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Using Phylogenetic Profiles to Predict Functional Relationships

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Bacterial Molecular Networks

Part of the book series: Methods in Molecular Biology ((MIMB,volume 804))

Abstract

Phylogenetic profiling involves the comparison of phylogenetic data across gene families. It is possible to construct phylogenetic trees, or related data structures, for specific gene families using a wide variety of tools and approaches. Phylogenetic profiling involves the comparison of this data to determine which families have correlated or coupled evolution. The underlying assumption is that in certain cases these couplings may allow us to infer that the two families are functionally related: that is their function in the cell is coupled. Although this technique can be applied to noncoding genes, it is more commonly used to assess the function of protein coding genes. Examples of proteins that are functionally related include subunits of protein complexes, or enzymes that perform consecutive steps along biochemical pathways. We hypothesize the deletion of one of the families from a genome would then indirectly affect the function of the other. Dozens of different implementations of the phylogenetic profiling technique have been developed over the past decade. These range from the first simple approaches that describe phylogenetic profiles as binary vectors to the most complex ones that attempt to model to the coevolution of protein families on a phylogenetic tree. We discuss a set of these implementations and present the software and databases that are available to perform phylogenetic profiling.

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Acknowledgments

The author wishes to acknowledge the UCLA-DOE Institute for Genomics and proteomics for support.

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Correspondence to Matteo Pellegrini .

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Pellegrini, M. (2012). Using Phylogenetic Profiles to Predict Functional Relationships. In: van Helden, J., Toussaint, A., Thieffry, D. (eds) Bacterial Molecular Networks. Methods in Molecular Biology, vol 804. Springer, New York, NY. https://doi.org/10.1007/978-1-61779-361-5_9

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  • DOI: https://doi.org/10.1007/978-1-61779-361-5_9

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-61779-360-8

  • Online ISBN: 978-1-61779-361-5

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