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Open Access Highly Accessed Research article

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

Tomer Shlomi1, Daniel Segal2, Eytan Ruppin13 and Roded Sharan1*

Author affiliations

1 School of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel

2 Dept. of Molecular Microbiology and Biotechnology, Tel-Aviv University, Tel-Aviv 69978, Israel

3 School of Medicine, Tel-Aviv University, Tel-Aviv, Tel-Aviv 69978, Israel

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Citation and License

BMC Bioinformatics 2006, 7:199  doi:10.1186/1471-2105-7-199

Published: 10 April 2006



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.


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.


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.