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Purdue-designed Web server wins competition
In
the age of genome projects and high-throughput proteomics experiments,
biologists are turning more and more to bioinformatics to help organize
data and guide experiments. Function prediction of identified but yet
uncharacterized proteins is a key element in this relationship, providing
what some refer to as “computationally-assisted hypothesis generation.”
A special interest group meeting on Automated Function prediction at the
recent International Conference on Intelligent Systems for Molecular Biology
(Detroit, MI, June 24-29) hosted an assessment of web servers that annotate
a query protein sequence or structure. The format of the assessment closely
resembled that of the widely-known CASP (Critical Assessment of Techniques
for Protein Structure Prediction), but was the first of its type.
Participants were required to provide a fully automatic server to which the organizer submitted sequence and structural information for a series of proteins for which functions were known but not yet published. The accuracy of each prediction was assessed by calculating the degree of specificity the predicted annotation shared with the known function annotation in the Gene Ontologies, a categorized, hierarchical vocabulary of biological function terms.
PFP, a web server designed and maintained by Biological Sciences and Computer Science Professor Daisuke Kihara’s lab, fared the best in the scored competition, beating notable entries from David Eisenberg’s lab at UCLA, Andrej Sali’s lab at UCSF, Adam Godzik’s lab at the Burnham Institute, and the European Bioinformatics Institute at EMBL.
PFP uses PSI-BLAST, the traditional tool used by biologists for inference of homology, and FAMs (Function Association Matrices), a novel functional data mining tool, to produce a unique score for each possible annotation, providing both accuracy and strong coverage of functional space. In addition to participating in the assessment, Kihara and Biological Sciences graduate student Troy Hawkins gave an invited talk, “The use of context-based functional association in automated protein function prediction methods” and presented two posters describing their work.
The description of the competition can be found at http://ffas.burnham.org/AFP.
The PFP server is online at http://dragon.bio.purdue.edu/pfp.
Archives: Alumni Profiles | Class Notes