  Background: How to extract useful information from complex biological
networks is a major goal in many fields, especially in genomics and proteomics.
We have shown in several works that iterative hierarchical clustering, as
implemented in the UVCluster program, is a powerful tool to analyze many of
those networks. However, the amount of computation time required to perform
UVCluster analyses imposed significant limitations to its use.
  Methodology/Principal Findings: We describe the suite Jerarca, designed to
efficiently convert networks of interacting units into dendrograms by means of
iterative hierarchical clustering. Jerarca is divided into three main sections.
First, weighted distances among units are computed using up to three different
approaches: a more efficient version of UVCluster and two new, related
algorithms called RCluster and SCluster. Second, Jerarca builds dendrograms
based on those distances, using well-known phylogenetic algorithms, such as
UPGMA or Neighbor-Joining. Finally, Jerarca provides optimal partitions of the
trees using statistical criteria based on the distribution of intra- and
intercluster connections. Outputs compatible with the phylogenetic software
MEGA and the Cytoscape package are generated, allowing the results to be easily
visualized.
  Conclusions/Significance: The four main advantages of Jerarca in respect to
UVCluster are: 1) Improved speed of a novel UVCluster algorithm; 2) Additional,
alternative strategies to perform iterative hierarchical clustering; 3)
Automatic evaluation of the hierarchical trees to obtain optimal partitions;
and, 4) Outputs compatible with popular software such as MEGA and Cytoscape.
