"Genomic and post-genomic studies are transforming our mechanistic understanding of organism-environment interactions." While this statement is certainly true, it masks many of the major challenges that have had to be overcome during the last decade. Today, genomics approaches are widely used by researchers from across the breadth of NERC science, utilising established (and ever cheaper) technologies and analysis pipelines, and delivering high impact publications. The same cannot yet be said for metabolomics, which is a considerably less mature approach, both analytically and computationally. The analytical challenges in metabolomics have restricted its use to experts of analytical chemistry, while the computational challenges have restricted the knowledge that can be mined from these rich datasets. Here we address the latter point, drawing from the wisdom and experience of genomics researchers. One of the reasons for the success of environmental genomics is that biologists, without an in-depth knowledge of biostatistics and programming, have been able to construct and execute Next Gen Sequencing (NGS) data analyses using standardised workflows. Galaxy (http://galaxyproject.org/) - headlined as "Online bioinformatics analysis for everyone" - has emerged as the leading open-source workflow platform for NGS data analysis, with many standard processing tools accessible from its Web-based user interface. This workflow software is also being applied successfully to proteomics and chemo-informatics. Researchers at BGI (Beijing Genomics Institute) in China, our Project Partner on this application, have considerable expertise in Galaxy, since this web-based data analysis and workflow system forms the basis of its data analysis platform. They also have close links with the Galaxy development team. We propose to 'hop' Dr Davidson from Professor Viant's environmental metabolomics laboratory and NBAF-B at the University of Birmingham into a computational laboratory at BGI-Hong Kong. Here he will gain specialist expertise in Galaxy workflows and implement our existing metabolomics pipelines into Galaxy. This is an extremely important step towards making metabolomics analysis pipelines more effective (by integrating powerful algorithms from the ever growing toolbox of metabolomics analysis methods), more standardised (enabling greater cross comparison of results from different studies), and considerably more accessible to biologists. Our aim is for both data and analysis tools to be accessible from a software platform that provides a single, user-friendly interface for developing computational pipelines in a form that can be shared and reused by the environmental community. Ultimately this will facilitate the integration of genomic and metabolomic datasets, enabling novel studies of the mechanisms underpinning stress responses of organisms within our environment. Here we will focus on the analysis of multi-omics datasets of Daphnia spp., to further investigate the molecular responses to environmental toxicants. Our international team of investigators provides a unique combination of expertise spanning environmental metabolomics (Viant, Davidson), environmental genomics (Colbourne, Zhou) and computational workflows (Li), and are all strongly tied by a common interest and track record in the handling, analyses and interpretation of large-scale 'omics datasets. While Colbourne, Davidson and Viant are based in the School of Biosciences, University of Birmingham, and Li and Zhou reside at BGI in China, all investigators are part of the newly launched Joint BGI-Birmingham Environment and Health Centre at Birmingham that will provide a world-class academic, research and training environment for the integration of state-of-the-art sequencing, metabolomic and bioinformatics technologies.

Rice can be considered the most important worldwide crop for human nutrition, currently providing ~20% of worldwide daily dietary energy. Due to a growing worldwide population, and the effects of climate change, research into improvements in rice yield, resilience to drought and resistance to pathogens is urgently needed. Such research must be underpinned by public databases storing high-quality information about the rice genome, genetic variants carried by varieties with desirable traits, and information about the function of each gene/protein. This project is a collaboration between research teams based at the University of Liverpool, the Beijing Institute of Genomics and the BGI Education Centre. Our teams have considerable track record in the development of methods for studying the abundance of genes (transcriptomics) and proteins (proteomics) on a large scale for rice and other species, as well as computational approaches for interpreting and integrating data from these different techniques. At present, the public databases storing the rice genome and information known about gene/protein function are disconnected from experimental data (transcriptomics/proteomics) being collected in laboratories all over the world. These experimental data can be used directly to improve the annotation of the genome, by showing how strongly particular genes or proteins are expressed under particular growth conditions or for a given rice variety (which gives clues as to functional importance). These data also show how genes or proteins differ in a given variety from the "reference" genome contained in the database. Our groups are developing software tools for integrating and analysing these data in new ways, so that when laboratories submit their data to a public repository, it can be directly integrated and viewed alongside the genome - which at present is not possible. We are also going to generate and analyse new data sets for several important rice varieties, so we can study how these gene and protein sequences differ from the reference genome. Our results will help to improve the sequences and annotation of rice genes and proteins, and will be made easily available to all other rice researchers through the most widely accessed international public databases.

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This proposal is to prove the concept and develop a high throughput methodology for screening virus infections and immunities in wild plant and insect communities. We propose to obtain small RNA profiles of the plant and insect communities from the Wytham Wood, Oxfordshire, by using Solexa high throughput sequencing. The anti-virus small interfering (avsi)RNAs that are produced by the host gene silencing systems against the virus RNAs will be screened for viral origins. We anticipate the detection of the avsiRNAs against the known prevalent viruses at the site and will use these viruses as positive controls to optimize conditions of sample preparation, sequencing, and bioinformatics. We also expect to discover the prevalence of previously unconfirmed and unknown viruses, and we plan to validate these newly detected infections by using the conventional methods (e.g., RTPCR, cloning and sequencing, Northern Blotting, etc.) to determine the sensitivity and accuracy of the high throughput methodology. To enable the conventional method assessment, we plan to label samples for each sampled species by using sequence tags. The sampling regime is designed for achieving a sensitivity of shotgun detection of 5% infection rate for plant populations. The tagged samples will be pooled together for high throughput sequencing runs to achieve cost effectiveness. The resulting sequences will be sorted back to their original sample identities and analyzed. Results will be validated by using the conventional methods with the sorted specific samples. Mass post-sequencing analyses will also be performed without sorting the samples to their original identities. Results from the specific analyses and mass analyses will be compared. The mass analyses without the requirement of sorting samples are designed for testing a capacity of genetic random sampling from an ecosystem without restriction of sampling regimes. The technology will offer a broad range of applications from large scale random sampling in natural conditions during the environment change, to defined survey in agricultural and the other managed conditions.