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Composition Profiler is a web-based tool that automates detection of enrichment or depletion patterns of individual amino acids or groups of amino acids classified by several physico-chemical and structural properties such as aromaticity, charge, polarity, hydrophobicity, flexibility, surface exposure, solvation potential, interface propensity, normalized frequency of occurrence in α helices, β structures, and coils, linker and disorder propensities, size and bulkiness.
Composition Profiler takes two samples of amino acid sequences as input, namely the query sample and the reference sample. The reference sample provides the background amino acid distribution. Suitable background distributions can be chosen according to the nature of the query sample, e.g., a standard amino acid datasets (such as SwissProt, PDB Select 25, DisProt, or surface residues of monomeric proteins), or a representative sample of proteins from the organism under study, or a group of proteins that have a contrasting functional annotation to the query sample.
The graphical output of our tool is a bar chart composed of twenty data points (one for each amino acid), where bar heights indicate enrichment or depletion. The output is designed to assist the discovery of statistically significant composition anomalies by color-coding and sorting residues according to their physico-chemical properties. For example, if the property being tested is flexibility, the tool will group rigid amino acids on the left hand side of the plot and flexible amino acids on the right hand side of the plot.
Composition Profiler software was developed by Vladimir Vacic and Stefano Lonardi (University of California, Riverside), and Vladimir N. Uversky and A. Keith Dunker (Indiana University School of Medicine, Indianapolis).
In citing this program, please refer to:
Vacic V., Uversky V.N., Dunker A.K., and Lonardi S. "Composition Profiler: A tool for discovery and visualization of amino acid composition differences". BMC Bioinformatics. 8:211. (2007)
Composition Profiler was created using the Ruby programming language. Routines for calculating p-values were written in C and use numerical approximation functions from the Stephen L. Moshier's Cephes Math Library.