Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae
- Equal contributors
1 Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto ON M5G 1X5, Canada
2 Department of Medical Genetics and Microbiology, University of Toronto, Toronto ON M5S 1A8, Canada
3 Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0412, USA
4 Lewis-Sigler Institute for Integrative Genomics, Princeton University, Washington Road, Princeton, NJ 08544, USA
5 Department of Computer Science, Princeton University, NJ 08544, USA
6 Banting and Best Department of Medical Research, University of Toronto, Toronto ON M5G 1L6, Canada
Citation and License
Journal of Biology 2006, 5:11 doi:10.1186/jbiol36Published: 8 June 2006
The study of complex biological networks and prediction of gene function has been enabled by high-throughput (HTP) methods for detection of genetic and protein interactions. Sparse coverage in HTP datasets may, however, distort network properties and confound predictions. Although a vast number of well substantiated interactions are recorded in the scientific literature, these data have not yet been distilled into networks that enable system-level inference.
We describe here a comprehensive database of genetic and protein interactions, and associated experimental evidence, for the budding yeast Saccharomyces cerevisiae, as manually curated from over 31,793 abstracts and online publications. This literature-curated (LC) dataset contains 33,311 interactions, on the order of all extant HTP datasets combined. Surprisingly, HTP protein-interaction datasets currently achieve only around 14% coverage of the interactions in the literature. The LC network nevertheless shares attributes with HTP networks, including scale-free connectivity and correlations between interactions, abundance, localization, and expression. We find that essential genes or proteins are enriched for interactions with other essential genes or proteins, suggesting that the global network may be functionally unified. This interconnectivity is supported by a substantial overlap of protein and genetic interactions in the LC dataset. We show that the LC dataset considerably improves the predictive power of network-analysis approaches. The full LC dataset is available at the BioGRID (http://www.thebiogrid.org webcite) and SGD (http://www.yeastgenome.org/ webcite) databases.
Comprehensive datasets of biological interactions derived from the primary literature provide critical benchmarks for HTP methods, augment functional prediction, and reveal system-level attributes of biological networks.