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In most bacteria, genes are organized into multi-gene operons. I argue that operon evolution is driven by selection on gene expression patterns.
Read the paper in PLoS Genetics
In an earlier paper, I argued that these operons form because they facilitate co-regulation and not because of horizontal gene transfer.
Read the paper in Genome Research
In most bacteria, genes also tend to be on the leading strand. Because operons are particularly biased to the leading strand, I argue that this reflects interruptions in gene expression.
Read the paper in Nucleic Acids Research
Operon predictions are available for hundreds of sequenced bacterial and archaeal genomes. The predictions rely primarily on conservation and inter-gene spacing. Tests in several species suggest that the predictions are 82-87% accurate in most genomes.
Read the paper in Nucleic Acids Research
OpWise uses operons to help determine the reliability of bacterial expression data from microarrays. In my experience, commonly used statistical methods can dramatically overstate the reliability of microarray data.
Read the paper in BMC Bioinformatics
MicrobesOnline.org is a website for comparing bacterial genomes. You can search for protein families or homologs, compare gene context across species, view predicted operons, and load genes into a "shopping cart" to build your own sequence alignments and phylogenetic trees.
Read the paper in Genome Research
LNP identifies coexpressed genes in expressed sequence tag (EST) data in a statistically sound manner.
Read the paper in Bioinformatics
The turnover of macromolecules involves tradeoffs between adapting to changing environments and maximizing growth rates.