Marks ME, Castro-Rojas CM, Teiling C, et al. (2010). The genetic basis of laboratory adaptation in Caulocabacter crescentus. Journal of Bacteriology. E-pub.
Researchers at the University of Chicago and their colleagues report a comprehensive set of polymorphisms that underlie multiple derived phenotypes of Caulobacter crescentus — a dimorphic aquatic bacterium that has been cultured in labs for more than 50 years. Using whole-genome sequencing and classic gene expression analysis, the team found evidence of parallel adaptive evolution in lab strains of C. crescentus. They write that their study shows that bacterial evolution, as driven by selection for survival in distinctive environments, is attained through multiple genetic mechanisms, including non-synonymous mutations, non-coding regulatory changes, the acquisition of new sequences, and inactivation of existing genes.
van den Berg BHJ, McCarthy FM, et al. (2010). Re-annotation is an essential step in systems biology modeling of functional genomics data. PLoS One. 5(5): e10642.
Investigators in Mississippi and Iowa suggest that "re-annotation should be considered to be an essential first step for deriving value from functional genomics data" because the process can uncover unique knowledge from previous research findings. In any systems biology modeling of functional genomics data, the authors say, structural and functional re-annotation is critical — particularly for species with recently sequenced genomes. As an example, the team quantified the impact of its structural and functional re-annotation of a microarray that was developed as a tool for disease research. The scientists found that re-annotation improved both the quality and quantity of annotations and allowed for "a more comprehensive Gene Ontology-based" model, they write.
Gibson DG, Glass JI, Lartigue C, et al. (2010). Creation of a bacterial cell controlled by a chemically synthesized genome. Science. E-pub.
Daniel Gibson and his colleagues at the J. Craig Venter Institute report their assembly of the synthetic 1.08Mbp Mycoplasma mycoides JCVI-syn1.0 genome and its transplantation into Mycoplasma capricolum recipient cells — which are, in turn, driven by the synthetic DNA sequence and capable of continuous self-replication. "The only DNA in the cells is the designed synthetic DNA sequence, including 'watermark' sequences and other designed gene deletions and polymorphisms, and mutations acquired during the building process," the authors write.
Converting to the Cloud
Stein LD. (2010). The case for cloud computing in genome informatics. Genome Biology. 11: 207.
In this review, Lincoln Stein at the Ontario Institute for Cancer Research in Toronto suggests that "with DNA sequencing now getting cheaper more quickly than data storage or computation, the time may have come for genome informatics to migrate to the cloud." Stein discusses the economics and practicalities of cloud computing for genomic data and notes that the largest challenge for moving to the cloud could be network bandwidth. "If cloud computing is to work for genomics, the service providers will have to offer some flexibility in how large data sets get into the system," the author writes.