QPath: a method for querying pathways in a protein-protein interaction network

@article{citeulike:2580569, abstract = {Background Sequence comparison is one of the most prominent tools in biological research, and is instrumental in studying gene function and evolution. The rapid development of high-throughput technologies for measuring protein interactions calls for extending this fundamental operation to the level of pathways in protein networks. Results We present a comprehensive framework for protein network searches using pathway queries. Given a linear query pathway and a network of interest, our algorithm, QPath, efficiently searches the network for homologous pathways, allowing both insertions and deletions of proteins in the identified pathways. Matched pathways are automatically scored according to their variation from the query pathway in terms of the protein insertions and deletions they employ, the sequence similarity of their constituent proteins to the query proteins, and the reliability of their constituent interactions. We applied QPath to systematically infer protein pathways in fly using an extensive collection of 271 putative pathways from yeast. QPath identified 69 conserved pathways whose members were both functionally enriched and coherently expressed. The resulting pathways tended to preserve the function of the original query pathways, allowing us to derive a first annotated map of conserved protein pathways in fly. Conclusion Pathway homology searches using QPath provide a powerful approach for identifying biologically significant pathways and inferring their function. The growing amounts of protein interactions in public databases underscore the importance of our network querying framework for mining protein network data.}, author = {Shlomi, Tomer and Segal, Daniel and Ruppin, Eytan and Sharan, Roded }, citeulike-article-id = {2580569}, doi = {10.1186/1471-2105-7-199}, journal = {BMC Bioinformatics}, keywords = {algorithms, biology, complex, graph}, pages = {199+}, posted-at = {2008-03-24 14:07:13}, priority = {2}, title = {QPath: a method for querying pathways in a protein-protein interaction network}, url = {http://dx.doi.org/10.1186/1471-2105-7-199}, volume = {7}, year = {2006} }

TY - JOUR ID - citeulike:2580569 L3 - citeulike-article-id:2580569 TI - QPath: a method for querying pathways in a protein-protein interaction network JF - BMC Bioinformatics VL - 7 SP - 199 N2 - Background Sequence comparison is one of the most prominent tools in biological research, and is instrumental in studying gene function and evolution. The rapid development of high-throughput technologies for measuring protein interactions calls for extending this fundamental operation to the level of pathways in protein networks. Results We present a comprehensive framework for protein network searches using pathway queries. Given a linear query pathway and a network of interest, our algorithm, QPath, efficiently searches the network for homologous pathways, allowing both insertions and deletions of proteins in the identified pathways. Matched pathways are automatically scored according to their variation from the query pathway in terms of the protein insertions and deletions they employ, the sequence similarity of their constituent proteins to the query proteins, and the reliability of their constituent interactions. We applied QPath to systematically infer protein pathways in fly using an extensive collection of 271 putative pathways from yeast. QPath identified 69 conserved pathways whose members were both functionally enriched and coherently expressed. The resulting pathways tended to preserve the function of the original query pathways, allowing us to derive a first annotated map of conserved protein pathways in fly. Conclusion Pathway homology searches using QPath provide a powerful approach for identifying biologically significant pathways and inferring their function. The growing amounts of protein interactions in public databases underscore the importance of our network querying framework for mining protein network data. KW - algorithms KW - biology KW - complex KW - graph AU - Shlomi, Tomer AU - Segal, Daniel AU - Ruppin, Eytan AU - Sharan, Roded PY - 2006/// UR - http://dx.doi.org/10.1186/1471-2105-7-199 ER -