PANDORA: Keyword-based analysis of protein sets by integration of annotation sources

Noam Kaplan, Avishay Vaaknin, Michal Linial*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

33 Scopus citations


Recent advances in high-throughput methods and the application of computational tools for automatic classification of proteins have made it possible to carry out large-scale proteomic analyses. Biological analysis and interpretation of sets of proteins is a time-consuming undertaking carried out manually by experts. We have developed PANDORA (Protein ANnotation Diagram ORiented Analysis), a web-based tool that provides an automatic representation of the biological knowledge associated with any set of proteins. PANDORA uses a unique approach of keyword-based graphical analysis that focuses on detecting subsets of proteins that share unique biological properties and the intersections of such sets. PANDORA currently supports SwissProt keywords, NCBI Taxonomy, InterPro entries and the hierarchical classification terms from ENZYME, SCOP and GO databases. The integrated study of several annotation sources simultaneously allows a representation of biological relations of structure, function, cellular location, taxonomy, domains and motifs. PANDORA is also integrated into the ProtoNet system, thus allowing testing thousands of automatically generated clusters. We illustrate how PANDORA enhances the biological understanding of large, non-uniform sets of proteins originating from experimental and computational sources, without the need for prior biological knowledge on individual proteins.

Original languageAmerican English
Pages (from-to)5617-5626
Number of pages10
JournalNucleic Acids Research
Issue number19
StatePublished - 1 Oct 2003

Bibliographical note

Funding Information:
We would like to thank Hillel Fleischer and Uri Inbar for excellent database management and Alexander Savenok for HTML help and for the integration PANDORA into the ProtoNet website. We would like to thank Nati Linial for valuable discussions. This study is partially supported by the Israeli Ministry of Defense and the Sudarsky Center for Computational Biology in the Hebrew University.


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