Assembly of repetitive regions using next-generation sequencing data

High read depth can be used to assemble short sequence repeats. The existing genome assemblers fail in repetitive regions of longer than average read. I propose a new algorithm for a DNA assembly which uses the relative frequency of reads to properly reconstruct repetitive sequences. The mathematical model shows the upper limits of accuracy of the results as a function of read coverage. For high coverage, the estimation error depends linearly on repetitive sequence length and inversely proportional to the sequencing coverage. The algorithm requires high read depth, provided by the next-generation sequencers and could use the existing data. The tests on errorless reads, generated in silico from several model genomes, pointed the properly reconstructed repetitive sequences, where existing assemblers fail. The C++ sources, the Python scripts and the additional data are available at http://dnaasm.sourceforge. org.