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Next-generation sequencing projects have underappreciated information management tasks requiring detailed attention to specimen curation, nucleic acid sample preparation and sequence production methods required for downstream data processing, comparison, interpretation, sharing and reuse. The few existing metadata management tools for genome-based studies provide weak curatorial frameworks for experimentalists to store and manage idiosyncratic, project-specific information, typically offering no automation supporting unified naming and numbering conventions for sequencing production environments that routinely deal with hundreds, if not thousands of samples at a time. Moreover, existing tools are not readily interfaced with bioinformatics executables, (e.g., BLAST, Bowtie2, custom pipelines). Our application, the Omics Metadata Management Software (OMMS), answers both needs, empowering experimentalists to generate intuitive, consistent metadata, and perform analyses and information management tasks via an intuitive web-based interface. Several use cases with short-read sequence datasets are provided to validate installation and integrated function, and suggest possible methodological road maps for prospective users. Provided examples highlight possible OMMS workflows for metadata curation, multistep analyses, and results management and downloading. The OMMS can be implemented as a stand alone-package for individual laboratories, or can be configured for webbased deployment supporting geographically-dispersed projects. The OMMS was developed using an open-source software base, is flexible, extensible and easily installed and executed. The OMMS can be obtained at http://omms.sandia.gov.AvailabilityThe OMMS can be obtained at http://omms.sandia.gov

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Brain connections formed during the nurturing period of an infant's development are fundamental for survival. In this paper, elementary brain (neural interconnection pattern) evolution is simulated for various individuals in two similar artificial species. The simulation yields information about the learning, performance and brain structure of the population over time. Concepts from Categorical Neural Semantic Theory (CNST) are used to analyze the development of neural structure as evolution progresses. FlatWorld, a virtual two dimensional environment, is used to test survival skills of simple embodied neural agents. A combination of Genetic Algorithms (GA) and Neural Networks (NN) is applied within FlatWorld to study the relationship between the nurturing of the infant individuals during their developmental period with their subsequent behavior in the environment and the evolution of the associated brain structures. The results show evidence that during evolution, learning performance increases when brain structures required from CNST are formed, and that survival skills increase over evolutionary time-scales due to the formation of these structures.