Relating gene expression data on two-component systems to functional annotations in Escherichia coli.
BACKGROUND
Obtaining physiological insights from microarray experiments requires computational techniques that relate gene expression data to functional information. Traditionally, this has been done in two consecutive steps. The first step identifies important genes through clustering or statistical techniques, while the second step assigns biological functions to the identified groups. Recently, techniques have been developed that identify such relationships in a single step.


RESULTS
We have developed an algorithm that relates patterns of gene expression in a set of microarray experiments to functional groups in one step. Our only assumption is that patterns co-occur frequently. The effectiveness of the algorithm is demonstrated as part of a study of regulation by two-component systems in Escherichia coli. The significance of the relationships between expression data and functional annotations is evaluated based on density histograms that are constructed using product similarity among expression vectors. We present a biological analysis of three of the resulting functional groups of proteins, develop hypotheses for further biological studies, and test one of these hypotheses experimentally. A comparison with other algorithms and a different data set is presented.


CONCLUSIONS
Our new algorithm is able to find interesting and biologically meaningful relationships, not found by other algorithms, in previously analyzed data sets. Scaling of the algorithm to large data sets can be achieved based on a theoretical model.
Denton
Anne M
AM
Department of Computer Science and Operations Research, North Dakota State University, Fargo, ND 58105, USA. anne.denton@ndsu.edu
Wu
Jianfei
J
Townsend
Megan K
MK
Sule
Preeti
P
Prüss
Birgit M
BM
D000220:Adaptation, Biological / Q000235:genetics; D000465:Algorithms; D001185:Artificial Intelligence; D016009:Chi-Square Distribution; D030541:Databases, Genetic; D004926:Escherichia coli / Q000235:genetics* / Q000378:metabolism*; D015870:Gene Expression / Q000502:physiology; D020869:Gene Expression Profiling / Q000379:methods*; D015964:Gene Expression Regulation, Bacterial; D016571:Neural Networks, Computer; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods / Q000706:statistics & numerical data; D010363:Pattern Recognition, Automated; D011494:Protein Kinases / Q000235:genetics / Q000378:metabolism; D015398:Signal Transduction; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism
D014157:Transcription Factors; D011494:Protein Kinases