Towards a genome-wide transcriptogram: the Saccharomyces cerevisiae case.

ABSTRACT: Analysis of genome-wide expression data poses a challenge to extract relevant information. The usual approaches compare cellular expression levels relative to a pre-established control and genes are clustered based on the correlation of their expression levels. This implies that cluster definitions are dependent on the cellular metabolic state, eventually varying from one experiment to another. We present here a computational method that order genes on a line and clusters genes by the probability that their products interact. Protein-protein association information can be obtained from large data bases as STRING. The genome organization obtained this way is independent from specific experiments, and defines functional modules that are associated with gene ontology terms. The starting point

SUBMITTER: Rybarczyk-Filho JL 

PROVIDER: S-EPMC3082889 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature