Nucleic Acids Research, 2003, Vol. 31, No. 19 5617-5626
© 2003 Oxford University Press
PANDORA: keyword-based analysis of protein sets by integration of annotation sources
Department of Biological Chemistry, Institute of Life Sciences and 1 School of Computer Science and Engineering, The Hebrew University, Jerusalem 91904, Israel
*To whom correspondence should be addressed. Tel: +972 2 6585425; Fax: +972 2 6586448; Email: michall{at}cc.huji.ac.il
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.
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  J. E. Gewehr, V. Hintermair, and R. Zimmer AutoSCOP: automated prediction of SCOP classifications using unique pattern-class mappings Bioinformatics, May 15, 2007; 23(10): 1203 - 1210. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J.-P. R Kayser, J. G Kim, R. L Cerny, and J. L Vallet Global characterization of porcine intrauterine proteins during early pregnancy Reproduction, February 1, 2006; 131(2): 379 - 388. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 I. Bahir and M. Linial ProTeus: identifying signatures in protein termini Nucleic Acids Res., July 1, 2005; 33(suppl_2): W277 - W280. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
N. Kaplan, O. Sasson, U. Inbar, M. Friedlich, M. Fromer, H. Fleischer, E. Portugaly, N. Linial, and M. Linial ProtoNet 4.0: A hierarchical classification of one million protein sequences Nucleic Acids Res., January 1, 2005; 33(suppl_1): D216 - D218. [Abstract] [Full Text] [PDF]
|
|