Similarities and Differences in Genome-Wide Expression Data of Six Organisms
By Sven Bergmann, Jan Ihmels, Naama Barkai. PLoS Biology, 2003
392 citations recorded by the source corpus.
Comparing genomic properties of different organisms is of fundamental importance in the study of biological and evolutionary principles. Although differences among organisms are often attributed to differential gene expression, genome-wide comparative analysis thus far has been based primarily on genomic sequence information. We present a comparative study of large datasets of expression profiles from six evolutionarily distant organisms: S. cerevisiae, C. elegans, E. coli, A. thaliana, D. melanogaster, and H. sapiens. We use genomic sequence information to connect these data and compare global and modular properties of the transcription programs. Linking genes whose expression profiles are similar, we find that for all organisms the connectivity distribution follows a power-law, highly connected genes tend to be essential and conserved, and the expression program is highly modular. We reveal the modular structure by decomposing each set of expression data into coexpressed modules. Functionally related sets of genes are frequently coexpressed in multiple organisms. Yet their relative importance to the transcription program and their regulatory relationships vary among organisms. Our results demonstrate the potential of combining sequence and expression data for improving functional gene annotation and expanding our understanding of how gene expression and diversity evolved.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/07c93824cea949adbeab3be9c194ecc75ebcdde3
How researchers see this paper
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In Molecular Biology
- Steve Horvath
- Aviv Regev
- Nir Friedman
- Sven Bergmann
- Naama Barkai
- Jan Ihmels
- Eran Segal
- Daphne Koller
Citations in the corpus
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- Defining transcription modules using large-scale gene expression data (2004, methodology): “Since our approach can identify overlapping clusters, it is well-suited for the analysis of data in higher eukaryotes (Bergmann et al., 2004), where combinatorial regulation is likely to play a more prominent role.”
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