Jerarca: efficient analysis of complex networks using hierarchical clustering.

PloS One
Rodrigo Aldecoa, Ignacio Marín

Abstract

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. 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 visualiz...Continue Reading

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Citations

Sep 13, 2011·PloS One·Rodrigo Aldecoa, Ignacio Marín
Jan 16, 2013·Scientific Reports·Rodrigo Aldecoa, Ignacio Marín
Jul 19, 2013·Scientific Reports·Rodrigo Aldecoa, Ignacio Marín
Sep 24, 2015·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Junzhong JiAidong Zhang
Sep 8, 2017·International Journal of Molecular Sciences·Bo Li, Bo Liao
Apr 3, 2012·Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics·Rodrigo Aldecoa, Ignacio Marín
Apr 8, 2017·Scientific Reports·Jingcheng FuJianliang Wu

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Software Mentioned

Cytoscape
Jerarca
MEGA
Linux
SCluster
UPGMA
UVCluster
Windows
RCluster
Spanish

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