PANDORA: Analysis of protein and peptide sets through the hierarchical integration of annotations

Nadav Rappoport, Menachem Fromer, Regev Schweiger, Michal Linial*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Derivation of biological meaning from large sets of proteins or genes is a frequent task in genomic and proteomic studies. Such sets often arise from experimental methods including large-scale gene expression experiments and mass spectrometry (MS) proteomics. Large sets of genes or proteins are also the outcome of computational methods such as BLAST search and homology-based classifications. We have developed the PANDORA web server, which functions as a platform for the advanced biological analysis of sets of genes, proteins, or proteolytic peptides. First, the input set is mapped to a set of corresponding proteins. Then, an analysis of the protein set produces a graph-based hierarchy which highlights intrinsic relations amongst biological subsets, in light of their different annotations from multiple annotation resources. PANDORA integrates a large collection of annotation sources (GO, UniProt Keywords, InterPro, Enzyme, SCOP, CATH, Gene-3D, NCBI taxonomy and more) that comprise ~200 000 different annotation terms associated with ~3.2 million sequences from UniProtKB. Statistical enrichment based on a binomial approximation of the hypergeometric distribution and corrected for multiple hypothesis tests is calculated using several background sets, including major gene-expression DNA-chip platforms. Users can also visualize either standard or user-defined binary and quantitative properties alongside the proteins. PANDORA 4.2 is available at http://www.pandora.cs.huji.ac.il.

Original languageAmerican English
Article numbergkq320
Pages (from-to)W84-W89
JournalNucleic Acids Research
Volume38
Issue numberSUPPL. 2
DOIs
StatePublished - 5 May 2010

Bibliographical note

Funding Information:
Prospects consortium (EU framework VII) and the BSF (grant number 2007219); Sudarsky Center for Computational Biology (to N.R., M.F. and R.S.). Funding for open access charge: Prospects consortium (EU framework VII) and the BSF (grant number 2007219).

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