String comparison in terms of statistical evaluation applied on biological sequences
Protein sequences from all different organisms can be treated as texts written in a universal language where the alphabet consists of 20 distinct symbols, the amino-acids. The mapping of a protein sequence to its structure, functional dynamics and biological role then becomes analogous to the mapping of words to their semantic meaning in natural languages. This analogy can be exploited by applying statistical language modeling and text classification techniques for the advancement of biological sequences understanding. Here a new general strategy for measuring similarity between proteins is introduced. Our approach has its roots in computational linguistics and the related techniques for quantifying and comparing content in strings of characters. We experimented with different implementations having as ultimate goal the development of practical, computational efficient algorithms. The experimental analysis provides evidence for the usefulness and the potential of the new approach and motivates the further development of linguistics-related tools as a means to decipher the biological sequences.
n-grams entropy cross-entropy protein similarity exploratory data analysis