  During viral infection, intrahost mutation and recombination can lead to
significant evolution, resulting in a population of viruses that harbor
multiple haplotypes. The task of reconstructing these haplotypes from
short-read sequencing data is called viral quasispecies assembly, and it can be
categorized as a multiassembly problem. We consider the de novo version of the
problem, where no reference is available.
  We present ViQUF, a de novo viral quasispecies assembler that addresses
haplotype assembly and quantification. ViQUF obtains a first draft of the
assembly graph from a de Bruijn graph. Then, solving a min-cost flow over a
flow network built for each pair of adjacent vertices based on their paired-end
information creates an approximate paired assembly graph with suggested
frequency values as edge labels, which is the first frequency estimation. Then,
original haplotypes are obtained through a greedy path reconstruction guided by
a min-cost flow solution in the approximate paired assembly graph. ViQUF
outputs the contigs with their frequency estimations. Results on real and
simulated data show that ViQUF is at least four times faster using at most half
of the memory than previous methods, while maintaining, and in some cases
outperforming, the high quality of assembly and frequency estimation of overlap
graph-based methodologies, which are known to be more accurate but slower than
the de Bruijn graph-based approaches.
