Eric Harold Lyons - US grants

iPlant is a new kind of virtual organization, a cyberinfrastructure (CI) collaborative created to catalyze progress in computationally-based discovery in plant biology. iPlant has created a comprehensive and widely used CI, driven by community needs, and adopted by a number of large-scale informatics projects and thousands of individual users. The project has laid a strong foundation to build an increasingly more capable CI and is poised to have an even greater impact on the plant sciences and a number of related fields, with a new focus on addressing computational bottlenecks for a broad number of life science researchers.

In the next five years, iPlant will continue to enhance the capabilities of a comprehensive CI and will also expand the scope to cover a number of new fields of inquiry. In iPlant's initial phase, Grand Challenge projects were defined to shape community requirements for the design of the CI. The two projects, Genotype-to-Phenotype and the Tree of Life, led to new analytical tools and competences for genomics and evolutionary biology. Future work will advance these capabilities and expand into capture and modeling of phenotypic, environmental, and ecological data. As before, this growth will be motivated by community needs and accomplished by community collaboration.

iPlant will continue to actively partner with other large CI development efforts and will coordinate CI development where feasible, appropriate, and mutually beneficial. iPlant will continue to be the underlying infrastructure provider for a number of projects that provide a variety of bioinformatics services. While continuing to support plant biology discovery research, iPlant will expand scope beyond the plant sciences, in coordination with nascent animal-centered efforts. The project will continue to adapt to the rapidly changing needs of the life sciences community and the rapidly changing technological landscape faced by researchers.

The intellectual merit of the project is in advancing the state of modern biology. Without question, research progress in the plant sciences, and in life sciences more generally, is increasingly limited by data and computational challenges. As knowledge of plant biology increases, the field will progress from informatics-based discovery to predictive modeling and eventually to synthetic biology. A comprehensive CI that eliminates the bottlenecks of data management, data standards, file formats, analysis, efficient collaboration, and knowledge dissemination will be a necessary underlying enabler to achieve this vision, and iPlant is positioned to be this enabling infrastructure.

The broader impacts of the project are numerous. The CI currently supports thousands of end users through its data storage, cloud, and online analytical capabilities. As a service provider, iPlant underlies a number of other online biological information resources. The project will continue its wide-ranging and successful education and outreach efforts, and will teach computational skills to learners at all levels, with particular focus on faculty to enable a sustained culture change that incorporates these advanced skills into the teaching of biology.

Rapid global changes are placing unprecedented pressure on plants in agricultural and natural landscapes. This project explores a poorly understood area of plant biology: how genome and biological complexity are related and how that relationship can be manipulated to design responses such as increased tolerance to abiotic and biotic stresses for agricultural applications. The future promises rich synthetic biology applications, if the next generation of scientists is appropriately trained. To address this gap, this project will provide research training in systems biology and predictive modeling for postdoctoral fellows, and graduate and undergraduate students. This team will partner with computer scientists, modeling experts, and journalism students to provide a vision of integrated systems biology for the new century. All research and outreach activities proposed are integrated with recruiting and mentoring students from underrepresented groups. This project will expose trainees and other students through daily dialogues, joint seminars, team-taught courses, and other venues. In addition, this project will develop novel computational and genomic methods that will start to integrate genotype with phenotype, thus transforming comparative biology. Access to all data, computational tools and resources generated in this project will be provided to the broader research community through long-term repositories and through CABBAGE: Community Assets for Brassica Biology And Genome Evolution, a web portal that will be integrated with the cyberinfrastructure developed by the NSF-supported iPlant Collaborative.

Brassica crops have tremendous morphological and chemical diversity and are ideal for studying domestication and other economically important plant processes. The goal of this project is to explore the relationship between polyploidy (the merger and doubling of two genomes) and plasticity using the crop Brassicas. By integrating comparative genomics, networks, and genetic models, this project confronts the key question "Did a whole genome triplication in the crop Brassicas facilitate their domestication and adaptability?". This project leverages the systems biology and -omics resources of Arabidopsis to focus on two hypotheses regarding how whole genome triplication (WGT) affected the Brassicas. The first is whether polyploidy in the crop Brassicas are associated with global alterations to the metabolic and gene expression networks, possibly allowing faster growth through duplication of core, high-flux, enzymes. The second is whether polyploidy and subsequent domestication by human farmers altered the networks related to biotic stress in the crop Brassicas. The specific objectives are to: (1) map post-polyploid duplicated genes using comparative genomics (across species of Brassica and Arabidopsis) and identify post-polyploid changes in gene co-expression and metabolism; (2) search for signatures of selection and recent adaptations to biotic stress during the parallel domestications of Brassica oleracea (broccoli, cabbage, cauliflower, Brussels sprouts, and kohlrabi) and B. rapa (turnips, Chinese cabbage, Pak Choi, and oilseeds); and (3) survey gene expression and glucosinolate levels in F1 hybrids of B. oleracea morphotypes to identify functional elements in genetic response to stress. Analyses of genome organization and gene expression will improve the current understanding of the genetic basis of metabolic innovation. Analyses of transcriptome and metabolic data will identify the types of variation selected during domestication and describe the resulting changes to metabolism, growth, and biotic stress response. Further, this project will describe the evolution of gene expression patterns in Brassica and test genome-scale models of metabolic networks in Brassica, which will be refined with glucosinolate measurements.

PI: David Fernandez-Baca (Iowa State University)

CoPIs: Steven B. Cannon (USDA-ARS/Iowa State University), Christopher D. Town (J. Craig Venter Institute), Andrew D. Farmer (National Center for Genome Resources) and Jeremy DeBarry (University of Arizona).

Senior Personnel: Ethalinda K.S. Cannon (Iowa State University)

Legumes play a central role in food security and nearly every cropping system worldwide. Among cultivated plants, legumes such as beans, peas and lentils are unique in their ability to fix atmospheric nitrogen which is converted into a soluble organic form that can be taken up by plants through symbiosis with a soil bacterium. Approximately 33% of all nutritional nitrogen comes from legumes which are the most important source of protein in most developing countries. Genomic resources have been or are being developed for many legume crops, but the fragmented nature of these data resources limits the ability of researchers to leverage information generated in one species to infer function or identify candidate genes in another species. The overarching goal of this project is to address this deficiency by developing software and methods for a "Legume Federation" (LF) of diverse, independently funded and geographically separated genomic data portals (GDPs). The LF will facilitate utilization of genomic, genetic and phenotypic information across a wide range of legume species that will allow researchers and breeders to take full advantage of the data that are currently available at the GDPs. The project will provide research training opportunities for students from diverse backgrounds at different educational levels. In the context of outreach, the project will pro-actively engage the community of legume data providers through presentations and workshops at national and international meetings as well as through a central web portal that will provide information describing project goals, methods of implementation and progress reports to the community.

The pace of data collection across crop and model plants has increased dramatically. Most crop species have both a data management problem and great opportunities to access genetic "big data". The objectives of this project are to develop a federation model for legume databases, to facilitate data exchange across a wide range of legume species to enable cross-species translational genomics, and to adapt an existing set of open-source tools for biological information management that will provide a framework for project-oriented data management enabling both long-term integration and widespread use. The specific goals are to:

1). Adopt and port the data currently managed in each of the custom frameworks into a set of well-integrated, open source model organism database tools;
2). Define data formats, metadata standards, data exchange and Web service protocols to facilitate communications between species-centric databases at various levels;
3). Utilize orthology, synteny, and mappings of other significant features to integrate genetic, genomic, and phenotypic data across legume species, to enable identification of common molecular bases for important traits and enable traversal across database projects;
4). Improve the capacity of organism database projects to collect and manage complex phenotype data using ontologies, controlled vocabularies and well-defined protocols and schemas; and,
5). Facilitate productive data exchange by implementing a common, open, virtualized Data Repository for data exchange across sites and for stable, long-term archiving of data sets, standardized metadata, and robust methods for archiving, searching, and accessing data sets from federated GDPs.

It is anticipated that the LF will provide a model for a well-accepted open-source technology stack that can be adopted by other research communities that have limited resources or technical expertise. All data will be freely available to the general public through the project web site and through the associated GDPs that include but are not limited to MedicagoGenome (http://medicagogenome.org), SoyBase (http://soybase.org), PeanutBase (http://peanutbase.org), and the Legume Information System (http://legumeinfo.org).

PI: Brian D. Gregory (University of Pennsylvania)

Co-PIs: Eric Lyons and Mark Beilstein (University of Arizona)

Long non-coding RNAs (lncRNAs) are an emerging class of molecules gaining attention for their roles in various biological processes. lncRNAs are defined by the fact that they do not code for proteins and are therefore not mRNAs. In addition, they do not fit into other well-defined small silencing RNA-producing categories such as small interfering RNAs (siRNA) and microRNAs (miRNAs). Despite the importance of lncRNAs in development, epigenetic modification, and stress responses, there is still much to be learned about their structure, protein interactions, and functions, especially in model and crop plant species. This project will address this significant gap using a combination of genomic, evolutionary, and bioinformatics approaches. It is anticipated that the data, web-accessible genome analytical tools, and data management systems developed by the project will provide novel insights into plant gene expression regulation by lncRNAs, and provide important new findings and resources for studies focused on the improvement of numerous crop and genetic model plants. With regard to outreach and training, this project will provide interdisciplinary research training in RNA biology, computational science and evolutionary biology for students and postdoctoral associates. In addition, the project will develop an interdisciplinary course entitled "Applied Concepts in RNA Biology" that will leverage large-scale computing and datasets to understand various aspects of the role of RNA in biological systems. This project-based course will teach the fundamentals of RNA biology, next-generation sequencing techniques, distributed and high performance computing, data-intensive science, and collaborative research techniques that will be used in student-driven research projects. The course will be taught simultaneously at the University of Pennsylvania and the University of Arizona, with two-way audio/video conferencing and lecture topics alternatively taught at each site. All project outcomes will be made readily accessible to the broader research community through a project website (https://genomevolution.org/wiki/index.php/EPIC-CoGe_Tutorial), the iPlant Collaborative and long-term repositories such as GenBank and the Short Read Archive (SRA).

This project is uniquely positioned to provide insights into the structure and function of lncRNAs, and their interaction with specific epigenomic regulatory modifications in the genome. The specific goals of the project are to define a subset of lncRNAs that are important for proper gene regulation in both normal development and stress response. Specifically, the project will focus on identifying and functionally characterizing those lncRNAs that are (1) nuclear, (2) highly structured, (3) stress responsive, (4) protein bound, and (5) evolutionary conserved in genetic models (Eutrema salsugineum and Arabidopsis thaliana) and in crop species (Camelina sativa, Brassica rapa, Zea mays, and Sorghum bicolor), focusing on their roles in stress adaptation. Finally, the project will expand EPIC-CoGe, a central repository for plant epigenomics data across all species, with advanced data integration, visualization, and analysis tools to allow for the integration of functional genomics data to provide new insight into genome-wide epigenomic interactions.

The overall goal of this resource program is to develop AviPort, a comprehensive genomic, biological and computational analysis portal for avian researchers and the broader biomedical research community. Since the beginning of the modern era of experimental biology in the late 1800?s, birds have been the premier non-mammalian model organism. Technological advances over the past decade have led to increased use of avian organisms, and a dramatic expansion of genomic and biological information for avian species. Lacking an avian model organism database (MOD), avian resources have been developed ad hoc and are dispersed across numerous platforms. A mechanism to unify these resources is critically needed, along with resources and platforms for large-scale data analysis. Accordingly, this proposal will establish AviPort, a comprehensive resource for the avian and broader biomedical research communities. By combining the information integration functions of a classic MOD with a state of the art platform for data management and experimental analysis, AviPort will represent a unique new resource for biomedical research. The goals of this proposal will be accomplished through the following overall specific aims: 1) Incorporate several existing high-value avian genomic and biological resources into a single integrated resource, and leverage larger external resources to provide robust data management and analysis capabilities. 2) Provide high quality, curated data to support biomedical discovery. This will be accomplished by improving the genomes of chicken and other key avian species, developing reference sets for key avian species, standardizing nomenclature, providing functional annotation, and providing data curation to enable integration of avian data types. 3) Disseminate data, results and training resources to ensure that biomedical researchers are able to effectively translate data into discoveries.