PARANA is an implementation of the method described in the paper Parsimonious Reconstruction of Network Evolution to perform parsimony based inference of ancestral biological networks. Given multiple extant networks and phylogenetic information relating extant nodes, PARANA finds a parsimonious set of ancestral interaction events (edge gains and losses) which explain the extant networks. The framework adopted by PARANA is able to represent network evolution under models that support gene duplication and loss and independent interaction gain and loss. The method works on both directed and undirected networks and can incorporate asymmetric interaction gain and loss costs. In contrast to previous approaches, our method does not require knowing the relative ordering of unrelated duplication events and thus, works on phylogenetic trees even where branch lengths are not provided.
Contact PARANA was developed by Rob Patro, Emre Sefer, Justin Malin, Guillaume Marçais, Saket Navlakha and Carl Kingsford at the Center for Bioinformatics and Computational Biology at the University of Maryland.
Please send any questions, comments or bug reports to ude.umc.sc@pbor.
PublicationsParsimonious Reconstruction of Network Evolution. R. Patro, E. Sefer, J. Malin, G. Marçais, S. Navlakha and C. Kingsford. Appeared in WABI 2011 [BibTeX]
Parsimonious Reconstruction of Network Evolution. R. Patro, E. Sefer, J. Malin, G. Marçais, S. Navlakha and C. Kingsford. Algorithms for Molecular Biology 7 (2012): 25
PresentationWABI 2011 Presentation
SoftwareDownload PARANA (v1.0)!
- EF-0849899, IIS-0812111, CCF-1053918
- 1R21AI085376, R01HG002945
- 2008-04049, 2010-15739-01
This material is based upon work supported by the National Science Foundation under Grant Numbers EF-0849899, IIS-0812111, CCF-1053918. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.