TY - JOUR
T1 - Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae
AU - Reguly, Teresa
AU - Breitkreutz, Ashton
AU - Boucher, Lorrie
AU - Breitkreutz, Bobby Joe
AU - Hon, Gary C.
AU - Myers, Chad L.
AU - Parsons, Ainslie
AU - Friesen, Helena
AU - Oughtred, Rose
AU - Tong, Amy
AU - Stark, Chris
AU - Ho, Yuen
AU - Botstein, David
AU - Andrews, Brenda
AU - Boone, Charles
AU - Troyanskaya, Olga G.
AU - Ideker, Trey
AU - Dolinski, Kara
AU - Batada, Nizar N.
AU - Tyers, Mike
PY - 2006
Y1 - 2006
N2 - Background. 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. Results. 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) and SGD (http://www.yeastgenome.org/) databases. Conclusion. 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.
AB - Background. 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. Results. 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) and SGD (http://www.yeastgenome.org/) databases. Conclusion. 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.
UR - http://www.scopus.com/inward/record.url?scp=33847064282&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33847064282&partnerID=8YFLogxK
U2 - 10.1186/jbiol36
DO - 10.1186/jbiol36
M3 - Article
C2 - 16762047
AN - SCOPUS:33847064282
SN - 1478-5854
VL - 5
JO - Journal of Biology
JF - Journal of Biology
M1 - 11
ER -