Double Digest Revisited: Complexity and Approximability in the Presence of Noisy Data | |
Authors: | Mark Cieliebak, Stephan Eidenbenz, and Gerhard J. Woeginger |
Reference: | In "Proceedings of the 9th International Computing and Combinatorics Conference (COCOON 2003)", Montana, USA, July 2003. Springer, LNCS 2697, pp. 519-527, 2003 |
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Abstract: |
We revisit the Double Digest problem, which occurs
in sequencing of large DNA strings and consists of reconstructing the
relative positions of cut sites from two different enzymes: we first
show that Double Digest is strongly NP-complete, improving upon previous
results that only showed weak NP-completeness. Even the (experimentally
more meaningful) variation in which we disallow coincident cut sites
turns out to be strongly NP-complete. In a second part, we model errors
in data as they occur in real-life experiments: we propose several optimization
variations of Double Digest that model partial cleavage errors. We then
show APX-completeness for most of these variations. In a third part,
we investigate these variations with the additional restriction that
conincident cut sites are disallowed, and we show that it is NP-hard
to even find feasible solutions in this case, thus making it impossible
to guarantee any approximation ratio at all.
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Remarks: |