  Motivation: Second generation sequencing technology makes it feasible for
many researches to obtain enough sequence reads to attempt the de novo assembly
of higher eukaryotes (including mammals). De novo assembly not only provides a
tool for understanding wide scale biological variation, but within human
bio-medicine, it offers a direct way of observing both large scale structural
variation and fine scale sequence variation. Unfortunately, improvements in the
computational feasibility for de novo assembly have not matched the
improvements in the gathering of sequence data. This is for two reasons: the
inherent computational complexity of the problem, and the in-practice memory
requirements of tools.
  Results: In this paper we use entropy compressed or succinct data structures
to create a practical representation of the de Bruijn assembly graph, which
requires at least a factor of 10 less storage than the kinds of structures used
by deployed methods. In particular we show that when stored succinctly, the de
Bruijn assembly graph for homo sapiens requires only 23 gigabytes of storage.
Moreover, because our representation is entropy compressed, in the presence of
sequencing errors it has better scaling behaviour asymptotically than
conventional approaches.
