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Open Access Highly Accessed Research article

HINT: High-quality protein interactomes and their applications in understanding human disease

Jishnu Das12 and Haiyuan Yu12*

Author affiliations

1 Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA

2 Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA

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Citation and License

BMC Systems Biology 2012, 6:92  doi:10.1186/1752-0509-6-92

Published: 30 July 2012



A global map of protein-protein interactions in cellular systems provides key insights into the workings of an organism. A repository of well-validated high-quality protein-protein interactions can be used in both large- and small-scale studies to generate and validate a wide range of functional hypotheses.


We develop HINT ( webcite) - a database of high-quality protein-protein interactomes for human, Saccharomyces cerevisiae, Schizosaccharomyces pombe, and Oryza sativa. These were collected from several databases and filtered both systematically and manually to remove low-quality/erroneous interactions. The resulting datasets are classified by type (binary physical interactions vs. co-complex associations) and data source (high-throughput systematic setups vs. literature-curated small-scale experiments). We find strong sociological sampling biases in literature-curated datasets of small-scale interactions. An interactome without such sampling biases was used to understand network properties of human disease-genes - hubs are unlikely to cause disease, but if they do, they usually cause multiple disorders.


HINT is of significant interest to researchers in all fields of biology as it addresses the ubiquitous need of having a repository of high-quality protein-protein interactions. These datasets can be utilized to generate specific hypotheses about specific proteins and/or pathways, as well as analyzing global properties of cellular networks. HINT will be regularly updated and all versions will be tracked.

Interactomes; Networks; Protein-protein interactions; Disease