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CC BY V4.0, Open Access
Journal article
Using protein clusters from whole proteomes to construct and augment a dendrogram
Advances in bioinformatics.
01/2013
Handle:
https://hdl.handle.net/2376/6000
Abstract
In this paper we present a new ab initio approach for constructing an unrooted dendrogram using protein clusters, an approach that has the potential for estimating relationships among several thousands of species based on their putative proteomes. We employ an open-source software program called pClust that was developed for use in metagenomic studies. Sequence alignment is performed by pClust using the Smith-Waterman algorithm, which is known to give optimal alignment and, hence, greater accuracy than BLAST-based methods. Protein clusters generated by pClust are used to create protein profiles for each species in the dendrogram, these profiles forming a correlation filter library for use with a new taxon. To augment the dendrogram with a new taxon, a protein profile for the taxon is created using BLASTp, and this new taxon is placed into a position within the dendrogram corresponding to the highest correlation with profiles in the correlation filter library. This work was initiated because of our interest in plasmids, and each step is illustrated using proteomes from Gram-negative bacterial plasmids. Proteomes for 527 plasmids were used to generate the dendrogram, and to demonstrate the utility of the insertion algorithm twelve recently sequenced pAKD plasmids were used to augment the dendrogram.
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Details
- Title
- Using protein clusters from whole proteomes to construct and augment a dendrogram
- Creators
- Yunyun Zhou (Author)Douglas R. Call (Author)Shira L. Broschat (Author)Yves Van de Peer (Author)
- Publication Details
- Advances in bioinformatics.
- Academic Unit
- Electrical Engineering and Computer Science, School of; Paul G. Allen School for Global Animal Health
- Identifiers
- 99900501949901842
- Copyright
- Creative Commons Attribution 4.0 International ; https://creativecommons.org/licenses/by/4.0/
- Language
- English
- Resource Type
- Journal article