Recent advances in high throughput techniques have made it possible to conduct biomedical research on a larger scale than was previously possible. These efforts often involve large groups of scientists from multiple institutions working in close collaboration on high throughput experiments, data collection, and analysis. There is little precedent in the biological sciences for executing or evaluating such large scale endeavors, but in the latter case a logical place to start is the product of those endeavors, namely publications. As we demonstrate below, the organization and output of a collaboration is very well reflected by patterns that can be extracted from its publication list in Figure .
Figure 3 Collaborative organization of the Northeast Structural Genomics (NESG) consortium. (a) Paper nodes linked by co-authorship edges show four major groups of publications, roughly corresponding to individual laboratories from which they were published. Here, (more ...)
The Protein Structure Initiative (PSI) is a large-scale effort led by the US National Institutes of Health that is aimed at streamlining the process of three-dimensional protein structure determination, with the long range goal of providing three-dimensional structures of most proteins in nature. Nine structural genomics research centers are supported by the PSI, each of which has its own expertise, organization, and research focus [16
]. To demonstrate the versatility of PubNet, we generated several graphs based on publication lists from each PSI center (Figure ), including the Northeast Structural Genomics (NESG) consortium.
Figure 4 Author/co-authorship graphs for nine pilot centers of the Protein Structure Initiative. Publications were collected from the official publication lists of the following centers: Berkeley Center for Structural Genomics (BSGC), Center for Eukaroytic Structural (more ...)
Structural genomics centers attempt to solve structures at very high throughput, and each center has its own unique approach to accomplish this task. Because the PSI is still in its pilot stages, it is yet to be determined which approach is the most successful. Here we show how organizational, geographic, and social patterns of large collaborative research efforts are reflected in their publications.
Collaborative organization of single consortium
We begin by illustrating the types of relationships that can be extracted from a single query (Figure ). A query consisting of a list of all NESG PubMed IDs was analyzed using four different combinations of node and edge types, and each yielded strikingly different graph structures. Depending on the parameters that were specified to generate the graph, these linkages may correspond to similarity between papers, frequency of copublication between two authors (for a given query), common geographic sources for publications, and so on. The scalable vector graphics formats supported by PubNet allow one to zoom in on specific regions in the graph. Each node in the graph image is hyperlinked to a detailed textual report, which includes a hyperlinked list of all outgoing edges and a list of all neighboring nodes with their respective edges. Thus, starting directly from the graphical output, it is possible to explore specific node-edge linkages in detail.
In the graph shown for the NESG consortium in Figure , nodes are authors (researchers) and edges represent co-authorships on publications. It demonstrates the confederated but coordinated approach used by the NESG consortium, which includes two protein sample production centers, at least six different sites at which three-dimensional structures are determined by nuclear magnetic resonance or X-ray crystallography, and a loosely coupled group of some dozen laboratories working on various aspects of the technology development and annotation.
Comparison of several consortia
We also compare the publication authorship patterns of each of the PSI centers in Figure , using nodes to represent authors and edges to represent co-authorship. Because a single set of parameters was used across multiple queries, the underlying relationships between nodes are identical for each graph, and so differing graph structures correspond to variations in the global structure of these relationships. A diverse array of graph structures is evident, highlighting significant differences in size, frequency in publication, and degree of cooperation across the consortia. For example, the Tuberculosis Structural Genomics consortium [17
] conducts its experiments in small separate groups, whereas the Joint Center for Structural Genomics [18
] uses a more centralized approach. Groups such as the NESG [19
] and New York Structural Genomics Research Consortium [20
] employ an intermediate approach, in which central groups are tightly clustered but also linked to other groups in a collaborative pipeline.