Authors: Fischer, Anja
Fischer, Frank
Jäger, Gerold
Keilwagen, Jens
Molitor, Paul
Grosse, Ivo
Title: Computational recognition of RNA splice sites by exact algorithms for the quadratic traveling salesman problem
Language (ISO): en
Abstract: One fundamental problem of bioinformatics is the computational recognition of DNA and RNA binding sites. Given a set of short DNA or RNA sequences of equal length such as transcription factor binding sites or RNA splice sites, the task is to learn a pattern from this set that allows the recognition of similar sites in another set of DNA or RNA sequences. Permuted Markov (PM) models and permuted variable length Markov (PVLM) models are two powerful models for this task, but the problem of finding an optimal PM model or PVLM model is NP-hard. While the problem of finding an optimal PM model or PVLM model of order one is equivalent to the traveling salesman problem (TSP), the problem of finding an optimal PM model or PVLM model of order two is equivalent to the quadratic TSP (QTSP). Several exact algorithms exist for solving the QTSP, but it is unclear if these algorithms are capable of solving QTSP instances resulting from RNA splice sites of at least 150 base pairs in a reasonable time frame. Here, we investigate the performance of three exact algorithms for solving the QTSP for ten datasets of splice acceptor sites and splice donor sites of five different species and find that one of these algorithms is capable of solving QTSP instances of up to 200 base pairs with a running time of less than two days.
Subject Headings: Splice site
Permuted Markov model
Permuted variable length Markov model
Quadratic traveling salesman problem
Combinatorial optimization
Dynamic programming
Branch and bound
Branch and cut
Integer linear programming
Subject Headings (RSWK): RNA-Spleißen
Markov-Modell
Travelling-salesman-Problem
URI: http://hdl.handle.net/2003/38280
http://dx.doi.org/10.17877/DE290R-20250
Issue Date: 2015-06-03
Rights link: https://creativecommons.org/licenses/by/4.0/
Appears in Collections:Lehrstuhl V Diskrete Optimierung

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