The re-use of previously validated designs is critical to the evolution of synthetic biology from a research discipline to an engineering practice. Here we describe the Synthetic Biology Open Language (SBOL), a proposed data standard for exchanging designs within the synthetic biology community. SBOL represents synthetic biology designs in a communitydriven, formalized format for exchange between software tools, research groups and commercial service providers. The SBOL Developers Group has implemented SBOL as an XML/RDF serialization and provides software libraries and specification documentation to help developers implement SBOL in their own software. We describe early successes, including a demonstration of the utility of SBOL for information exchange between several different software tools and repositories from both academic and industrial partners. As a community-driven standard, SBOL will be updated as synthetic biology evolves to provide specific capabilities for different aspects of the synthetic biology workflow.Synthetic biology treats biological organisms as a new technological medium with a unique set of characteristics, such as the ability to self-repair, evolve and replicate. These characteristics create their own engineering challenges, but offer a rich and largely untapped source of potential applications across a broad range of sectors 1,2 . Applications such as biomolecular computing 3 , metabolic engineering 4 , or reconstruction and exploration of natural cell biology 5,6 commonly require the design of new genetically encoded systems. As engineers, synthetic biologists most often base their designs on previously described 'DNA segments' (see Supplementary Table 1 for definitions of selected terms) to meet their design requirements. Reuse of the DNA sequence for these segments involves their exchange between laboratories and their hierarchical composition to form devices and systems with higher level function.Every engineering field relies on a set of 'standards' 7 that practitioners follow to enable the exchange and reuse of designs for 'systems' , 'devices' and 'components' . Similarly, the representation of synthetic biology designs using computer-readable 'data standards' has the potential to facilitate the forward engineering of novel biological systems from previously characterized devices and components. For example, such standards could enable synthetic biology companies to offer catalogs of devices and components by means of computerreadable data sheets, just as modern semiconductor companies do for electronics. Such standards could also enable a synthetic biologist to develop portions of a design using one software tool, refine the design using another tool, and finally transmit it electronically to a colleague or commercial fabrication company.In order for synthetic biology designs to scale up in complexity, researchers will need to make greater use of specialized design tools and parts repositories. Seamless inter-tool communication would, for example, allow the separation of gene...

It is believed that 3.2 billion bp of the human genome harbor ∼35,000 protein-coding genes. On average, one could expect one gene per 300,000 nucleotides (nt). Although the distribution of the genes in the human genome is not random,it is rather surprising that a large number of genes overlap in the mammalian genomes. Thousands of overlapping genes were recently identified in the human and mouse genomes. However,the origin and evolution of overlapping genes are still unknown. We identified 1316 pairs of overlapping genes in humans and mice and studied their evolutionary patterns. It appears that these genes do not demonstrate greater than usual conservation. Studies of the gene structure and overlap pattern showed that only a small fraction of analyzed genes preserved exactly the same pattern in both organisms

Autism Spectrum Disorders (ASD) is a spectrum of highly heritable neurodevelopmental disorders in which known mutations contribute to disease risk in 20% of cases. Here, we report the results of the largest blood transcriptome study to date that aims to identify differences in 170 ASD cases and 115 age/sex-matched controls and to evaluate the utility of gene expression profiling as a tool to aid in the diagnosis of ASD. The differentially expressed genes were enriched for the neurotrophin signaling, long-term potentiation/depression, and notch signaling pathways. We developed a 55-gene prediction model, using a cross-validation strategy, on a sample cohort of 66 male ASD cases and 33 age-matched male controls (P1). Subsequently, 104 ASD cases and 82 controls were recruited and used as a validation set (P2). This 55-gene expression signature achieved 68% classification accuracy with the validation cohort (area under the receiver operating characteristic curve (AUC): 0.70 [95% confidence interval [CI]: 0.62–0.77]). Not surprisingly, our prediction model that was built and trained with male samples performed well for males (AUC 0.73, 95% CI 0.65–0.82), but not for female samples (AUC 0.51, 95% CI 0.36–0.67). The 55-gene signature also performed robustly when the prediction model was trained with P2 male samples to classify P1 samples (AUC 0.69, 95% CI 0.58–0.80). Our result suggests that the use of blood expression profiling for ASD detection may be feasible. Further study is required to determine the age at which such a test should be deployed, and what genetic characteristics of ASD can be identified.