Posted Tue, May, 07,2013
Published today in Evolutionary Bioinformatics is a new original research article by Eduardo P. Costa, Celine Vens and Hendrik Blockeel. Read more about this paper below:
Title
Top-Down Clustering for Protein Subfamily Identification
Abstract
We propose a novel method for the task of protein subfamily identification; that is, finding subgroups of functionally closely related sequences within a protein family. In line with phylogenomic analysis, the method first builds a hierarchical tree using as input a multiple alignment of the protein sequences, then uses a post-pruning procedure to extract clusters from the tree. Differently from existing methods, it constructs the hierarchical tree top-down, rather than bottom-up and associates particular mutations with each division into subclusters. The motivating hypothesis for this method is that it may yield a better tree topology with more accurate subfamily identification as a result and additionally indicates functionally important sites and allows for easy classification of new proteins. A thorough experimental evaluation confirms the hypothesis. The novel method yields more accurate clusters and a better tree topology than the state-of-the-art method SCI-PHY, identifies known functional sites, and identifies mutations that alone allow for classifying new sequences with an accuracy approaching that of hidden Markov models.
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Posted in: Articles Published
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Thu 08 Oct, 2015
Published This Week (5th - 9th October)Thu 08 Oct, 2015
Biomarker Insights Paper Endorsed by Editor in ChiefWed 07 Oct, 2015
Interview with Professor Jamie DaviesContributing an invited review was a pleasant experience with Libertas. We are grateful for such a solid platform and their belief in young talented scientists and their research. Overall, it was a wonderful and simplified process from submission to publication of the article with regular updates at each step from various staff members. Each person was extremely efficient and very professional during manuscript processing.
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