Biological interaction networks are conserved at the module level
TLDR
This work collected comprehensive high-throughput interaction datasets for four model organisms and carried out systematic analyses to explain the apparent lower conservation of interaction data when compared to the conservation of sequence data, showing that conservation is maintained between species, but mainly at the module level.Abstract:
Orthologous genes are highly conserved between closely related species and biological systems often utilize the same genes across different organisms. However, while sequence similarity often implies functional similarity, interaction data is not well conserved even for proteins with high sequence similarity. Several recent studies comparing high throughput data including expression, protein-protein, protein-DNA, and genetic interactions between close species show conservation at a much lower rate than expected. In this work we collected comprehensive high-throughput interaction datasets for four model organisms (S. cerevisiae, S. pombe, C. elegans, and D. melanogaster) and carried out systematic analyses in order to explain the apparent lower conservation of interaction data when compared to the conservation of sequence data. We first showed that several previously proposed hypotheses only provide a limited explanation for such lower conservation rates. We combined all interaction evidences into an integrated network for each species and identified functional modules from these integrated networks. We then demonstrate that interactions that are part of functional modules are conserved at much higher rates than previous reports in the literature, while interactions that connect between distinct functional modules are conserved at lower rates. We show that conservation is maintained between species, but mainly at the module level. Our results indicate that interactions within modules are much more likely to be conserved than interactions between proteins in different modules. This provides a network based explanation to the observed conservation rates that can also help explain why so many biological processes are well conserved despite the lower levels of conservation for the interactions of proteins participating in these processes. Accompanying website: http://www.sb.cs.cmu.edu/CrossSPread more
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References
More filters
Journal ArticleDOI
Gene Ontology: tool for the unification of biology
M Ashburner,Catherine A. Ball,Judith A. Blake,David Botstein,Heather Butler,J. M. Cherry,Allan Peter Davis,Kara Dolinski,Selina S. Dwight,J.T. Eppig,Midori A. Harris,David P. Hill,Laurie Issel-Tarver,Andrew Kasarskis,Suzanna E. Lewis,John C. Matese,Joel E. Richardson,M. Ringwald,Gerald M. Rubin,Gavin Sherlock +19 more
TL;DR: The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.
Journal ArticleDOI
Gene Expression Omnibus: NCBI gene expression and hybridization array data repository
TL;DR: The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data and provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-power gene expression and genomic hybridization experiments.
Journal ArticleDOI
BioGRID: a general repository for interaction datasets
Chris Stark,Bobby-Joe Breitkreutz,Teresa Reguly,Lorrie Boucher,Ashton Breitkreutz,Mike Tyers +5 more
TL;DR: BioGRID is a freely accessible database of physical and genetic interactions that includes >116 000 interactions from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens.
Journal ArticleDOI
An efficient algorithm for large-scale detection of protein families
TL;DR: This work presents a novel approach called TRIBE-MCL for rapid and accurate clustering of protein sequences into families based on precomputed sequence similarity information that has been rigorously tested and validated on a number of very large databases.
Journal ArticleDOI
Global landscape of protein complexes in the yeast Saccharomyces cerevisiae
Nevan J. Krogan,Gerard Cagney,Gerard Cagney,Haiyuan Yu,Gouqing Zhong,Xinghua Guo,Alexandr Ignatchenko,Joyce Li,Shuye Pu,Nira Datta,Aaron Tikuisis,Thanuja Punna,José M. Peregrín-Alvarez,Michael Shales,Xin Zhang,Michael Davey,Mark D. Robinson,Alberto Paccanaro,James E. Bray,Anthony Sheung,Bryan Beattie,Dawn Richards,Veronica Canadien,Atanas Iliev Lalev,Frank Mena,Peter D Wong,Andrei Starostine,Myra M. Canete,James Vlasblom,Samuel Wu,Chris Orsi,Sean R. Collins,Shamanta Chandran,Robin Haw,Jennifer J. Rilstone,Kiran Gandi,Natalie J. Thompson,Gabe Musso,Peter St Onge,Shaun Ghanny,Mandy H. Y. Lam,Gareth Butland,Amin M. Altaf-Ul,Shigehiko Kanaya,Ali Shilatifard,Erin K. O'Shea,Jonathan S. Weissman,C. James Ingles,Timothy P. Hughes,John Parkinson,Mark Gerstein,Shoshana J. Wodak,Andrew Emili,Jack Greenblatt +53 more
TL;DR: T tandem affinity purification was used to process 4,562 different tagged proteins of the yeast Saccharomyces cerevisiae to identify protein–protein interactions, which will help future studies on individual proteins as well as functional genomics and systems biology.
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