Parallel Algorithms for DNA Probe Placement on Small Oligonucleotide Arrays

TL;DR

This paper introduces two parallel algorithms based on local search for the border length minimization problem in DNA probe placement, significantly improving results for small microarrays with up to 1156 probes.

Contribution

The paper presents novel parallel algorithms specifically designed for the BLMP, outperforming previous methods on small microarrays.

Findings

Better performance than previous algorithms on small arrays

Effective reduction of border lengths in probe placement

Applicable to arrays with up to 1156 probes

Abstract

Oligonucleotide arrays are used in a wide range of genomic analyses, such as gene expression profiling, comparative genomic hybridization, chromatin immunoprecipitation, SNP detection, etc. During fabrication, the sites of an oligonucleotide array are selectively exposed to light in order to activate oligonucleotides for further synthesis. Optical effects can cause unwanted illumination at masked sites that are adjacent to the sites intentionally exposed to light. This results in synthesis of unforeseen sequences in masked sites and compromises interpretation of experimental data. To reduce such uncertainty, one can exploit freedom in how probes are assigned to array sites. The border length minimization problem (BLMP) seeks a placement of probes that minimizes the sum of border lengths in all masks. In this paper, we propose two parallel algorithms for the BLMP. The proposed parallel algorithms have the local-search paradigm at their core, and are especially developed for the BLMP. The results reported show that, for small microarrays with at most 1156 probes, the proposed parallel algorithms perform better than the best previous algorithms.