Wavelet Analysis in Current Cancer Genome Research: A Survey

Abstract

With the rapid development of next generation sequencing technology, the amount of biological sequence data of the cancer genome increases exponentially, which calls for efficient and effective algorithms that may identify patterns hidden underneath the raw data that may distinguish cancer Achilles' heels. From a signal processing point of view, biological units of information, including DNA and protein sequences, have been viewed as one-dimensional signals. Therefore, researchers have been applying signal processing techniques to mine the potentially significant patterns within these sequences. More specifically, in recent years, wavelet transforms have become an important mathematical analysis tool, with a wide and ever increasing range of applications. The versatility of wavelet analytic techniques has forged new interdisciplinary bounds by offering common solutions to apparently diverse problems and providing a new unifying perspective on problems of cancer genome research. In this paper, we provide a survey of how wavelet analysis has been applied to cancer bioinformatics questions. Specifically, we discuss several approaches of representing the biological sequence data numerically and methods of using wavelet analysis on the numerical sequences.

Search for Latent Periodicity in Amino Acid Sequences with Insertions and Deletions

“…Spectral approaches enable the discovery of enough "fuzzy" periodicity in protein sequences without insertion(s) or deletion(s) of amino acids. Fourier transform, wavelet transform, information decomposition and some other methods can be attributed to a number of spectral methods (Tiwari et al, 1997;Lobzin and Chechetkin, 2000;Kravatskaya et al, 2011;Korotkov et al, 2003a;de Sousa Vieira, 1999;Meng et al, 2013;Suvorova et al, 2014;Sosa et al, 2013;Kumar et al, 2006). However, these approaches have a significant limitation, such as the fact that they do not allow the detection of periodicity with insertions and deletions.…”

Driver Missense Mutation Identification Using Feature Selection and Model Fusion

“…The amino acid mutation sample is represented by a numerical sequence according to the mapping scheme defined in Table 1. Wavelet analysis is then applied to generate the wavelets features (Meng et al, 2013). The Matlab wavelet toolbox was used to perform wavelet analysis, where a continuous wavelet transform based on Gaussian wavelets function is used to extract wavelet coefficients.…”

Computational implementation of the inverse continuous wavelet transform without a requirement of the admissibility condition

“…The modern applications of the continuous wavelet transform are focused, in particular, on a study of environmental time series [3,4], geo-and astrophysics [5,6,7], biophysics [8,9,10] and neuroscience, see an extensive review in the recently published book [11].…”