Abstract
In some areas of bioinformatics (like protein folding or sequence alignment) the full alphabet of amino acid symbols is not necessary. Often, better results are received with simplified alphabets. In general, simplified alphabets are as universal as possible. In this paper we show that this concept may not be optimal. We present a genetic algorithm for alphabet simplifying and we use it in a method based on global sequence alignment. We demonstrate that our algorithm is much faster and produces better results than the previously presented genetic algorithm. We also compare alphabets constructed on the base of universal substitution matrices like BLOSUM with our alphabets built through sequence alignment and propose a new coefficient describing the value of alphabets in the sequence alignment context. Finally we show that our simplified alphabets give better results in a sequence classification (using k-NN classifier), than most previously presented simplified alphabets and better than full 20-letter alphabet.
Keywords
- amino acid alphabet
- sequence alignment
- substitution matrices
- protein classification.
The research has been partially supported by grant No 3 T11C 002 29 received from Polish Ministry of Education and Science.
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Lenckowski, J., Walczak, K. (2007). Simplifying Amino Acid Alphabets Using a Genetic Algorithm and Sequence Alignment. In: Marchiori, E., Moore, J.H., Rajapakse, J.C. (eds) Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics. EvoBIO 2007. Lecture Notes in Computer Science, vol 4447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71783-6_12
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DOI: https://doi.org/10.1007/978-3-540-71783-6_12
Publisher Name: Springer, Berlin, Heidelberg
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