Clustering of protein families into functional subtypes using relative complexity measure with reduced amino acid alphabets

Warning The system is temporarily closed to updates for reporting purpose.

Albayrak, Aydın and Otu, Hasan Hüseyin and Sezerman, Uğur (2010) Clustering of protein families into functional subtypes using relative complexity measure with reduced amino acid alphabets. BMC Bioinformatics, 11 . ISSN 1471-2105

[thumbnail of This is a RoMEO green publisher -- author can archive pre-print (ie pre-refereeing) and post-print (ie final draft post-refereeing)] PDF (This is a RoMEO green publisher -- author can archive pre-print (ie pre-refereeing) and post-print (ie final draft post-refereeing))
AYDİN_bmc1471-2105-11-428.pdf

Download (619kB)

Abstract

Background: Phylogenetic analysis can be used to divide a protein family into subfamilies in the absence of experimental information. Most phylogenetic analysis methods utilize multiple alignment of sequences and are based on an evolutionary model. However, multiple alignment is not an automated procedure and requires human intervention to maintain alignment integrity and to produce phylogenies consistent with the functional splits in underlying sequences. To address this problem, we propose to use the alignment-free Relative Complexity Measure (RCM) combined with reduced amino acid alphabets to cluster protein families into functional subtypes purely on sequence criteria. Comparison with an alignment-based approach was also carried out to test the quality of the clustering. Results: We demonstrate the robustness of RCM with reduced alphabets in clustering of protein sequences into families in a simulated dataset and seven well-characterized protein datasets. On protein datasets, crotonases, mandelate racemases, nucleotidyl cyclases and glycoside hydrolase family 2 were clustered into subfamilies with 100% accuracy whereas acyl transferase domains, haloacid dehalogenases, and vicinal oxygen chelates could be assigned to subfamilies with 97.2%, 96.9% and 92.2% accuracies, respectively. Conclusions: The overall combination of methods in this paper is useful for clustering protein families into subtypes based on solely protein sequence information. The method is also flexible and computationally fast because it does not require multiple alignment of sequences.
Item Type: Article
Additional Information: Article Number: 428
Subjects: Q Science > Q Science (General)
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Biological Sciences & Bio Eng.
Faculty of Engineering and Natural Sciences
Depositing User: Uğur Sezerman
Date Deposited: 08 Dec 2010 22:16
Last Modified: 29 Jul 2019 12:21
URI: https://research.sabanciuniv.edu/id/eprint/15781

Actions (login required)

View Item
View Item