Optimal robust non-unique probe selection using Integer Linear Programming
Author(s) -
Gunnar W. Klau,
Sven Rahmann,
Alexander Schliep,
Martin Vingron,
Knut Reinert
Publication year - 2004
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bth936
Subject(s) - sample (material) , integer programming , set (abstract data type) , dna microarray , computer science , selection (genetic algorithm) , sample size determination , oligomer restriction , integer (computer science) , algorithm , oligonucleotide , decoding methods , computational biology , data mining , artificial intelligence , biology , mathematics , genetics , gene , statistics , programming language , chemistry , gene expression , chromatography
Besides their prevalent use for analyzing gene expression, microarrays are an efficient tool for biological, medical and industrial applications due to their ability to assess the presence or absence of biological agents, the targets, in a sample. Given a collection of genetic sequences of targets one faces the challenge of finding short oligonucleotides, the probes, which allow detection of targets in a sample. Each hybridization experiment determines whether the probe binds to its corresponding sequence in the target. Depending on the problem, the experiments are conducted using either unique or non-unique probes and usually assume that only one target is present in the sample. The problem at hand is to compute a design, i.e. a minimal set of probes that allows to infer the targets in the sample from the result of the hybridization experiment. If we allow to test for more than one target in the sample, the design of the probe set becomes difficult in the case of non-unique probes.
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