In an early, online Proceedings of the National Academy of Sciences paper, a team from the UK and Spain describes transcriptional, translational, and post-translational modifications contributing to snake venom variability. By bringing together information on snake venom proteomes, snake genomes, and transcriptomes for six poisonous snake species, the researchers found that gene sequences alone are not the only venom composition determinants. Rather, other levels of regulation apparently play into this process, affecting different venom gene paralogs in different ways. The study's authors conclude that between-species venom variation results from "complex interaction between a variety of genetic and post-genomic factors acting on toxin genes."
Stone Age farming populations reached Europe from the Near East via a so-called maritime route that involved island hopping along the Southern European coast, according to a study by researchers from the US, Greece, Italy, and Serbia. The team considered genome-wide SNP data from individuals representing almost three dozen modern day populations from North Africa, Southern Europe, Northern Europe, mainland Greece, Central Anatolia, Cappadocia, Crete, and Dodecanese. Based on the polymorphism clustering and gene flow patterns they detected, the investigators argue that "Near Eastern migrants reached Europe from Anatolia. A maritime route and island hopping was mainly used by these Near Eastern migrants to reach Southern Europe."
An Argonne National Laboratory-led team presents a plant resource called PlantSEED that uses comparative genomics to iteratively annotate genomes and interpret metabolic features based on genomic data, protein family patterns, biochemical pathway information, and sub-system analyses. In proof-of-principle analyses, for example, the researchers used PlantSEED to annotate 10 plant reference genomes, filling in annotation gaps and developing metabolic models for the genomes. "PlantSEED was developed to streamline the process of annotating plant genome sequences, to construct metabolic models based on genome annotations automatically, and to use models to test the annotation of these sequences, allowing the detection of gaps and errors in gene annotations and the prediction of new functions," authors of the study say.