  Genome assembly using high throughput data with short reads, arguably,
remains an unresolvable task in repetitive genomes, since when the length of a
repeat exceeds the read length, it becomes difficult to unambiguously connect
the flanking regions. The emergence of third generation sequencing (Pacific
Biosciences) with long reads enables the opportunity to resolve complicated
repeats that could not be resolved by the short read data. However, these long
reads have high error rate and it is an uphill task to assemble the genome
without using additional high quality short reads. Recently, Koren et al. 2012
proposed an approach to use high quality short reads data to correct these long
reads and, thus, make the assembly from long reads possible. However, due to
the large size of both dataset (short and long reads), error-correction of
these long reads requires excessively high computational resources, even on
small bacterial genomes. In this work, instead of error correction of long
reads, we first assemble the short reads and later map these long reads on the
assembly graph to resolve repeats.
  Contribution: We present a hybrid assembly approach that is both
computationally effective and produces high quality assemblies. Our algorithm
first operates with a simplified version of the assembly graph consisting only
of long contigs and gradually improves the assembly by adding smaller contigs
in each iteration. In contrast to the state-of-the-art long reads error
correction technique, which requires high computational resources and long
running time on a supercomputer even for bacterial genome datasets, our
software can produce comparable assembly using only a standard desktop in a
short running time.
