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Sparse approaches for the exact distribution of patterns in long state sequences generated by a Markov source. (English) Zbl 1291.60148
Summary: We present two novel approaches for the computation of the exact distribution of a pattern in a long sequence. Both approaches take into account the sparse structure of the problem and are two-part algorithms. The first approach relies on a partial recursion after a fast computation of the second largest eigenvalue of the transition matrix of a Markov chain embedding. The second approach uses fast Taylor expansions of an exact bivariate rational reconstruction of the distribution.
We illustrate the interest of both approaches on a simple toy example and two biological applications: the transcription factors of the Human Chromosome 10 and the PROSITE signatures of functional motifs in proteins. On these examples our methods demonstrate their complementarity and their ability to extend the domain of feasibility for exact computations in pattern problems to a new level.

MSC:
60J10 Markov chains (discrete-time Markov processes on discrete state spaces)
60J20 Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.)
92D20 Protein sequences, DNA sequences
68Q25 Analysis of algorithms and problem complexity
Software:
RE2; PCRE; SPatt
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