Since no technique exists to read the full DNA, DNA fragments (called reads) are generated through chemical processing, and algorithms try to reconstruct the complete DNA from them. Genome reassembling is a challenging optimization problem since reads are randomly sequenced, have overlapped and deleted parts, and exist in a large number.

After reviewing the available methods, the authors opt for nature-inspired algorithms. They extend ant colony optimization (ACO) with a local score guide, creating LSACO. In LSACO, ants assigned to reads move to unvisited places with a probability that depends on the local alignment score and the pheromone level, in order to create a path from a dummy starting point to a place where the solution length exceeds a threshold.

The authors compare LSACO with other nature-inspired algorithms on 22 sequences of different organisms, with a maximum length of 100,000. The positive result is that LSACO has the best accuracy. The drawback is that the computational power requirement is usually the greatest.

Even though this paper is not easy to read, the results for such a difficult problem are interesting. A final note: since it is common in bioinformatics to deliver open-source code, will this software be made freely available?