Year	2010
Abstract	The available data of complete ordering of genes on each chromosome of organisms is increasing thank to the research in gene hunting and annotation. Researchers like evolutionary biologists and computer scientists are greatly interested in gene order data because it make possible to resolve the ancient branches to fill the tree of life. Inversion based distance metric and the so called DCJ (double-cut-and-join) distance metric have been intensively studied through all these years of work. However, both of the two metrics only consider the smallest possible distance between two genomes, known as edit distance. It underestimates the true evolutionary distance and thus makes the phylogenies less accurate. Correction techniques for inversion and DCJ have been proposed to improve the aforementioned problem. The first has been widely used in phylogenetic reconstructions and the second has not seen significant use to date. We design and conduct a series of experiments to estimate the quality of inferred phylogenies by using inversion and DCJ based distance metrics in various aspects, with and without applying correction. Our main findings are three folds: First, correction techniques yield much more accurate phylogenies; second, inversion and DCJ measure return similar results; last but not least, corrected DCJ (CDCJ) provides the most accurate distance metric for tree reconstruction.
Starting Page	1
Ending Page	4
File Size	107505
Page Count	4
File Format	PDF
ISBN	9781424447121
ISSN	21517622
DOI	10.1109/ICBBE.2010.5518035
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Computational modeling Genomics Biology computing Data engineering Organisms Phylogeny Bioinformatics Biological cells Testing
Content Type	Text
Resource Type	Article

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