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Whole genome duplication (WGD) has made a significant contribution to many eukaryotic genomes including yeast, plants, and vertebrates. Following WGD, some ohnologs (WGD paralogs) remain in the genome arranged in blocks of conserved gene order and content (paralogons). However, the most common outcome is loss of one of the ohnolog pair. It is unclear what factors, if any, govern gene loss from paralogons. Recent studies have reported physical clustering (genetic linkage) of functionally linked (interacting) genes in the human genome and propose a biological significance for the clustering of interacting genes such as coexpression or preservation of epistatic interactions. Here we conduct a novel test of a hypothesis that functionally linked genes in the same paralogon are preferentially retained in cis after WGD. We compare the number of protein-protein interactions (PPIs) between linked singletons within a paralogon (defined as cis-PPIs) with that of PPIs between singletons across paralogon pairs (defined as trans-PPIs). We find that paralogons in which the number of cis-PPIs is greater than that of trans-PPIs are significantly enriched in human and yeast. The trend is similar in plants, but it is difficult to assess statistical significance due to multiple, overlapping WGD events. Interestingly, human singletons participating in cis-PPIs tend to be classified into "response to stimulus." We uncover strong evidence of biased gene loss after WGD, which further supports the hypothesis of biologically significant gene clusters in eukaryotic genomes. These observations give us new insight for understanding the evolution of genome structure and of protein interaction networks.
Pubmed ID: 22835904 RIS Download
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Institute to advance genomics in support of the DOE missions related to clean energy generation and environmental characterization and cleanup. Supported by the DOE Office of Science, the DOE JGI unites the expertise at Lawrence Berkeley National Laboratory, Lawrence Livermore National Laboratory, and the HudsonAlpha Institute for Biotechnology. The facility provides integrated high-throughput sequencing and computational analysis that enable systems-based scientific approaches to these challenges.
View all literature mentionsDatabase to catalog experimentally determined interactions between proteins combining information from a variety of sources to create a single, consistent set of protein-protein interactions that can be downloaded in a variety of formats. The data were curated, both, manually and also automatically using computational approaches that utilize the the knowledge about the protein-protein interaction networks extracted from the most reliable, core subset of the DIP data. Because the reliability of experimental evidence varies widely, methods of quality assessment have been developed and utilized to identify the most reliable subset of the interactions. This CORE set can be used as a reference when evaluating the reliability of high-throughput protein-protein interaction data sets, for development of prediction methods, as well as in the studies of the properties of protein interaction networks. Tools are available to analyze, visualize and integrate user's own experimental data with the information about protein-protein interactions available in the DIP database. The DIP database lists protein pairs that are known to interact with each other. By interact they mean that two amino acid chains were experimentally identified to bind to each other. The database lists such pairs to aid those studying a particular protein-protein interaction but also those investigating entire regulatory and signaling pathways as well as those studying the organization and complexity of the protein interaction network at the cellular level. Registration is required to gain access to most of the DIP features. Registration is free to the members of the academic community. Trial accounts for the commercial users are also available.
View all literature mentionsCurated protein-protein and genetic interaction repository of raw protein and genetic interactions from major model organism species, with data compiled through comprehensive curation efforts.
View all literature mentionsThe SciCrunch Infrastructure was developed as a cooperative data platform to be used by diverse communities in making data more FAIR.
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