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Nucleic Acids Research Advance Access originally published online on February 17, 2009
Nucleic Acids Research 2009 37(7):2096-2104; doi:10.1093/nar/gkp075
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Nucleic Acids Research, 2009, Vol. 37, No. 7 2096-2104
© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Genomics

Integration of phenotypic metadata and protein similarity in Archaea using a spectral bipartitioning approach

Sean D. Hooper1,*, Iain J. Anderson1, Amrita Pati1, Daniel Dalevi2, Konstantinos Mavromatis1 and Nikos C. Kyrpides1

1Department of Energy Joint Genome Institute (DOE-JGI), Genome Biology Program, 2800 Mitchell Drive, Walnut Creek, CA 94598 and 2Biological Data Management and Technology Center, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA

*To whom correspondence should be addressed. Email: SHooper{at}lbl.gov

Received October 7, 2008. Revised January 6, 2009. Accepted January 27, 2009.

In order to simplify and meaningfully categorize large sets of protein sequence data, it is commonplace to cluster proteins based on the similarity of those sequences. However, it quickly becomes clear that the sequence flexibility allowed a given protein varies significantly among different protein families. The degree to which sequences are conserved not only differs for each protein family, but also is affected by the phylogenetic divergence of the source organisms. Clustering techniques that use similarity thresholds for protein families do not always allow for these variations and thus cannot be confidently used for applications such as automated annotation and phylogenetic profiling. In this work, we applied a spectral bipartitioning technique to all proteins from 53 archaeal genomes. Comparisons between different taxonomic levels allowed us to study the effects of phylogenetic distances on cluster structure. Likewise, by associating functional annotations and phenotypic metadata with each protein, we could compare our protein similarity clusters with both protein function and associated phenotype. Our clusters can be analyzed graphically and interactively online.


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