This week in PLoS Neglected Tropical Diseases, an international team led by investigators at the Wellcome Trust Sanger Institute report the genome and transcriptome of the human blood fluke Schistosoma mansoni. The team's data now appear in a searchable format in both the GeneDM and SchistroDB databases. "With further transcriptional profiling and genome sequencing increasingly accessible, the upgraded genome will form a fundamental dataset to underpin further advances in schistosome research," the authors write.
Over in PLoS One, researchers in Germany report their sequencing of 1,002 Trigonopterus weevils assigned to 270 morphospecies from seven sites across New Guinea. The researchers found that "success rates of DNA barcoding methods were lowest when species showed a pronounced geographical structure," and that those rates "might drop when closer localities are included."
A team led by investigators at Stanford University reports in PLoS Genetics this week an array-based genomic analysis to support "'back-to-Africa' gene flow from more than 12,000 years ago." When estimating times of migration from sub-Saharan populations into North Africa, the team assigned local ancestry to haplotypes, "using a novel, principal component-based analysis of three ancestral populations." With these haplotypes, the team estimated a "migration of western African origin into Morocco began about 40 generations ago," and that a "migration of individuals with Nilotic ancestry into Egypt occurred about 25 generations ago."
Elsewhere in the same journal, researchers at the University of Michigan and their colleagues identify 1,582 Arabidopsis genes differentially expressed in the root-hair or non-hair cell types, including 208 core root epidermal genes. "The organization of the core genes into a network was accomplished by using 17 distinct root epidermis mutants and 2 hormone treatments to perturb the system and assess the effects on each gene's transcript accumulation," the authors write. "Temporal gene expression information from a developmental time series dataset and predicted gene associations derived from a Bayesian modeling approach were used to aid the positioning of genes within the network."