In the PNAS Early Edition, researchers at Princeton University demonstrate that the biophysical mechanisms of transcriptional regulatory sequences can be deciphered "by a simple experiment in which a library of partially mutated regulatory sequences are partitioned according to their in vivo transcriptional activities and then sequenced en masse." Consequent computational analysis of the sequence data "can provide precise quantitative information about how the regulatory proteins at a specific arrangement of binding sites work together to regulate transcription," the authors write, adding that their method, when applied to the E. coli lac promoter, was useful in identifying regulatory protein binding sites de novo.
A pair of researchers from Trinity College, Dublin, report that whole-genome duplicated genes, or ohnologs, in the human genome "are dosage balanced and frequently associated with disease." They show that ohnologs "have rarely experienced subsequent small-scale duplication and are also refractory to copy number variation in human populations and are thus likely to be sensitive to relative quantities," and that they maintain a strong association with human disease. "In particular, Down Syndrome caused by trisomy 21 is widely assumed to be caused by dosage effects, and 75 percent of previously reported candidate genes for this syndrome are ohnologs that experienced no other copy number changes," the team writes.
A collaborative duo reports their "Bayesian modeling approach to generate the posterior probabilities of species assignments taking account of uncertainties due to unknown gene trees and the ancestral coalescent process." To test the statistical performance of their method, the team analyzed sequence data from rotifers, fence lizards, and human populations.
Researchers at Yale University compare genomes to computer operating systems in PNAS Early Edition research article this week. Namely, they apply their "firsthand knowledge of the architecture of software systems to understand cellular design principles," by comparing the transcriptional regulatory network of E. coli to the Linux OS call graph. While "both networks have a fundamentally hierarchical layout," the team writes, the E. coli transcriptional network has a "few global regulators at the top and many targets at the bottom," while the Linux "call graph has many regulators controlling a small set of generic functions."