The Samba algorithm is available here both as a stand alone executable or as part of the Expander software suite.
SAMBARevealing
Modularity and Organization in the Yeast Molecular Network by Integrated
Analysis of Highly Heterogeneous Genome-wide DataAmos Tanay, Roded Sharan, Martin Kupiec and Ron Shamir Proc. National Academy of Science USA 101 (9) 2981-2986 (March 2004). pdf |
| The dissection of complex biological
systems is a challenging task, made difficult by the size of the underlying
molecularnetwork and by the heterogeneous nature of the control mechanisms
involved. Novel high throughput techniques aregenerating massive datasets
on various aspects of such systems. Here we perform the first analysis of
a highly diverse collection of genome-wide datasets, including gene expression,
protein interactions, growth phenotype data and transcription factor binding,
in order to reveal the modular organization of the yeast system. By integrating
experimental data of heterogeneous sources and types, we are able to perform
analysis on a much broader scope than previous studies. At the core of our
methodology is the ability to identify modules, namely, groups of genes with
statistically significant correlated behavior across diverse data sources.
Numerous biological processes are revealed through these modules, which also
obey global hierarchical organization. We use the identified modules to study
the yeast transcriptional network and also to predict the function of over
800 uncharacterized genes. Our analysis framework, entitled SAMBA, enables
the processing of current as well as future sources of biological information,
and is readily extendable to novel experimental techniques and higher organisms.
The Samba algorithm is available here both as a stand alone executable or as part of the Expander software suite. |
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for comments or questions: Amos Tanay (amos@post.tau.ac.il)