  Metagenomics offers a way to analyze biotopes at the genomic level and to
reach functional and taxonomical conclusions. The bio-analyzes of large
metagenomic projects face critical limitations: complex metagenomes cannot be
assembled and the taxonomical or functional annotations are much smaller than
the real biological diversity. This motivated the development of de novo
metagenomic read comparison approaches to extract information contained in
metagenomic datasets.
  However, these new approaches do not scale up large metagenomic projects, or
generate an important number of large intermediate and result files. We
introduce Commet ("COmpare Multiple METagenomes"), a method that provides
similarity overview between all datasets of large metagenomic projects.
  Directly from non-assembled reads, all against all comparisons are performed
through an efficient indexing strategy. Then, results are stored as bit
vectors, a compressed representation of read files, that can be used to further
combine read subsets by common logical operations. Finally, Commet computes a
clusterization of metagenomic datasets, which is visualized by dendrogram and
heatmaps.
  Availability: http://github.com/pierrepeterlongo/commet
