Using cellular automata to generate image representation for biological sequences.
A novel approach to visualize biological sequences is developed based on cellular automata (Wolfram, S. Nature 1984, 311, 419-424), a set of discrete dynamical systems in which space and time are discrete. By transforming the symbolic sequence codes into the digital codes, and using some optimal space-time evolvement rules of cellular automata, a biological sequence can be represented by a unique image, the so-called cellular automata image. Many important features, which are originally hidden in a long and complicated biological sequence, can be clearly revealed thru its cellular automata image. With biological sequences entering into databanks rapidly increasing in the post-genomic era, it is anticipated that the cellular automata image will become a very useful vehicle for investigation into their key features, identification of their function, as well as revelation of their "fingerprint". It is anticipated that by using the concept of the pseudo amino acid composition (Chou, K.C. Proteins: Structure, Function, and Genetics, 2001, 43, 246-255), the cellular automata image approach can also be used to improve the quality of predicting protein attributes, such as structural class and subcellular location.
Xiao
X
X
Bio-Informatics Research Center, Donghua University, Shanghai, China.
Shao
S
S
Ding
Y
Y
Huang
Z
Z
Chen
X
X
Chou
K-C
KC
D000818:Animals; D017934:Coronavirus / Q000235:genetics; D003628:Database Management Systems; D030541:Databases, Genetic; D006509:Hepatitis B / Q000235:genetics; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D051379:Mice; D045473:Severe acute respiratory syndrome-related coronavirus / Q000235:genetics; D012689:Sequence Homology, Nucleic Acid; D012984:Software; D016211:Transforming Growth Factor alpha / Q000235:genetics
D016211:Transforming Growth Factor alpha