Non-Unique oligonucleotide probe selection heuristics

The non-unique probe selection problem consists of selecting both unique and nonunique oligonucleotide probes for oligonucleotide microarrays, which are widely used tools to identify viruses or bacteria in biological samples. The non-unique probes, designed to hybridize to at least one target, are used as alternatives when the design of unique probes is particularly difficult for the closely related target genes. The goal of the non-unique probe selection problem is to determine a smallest set of probes able to identify all targets present in a biological sample. This problem is known to be NP-hard. In this thesis, several novel heuristics are presented based on greedy strategy, genetic algorithms and evolutionary strategy respectively for the minimization problem arisen from the non-unique probe selection using the best-known ILP formulation. Experiment results show that our methods are capable of reducing the number of probes required over the state-of-the-art methods

Decoding Algorithms in Pooling Designs with Inhibitors and Error-Tolerance

Abstract: Pooling designs are used in DNA library screening to efficiently distinguish positive clones from negative clones, which is of fundamental importance in studying gene functions and many other applications in biology. One of the challenges of pooling designs is to design decoding algorithms for determining whether a clone is positive based on the test outcomes and a binary matrix representing the pools. This problem becomes more difficult in practice due to errors in biological experiments. More challenging, in some applications, besides positive and negative clones, there is a third category of clones called ”inhibitors” whose effect is to neutralize positives. In this paper, we present a novel decoding algorithm identifying all positive clones with the presence o