A couple of worm genomics papers caught my eye this week.
One is about using networks of genes as biomarkers. (The first author is our own turritopsis, and we extend our heartiest congratulations on the publication of this interesting paper.) It’s a neat idea: networks make better biomarkers than single genes; furthermore, thinking about genes as elements of networks allows us to make inferences about the functions of previously unknown genes.
The other paper describes the use of next-generation or “deep” sequencing to characterize the transcriptome of aging in C. elegans. The paper demonstrates that applying the newest sequencing methodologies to gene expression analysis isn’t just faster and more expensive, it’s qualitatively different: it can detect un-annotated genes, antisense products, and isoform switching in a way that array-based technologies can’t.
In a sense, next-gen sequencing it’s extending the idea of “unbiased” gene expression analysis. When arrays first became available, we were all excited about the fact that we no longer had to decide in advance which genes to look at — we could just look at all of them simultaneously, which led to a qualitatively different way of looking at the genome (and, ultimately, of doing science). The new technologies push away another kind of bias: our prior assumptions about what is (or “ought to be”) transcribed within a cell.
Fortney, K., Kotlyar, M., & Jurisica, I. (2010). Inferring the functions of longevity genes with modular subnetwork biomarkers of Caenorhabditis elegans aging Genome Biology, 11 (2) DOI: 10.1186/gb-2010-11-2-r13
Ruzanov, P., & Riddle, D. (2010). Deep SAGE analysis of the Caenorhabditis elegans transcriptome Nucleic Acids Research DOI: 10.1093/nar/gkq035