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Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae.

Reguly T, Breitkreutz A, Boucher L, Breitkreutz BJ, Hon GC, Myers CL, Parsons A, Friesen H, Oughtred R, Tong A, Stark C, Ho Y, Botstein D, Andrews B, Boone C, Troyanskya OG, Ideker T, Dolinski K, Batada NN, Tyers M - J. Biol. (2006)

Bottom Line: Sparse coverage in HTP datasets may, however, distort network properties and confound predictions.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.We show that the LC dataset considerably improves the predictive power of network-analysis approaches.

View Article: PubMed Central - HTML - PubMed

Affiliation: Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto ON M5G 1X5, Canada. teresa.reguly@utoronto.ca

ABSTRACT

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.

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Intersection of LC and HTP datasets. (a) Datasets were rendered with the Osprey visualization system [65] to show overlap between indicated LC and HTP datasets. n, number of nodes; i, number of interactions. (b) Coverage in the HTP physical interaction dataset (collated from five major HTP studies: Uetz et al. [5], Ito et al. [6], Ito et al. [7], Gavin et al. [9], Ho et al. [8]) overlaps strongly with coverage in the LC dataset. Proteins present only in the LC dataset were labeled first, followed by proteins present only in the individual HTP datasets. In all plots, a dot represents interaction between proteins on the x- and y-axes. As the networks are undirected, plots are symmetric about the x = y line. Self interactions were removed. (c) Overlap of individual HTP datasets with the LC dataset. Dot plots show all interactions from each HTP dataset partitioned according to proteins that are present in the LC-PI dataset (inside the boxed region) and those that are not (outside the boxed region). 'Ito' indicates data from Ito et al. [7] rather than Ito et al. [7]. The protein content is different for each dataset and so ordinates are not superimposable. The number of overlapping interactions between each HTP dataset and the LC dataset is shown in parentheses. Note that only a small fraction of interactions in each boxed region actually overlaps with the LC-PI dataset because of the high false-negative rate in HTP data. (d) The number of LC interactions in HTP datasets.
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Figure 4: Intersection of LC and HTP datasets. (a) Datasets were rendered with the Osprey visualization system [65] to show overlap between indicated LC and HTP datasets. n, number of nodes; i, number of interactions. (b) Coverage in the HTP physical interaction dataset (collated from five major HTP studies: Uetz et al. [5], Ito et al. [6], Ito et al. [7], Gavin et al. [9], Ho et al. [8]) overlaps strongly with coverage in the LC dataset. Proteins present only in the LC dataset were labeled first, followed by proteins present only in the individual HTP datasets. In all plots, a dot represents interaction between proteins on the x- and y-axes. As the networks are undirected, plots are symmetric about the x = y line. Self interactions were removed. (c) Overlap of individual HTP datasets with the LC dataset. Dot plots show all interactions from each HTP dataset partitioned according to proteins that are present in the LC-PI dataset (inside the boxed region) and those that are not (outside the boxed region). 'Ito' indicates data from Ito et al. [7] rather than Ito et al. [7]. The protein content is different for each dataset and so ordinates are not superimposable. The number of overlapping interactions between each HTP dataset and the LC dataset is shown in parentheses. Note that only a small fraction of interactions in each boxed region actually overlaps with the LC-PI dataset because of the high false-negative rate in HTP data. (d) The number of LC interactions in HTP datasets.

Mentions: A primary purpose of compiling the LC dataset was to provide a benchmark for HTP interaction studies. When each dataset is represented as a minimal spoke network model [34], the LC-PI network is of roughly the same size as the HTP-PI network, yet overlap between the two is only 14% (Figure 4a). To visualize the relative coverage of each dataset, dot-matrix representations of all pairwise interactions in each of the LC and HTP datasets were created and overlaid on the same ordinates. As expected, each dataset contains its own unique set of interactions (see Additional data file 3). To assess the relative distribution of interactions in the LC-PI versus HTP-PI datasets, full dot plots for each were compared, ordered first by proteins in the LC dataset then by proteins in the HTP dataset (Figure 4b). Interactions in the LC-PI dataset were uniform with respect to protein labels; that is, as expected there are no obvious areas of higher or lower interaction density across the approximately 3,000 proteins in the dataset. In the HTP-PI protein dataset, however, which contains interactions between 4,478 proteins, there were two distinct regions of interaction density: a high-density region that corresponded precisely to proteins defined in the LC-PI dataset (7.3 interactions per protein in LC-PI) and a low-density region that corresponded to interactions between proteins not in the LC-PI dataset (2.8 interactions per protein in HTP-PI). This indicates that there is a strong bias in interactions detected by HTP techniques. Analysis of each individual HTP-PI dataset revealed that bias towards previously studied proteins is inherent in the Gavin et al. [9], Ho et al. [8] and Uetz et al. [5] datasets (Figure 4c).


Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae.

Reguly T, Breitkreutz A, Boucher L, Breitkreutz BJ, Hon GC, Myers CL, Parsons A, Friesen H, Oughtred R, Tong A, Stark C, Ho Y, Botstein D, Andrews B, Boone C, Troyanskya OG, Ideker T, Dolinski K, Batada NN, Tyers M - J. Biol. (2006)

Intersection of LC and HTP datasets. (a) Datasets were rendered with the Osprey visualization system [65] to show overlap between indicated LC and HTP datasets. n, number of nodes; i, number of interactions. (b) Coverage in the HTP physical interaction dataset (collated from five major HTP studies: Uetz et al. [5], Ito et al. [6], Ito et al. [7], Gavin et al. [9], Ho et al. [8]) overlaps strongly with coverage in the LC dataset. Proteins present only in the LC dataset were labeled first, followed by proteins present only in the individual HTP datasets. In all plots, a dot represents interaction between proteins on the x- and y-axes. As the networks are undirected, plots are symmetric about the x = y line. Self interactions were removed. (c) Overlap of individual HTP datasets with the LC dataset. Dot plots show all interactions from each HTP dataset partitioned according to proteins that are present in the LC-PI dataset (inside the boxed region) and those that are not (outside the boxed region). 'Ito' indicates data from Ito et al. [7] rather than Ito et al. [7]. The protein content is different for each dataset and so ordinates are not superimposable. The number of overlapping interactions between each HTP dataset and the LC dataset is shown in parentheses. Note that only a small fraction of interactions in each boxed region actually overlaps with the LC-PI dataset because of the high false-negative rate in HTP data. (d) The number of LC interactions in HTP datasets.
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Figure 4: Intersection of LC and HTP datasets. (a) Datasets were rendered with the Osprey visualization system [65] to show overlap between indicated LC and HTP datasets. n, number of nodes; i, number of interactions. (b) Coverage in the HTP physical interaction dataset (collated from five major HTP studies: Uetz et al. [5], Ito et al. [6], Ito et al. [7], Gavin et al. [9], Ho et al. [8]) overlaps strongly with coverage in the LC dataset. Proteins present only in the LC dataset were labeled first, followed by proteins present only in the individual HTP datasets. In all plots, a dot represents interaction between proteins on the x- and y-axes. As the networks are undirected, plots are symmetric about the x = y line. Self interactions were removed. (c) Overlap of individual HTP datasets with the LC dataset. Dot plots show all interactions from each HTP dataset partitioned according to proteins that are present in the LC-PI dataset (inside the boxed region) and those that are not (outside the boxed region). 'Ito' indicates data from Ito et al. [7] rather than Ito et al. [7]. The protein content is different for each dataset and so ordinates are not superimposable. The number of overlapping interactions between each HTP dataset and the LC dataset is shown in parentheses. Note that only a small fraction of interactions in each boxed region actually overlaps with the LC-PI dataset because of the high false-negative rate in HTP data. (d) The number of LC interactions in HTP datasets.
Mentions: A primary purpose of compiling the LC dataset was to provide a benchmark for HTP interaction studies. When each dataset is represented as a minimal spoke network model [34], the LC-PI network is of roughly the same size as the HTP-PI network, yet overlap between the two is only 14% (Figure 4a). To visualize the relative coverage of each dataset, dot-matrix representations of all pairwise interactions in each of the LC and HTP datasets were created and overlaid on the same ordinates. As expected, each dataset contains its own unique set of interactions (see Additional data file 3). To assess the relative distribution of interactions in the LC-PI versus HTP-PI datasets, full dot plots for each were compared, ordered first by proteins in the LC dataset then by proteins in the HTP dataset (Figure 4b). Interactions in the LC-PI dataset were uniform with respect to protein labels; that is, as expected there are no obvious areas of higher or lower interaction density across the approximately 3,000 proteins in the dataset. In the HTP-PI protein dataset, however, which contains interactions between 4,478 proteins, there were two distinct regions of interaction density: a high-density region that corresponded precisely to proteins defined in the LC-PI dataset (7.3 interactions per protein in LC-PI) and a low-density region that corresponded to interactions between proteins not in the LC-PI dataset (2.8 interactions per protein in HTP-PI). This indicates that there is a strong bias in interactions detected by HTP techniques. Analysis of each individual HTP-PI dataset revealed that bias towards previously studied proteins is inherent in the Gavin et al. [9], Ho et al. [8] and Uetz et al. [5] datasets (Figure 4c).

Bottom Line: Sparse coverage in HTP datasets may, however, distort network properties and confound predictions.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.We show that the LC dataset considerably improves the predictive power of network-analysis approaches.

View Article: PubMed Central - HTML - PubMed

Affiliation: Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto ON M5G 1X5, Canada. teresa.reguly@utoronto.ca

ABSTRACT

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.

Show MeSH