Pattern detection in biomolecules using synthesized random sequence

Abstract This paper presents a methodology which is able to: (1) synthesize a class of biomolecular sequences into a probabilistic pattern known as a random sequence for that class and (2) use the random sequence to search and detect subsequences pertaining to that class from a much longer sequence. The detection is achieved through an optimal matching of the random sequence against segments of the search sequence. Since the random sequence contains probabilistic characteristics of many sequences in the class, its comparison with search sequence segments is much more reliable than between two single sequences. The paper presents both the basic notion as well as an algorithm of the synthesis process. It also describes an experiment for detecting transfer RNA sequences embedded in a long DNA sequence derived from bovine mitochondrial genome. The successful detection is based on the optimal matching of the DNA sequence segments with the random sequence synthesized from 12 transfer RNA sequences.

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