Table of Contents
Advances in Bioinformatics
Volume 2015, Article ID 909765, 10 pages
Research Article

CISAPS: Complex Informational Spectrum for the Analysis of Protein Sequences

1Department of Genetics, University of Leicester, University Road, Leicester LE1 7RH, UK
2Department of Computer Science and Digital Technologies, Faculty of Engineering and Environment, The University of Northumbria at Newcastle, Newcastle-upon-Tyne NE1 8ST, UK
3Department of Computer Engineering, Yildiz Technical University, 34220 Istanbul, Turkey

Received 28 July 2014; Revised 27 November 2014; Accepted 4 December 2014

Academic Editor: Tatsuya Akutsu

Copyright © 2015 Charalambos Chrysostomou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Complex informational spectrum analysis for protein sequences (CISAPS) and its web-based server are developed and presented. As recent studies show, only the use of the absolute spectrum in the analysis of protein sequences using the informational spectrum analysis is proven to be insufficient. Therefore, CISAPS is developed to consider and provide results in three forms including absolute, real, and imaginary spectrum. Biologically related features to the analysis of influenza A subtypes as presented as a case study in this study can also appear individually either in the real or imaginary spectrum. As the results presented, protein classes can present similarities or differences according to the features extracted from CISAPS web server. These associations are probable to be related with the protein feature that the specific amino acid index represents. In addition, various technical issues such as zero-padding and windowing that may affect the analysis are also addressed. CISAPS uses an expanded list of 611 unique amino acid indices where each one represents a different property to perform the analysis. This web-based server enables researchers with little knowledge of signal processing methods to apply and include complex informational spectrum analysis to their work.