Predicting gene function through systematic analysis and quality assessment of high-throughput data.
BACKGROUND
Determining gene function is an important challenge arising from the availability of whole genome sequences. Until recently, approaches based on sequence homology were the only high-throughput method for predicting gene function. Use of high-throughput generated experimental data sets for determining gene function has been limited for several reasons.


RESULTS
Here a new approach is presented for integration of high-throughput data sets, leading to prediction of function based on relationships supported by multiple types and sources of data. This is achieved with a database containing 125 different high-throughput data sets describing phenotypes, cellular localizations, protein interactions and mRNA expression levels from Saccharomyces cerevisiae, using a bit-vector representation and information content-based ranking. The approach takes characteristic and qualitative differences between the data sets into account, is highly flexible, efficient and scalable. Database queries result in predictions for 543 uncharacterized genes, based on multiple functional relationships each supported by at least three types of experimental data. Some of these are experimentally verified, further demonstrating their reliability. The results also generate insights into the relative merits of different data types and provide a coherent framework for functional genomic datamining.


BACKGROUND
Free availability over the Internet.


BACKGROUND
f.c.p.holstege@med.uu.nl


BACKGROUND
http://www.genomics.med.uu.nl/pub/pk/comb_gen_network.
Kemmeren
Patrick
P
Department of Physiological Chemistry, University Medical Center Utrecht, PO Box 85060, 3508 AB Utrecht, The Netherlands.
Kockelkorn
Thessa T J P
TT
Bijma
Theo
T
Donders
Rogier
R
Holstege
Frank C P
FC
D003198:Computer Simulation; D003628:Database Management Systems; D030562:Databases, Protein; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D016247:Information Storage and Retrieval / Q000379:methods*; D008957:Models, Genetic; D010506:Periodicals as Topic; D011786:Quality Control; D012441:Saccharomyces cerevisiae / Q000502:physiology*; D029701:Saccharomyces cerevisiae Proteins / Q000502:physiology*; D015398:Signal Transduction / Q000502:physiology*; D013329:Structure-Activity Relationship
D029701:Saccharomyces cerevisiae Proteins