Document Type
Article
Publication Date
2007
Keywords
Relative Entropy, Composition Profiler, Fractional Difference, Intrinsic Disorder, Query Sample
Digital Object Identifier (DOI)
https://doi.org/10.1186/1471-2105-8-211
Abstract
Background: Composition Profiler is a web-based tool for semi-automatic discovery of enrichment or depletion of amino acids, either individually or grouped by their physico-chemical or structural properties.
Results: The program takes two samples of amino acids as input: a query sample and a reference sample. The latter provides a suitable background amino acid distribution, and should be chosen according to the nature of the query sample, for example, a standard protein database (e.g. SwissProt, PDB), a representative sample of proteins from the organism under study, or a group of proteins with a contrasting functional annotation. The results of the analysis of amino acid composition differences are summarized in textual and graphical form.
Conclusion: As an exploratory data mining tool, our software can be used to guide feature selection for protein function or structure predictors. For classes of proteins with significant differences in frequencies of amino acids having particular physico-chemical (e.g. hydrophobicity or charge) or structural (e.g. α helix propensity) properties, Composition Profiler can be used as a rough, light-weight visual classifier.
Was this content written or created while at USF?
No
Citation / Publisher Attribution
BMC Bioinformatics, v. 8, art. 211
This is an Open Access article is distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Scholar Commons Citation
Vacic, Vladimir; Uversky, Vladimir N.; Dunker, A. Keith; and Lonardi, Stefano, "Composition Profiler: a Tool for Discovery and Visualization of Amino Acid Composition Differences" (2007). Molecular Medicine Faculty Publications. 593.
https://digitalcommons.usf.edu/mme_facpub/593