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This Week in Genome Biology: Feb 13, 2013

An international team led by investigators at University College Dublin describes efforts to sequence and start assessing the genome of a free-living, bacteria-consuming soil amoeba species called Acanthamoeba castellanii. Among the almost 15,500 predicted protein-coding genes in the A. castellanii genome, the researchers uncovered hundreds of genes believed to have made their way into the amoeba via a process known as inter-kingdom lateral gene transfer. And their RNA-sequencing experiments indicated that most of these do get expressed under one condition or another. "[Lateral gene transfer] has shaped both the genome and transcriptome of [A. castellanii]," the study authors say, "and our analysis of [lateral gene transfer] across a number of amoeboid genomes reveals unexpected similarities between phylogenetically distinct amoebae."

Members of the Sesame Genome Working Group discuss the status of ongoing genomic studies on sesame, Sesamum indicum, in an open letter appearing in Genome Biology. Six Chinese teams are currently participating in genetic, functional genomic, and other studies of the oilseed-producing plant for this effort, authors of the paper explain. In addition to a draft sesame genome assembly already released on the Sesame Genome Project website, the group plans to put together an S. indicum fine map that can be used for more detailed genetic, genomic, and functional studies on sesame. "We believe that the main achievement of this project will be to markedly accelerate sesame genetic research and breeding," the team notes. "Members of the SGWG also hope to address additional questions about the relationship between growth and environmental conditions, such as identifying which genes regulate low temperature responses and drought sensitivity."

Finally, European Bioinformatics Institute researchers Jong Kyoung Kim and John Marioni report on a new statistical framework for discerning stochastic gene expression kinetics using RNA sequencing data on individual cells. This framework is "motivated by a kinetic model for transcriptional bursting to model the biological variability present in single-cell RNA-seq data," Kim and Marioni say. In mouse embryonic stem cells, for instance, the investigators demonstrated that they could use this so-called "Poisson-Beta" modeling method to gain new insights into the contributions that histone modifications make to transcriptional kinetics.