  The large volumes of sequencing data required to sample complex environments
deeply pose new challenges to sequence analysis approaches. De novo metagenomic
assembly effectively reduces the total amount of data to be analyzed but
requires significant computational resources. We apply two pre-assembly
filtering approaches, digital normalization and partitioning, to make large
metagenome assemblies more comput\ ationaly tractable. Using a human gut mock
community dataset, we demonstrate that these methods result in assemblies
nearly identical to assemblies from unprocessed data. We then assemble two
large soil metagenomes from matched Iowa corn and native prairie soils. The
predicted functional content and phylogenetic origin of the assembled contigs
indicate significant taxonomic differences despite similar function. The
assembly strategies presented are generic and can be extended to any
metagenome; full source code is freely available under a BSD license.
