Patrick Schnable - US grants

The composition of cuticular wax of plants is complex and incompletely known, and rather little is known of the biosynthesis of this material(s). Two of the seventeen loci known to be involved in biosynthesis of this wax in maize have been tagged with transposable elements; these genes will be cloned and, using the transposable element as a molecular probe, a study of the biosynthetic pathway (localization, structure, and regulation of the gene products) undertaken. Comparison of the events in maize with those of Arabidopsis is planned; these studies should lead to increased comprehension of the biochemical mechanisms involved in the synthesis of these unique and biologically significant plant lipids. The cuticular waxes of plants constitute the first barrier to the environment and the stresses it imposes on plants, and thus these waxes play an important role in plant survival. The composition of these waxes and the biosynthetic pathways by which they are formed are incompletely known. This project will make use of transposon-tagged cloned genes to examine the gene products of the biosynthetic pathway and thus enlarge our understanding of it.//

9808559 Schnable Survival requires that organisms produce a protective barrier between themselves and their environment. Because organisms must be able to sense and respond to environmental signals, this barrier must also allow for an exchange of molecules and information. In plants, the cuticle serves as this barrier. In addition to functioning as a water barrier, it has been suggested that the cuticle provides frost and UV resistance and plays a role in plant-pathogen interactions. Furthermore, the nature of the cuticle greatly affects the deposition and behavior of agricultural chemicals such as pesticides, growth regulators and foliar nutrients sprayed on plants. Cuticular waxes are an important component of the cuticle. They are complex mixtures of acyl derivatives of very long-chain fatty acids made in plant epidermal cells by VLCFA elongase enzyme systems. A maize gene (gl8) responsible for the production of one of these enzymatic functions has been cloned, and the other genes required for the production of functional VLCFA elongase enzyme systems will be cloned. This integrated research approach will enhance our understanding of the mechanism by which the elongation of acyl chains occurs. Because similar types elongation reactions are important in the biosynthesis of many classes of molecules (including polyketides, flavonoids, and stilbenoids) which function as antibiotics, plant-pathogen toxins, pigments and protective compounds, the proposed studies will impact our understanding and ability to manipulate many fundamental biological processes

This Integrative Graduate Education and Research Training (IGERT) award supports the establishment of a multidisciplinary graduate training program of education and research
in computational molecular biology. Due to advances in molecular biology over the past fifteen years, biological questions can now be approached at new levels of complexity. Rather than dissecting individual components of a biological system, system-wide analytical approaches can be pursued. A critical factor in this paradigm shift has been the availability of vast amounts of genomic sequence and expression data. Iowa State University will establish a Computational Molecular Biology Training Group to provide graduate students with the necessary skills to explore complex biological questions. This group will be composed of 25 investigators with diverse areas of expertise, including evolutionary, molecular and structural biology, computer science, mathematics and statistics. Students will be trained to discern biological information from genome sequence and expression data and will focus on four interrelated areas of research: genomics, bioinformatics, genome evolution, and macromolecular structure and function. By infusing training in the biological sciences with the analytic perspective of mathematics and computer science, Iowa State University will create an exciting learning environment in which to prepare students for the challenges and opportunities presented by the post-genomics era.

IGERT is an NSF-wide program intended to facilitate the establishment of innovative, research-based graduate programs that will train a diverse group of scientists and engineers to be well-prepared to take advantage of a broad spectrum of career options. IGERT provides doctoral institutions with an opportunity to develop new, well-focussed multidisciplinary graduate programs that transcend organizational boundaries and unite faculty from several departments or institutions to establish a highly interactive, collaborative environment for both training and research. In this second year of the program, awards are being made to twenty-one institutions for programs that collectively span all areas of science and engineering supported by NSF. This specific award is supported by funds from the Directorates for Biological Sciences, for Computer and Information Science and Engineering, for Mathematical and Physical Sciences (Office of Multidisciplinary Activities), and for Education and Human Resources.

A greater understanding of the organization and function of the maize genome is essential if US agriculture is to be successful in meeting the growing needs for maize as food, feed and a source of industrial raw materials as the US moves towards a "plant-based" economy. A team of molecular, quantitative and evolutionary geneticists and bioinformaticists has been assembled to build upon existing NSF investments in plant genomics to develop the novel high-throughput genetic mapping technologies and resources needed to meet this challenge.

IDPs (InDel Polymorphisms) are a new class of co-dominant, allele-specific, genetic markers suitable for high-throughput analyses. The project team will identify, develop and genetically map 500 IDP markers specific to two widely used inbred lines (B73 and Mo17). To identify efficient IDP isolation strategies, the rates of IDP identification will be compared from four sources: 1) GenBank genic sequences; 2) Mu transposon flanking sequences; 3) 3' UTRs from ESTs; and 4) previously mapped RFLP markers. In addition, computational and wet lab approaches for the high-throughput acquisition and mapping of IDPs will be developed and optimized.

The Mapping Array is a novel chip-based technology designed to genetically map a large number of non-redundant, sequence-defined cDNAs. During Phase I, an existing nylon-based protocol will be adapted to DNA "chips"; hybridization conditions optimized; various sources of "target" sequences (e.g., 3' UTRs, exons, full-length cDNAs and RFLP markers) tested; experimental design parameters determined; and mapping software developed. Upon successful completion of Phase I, 10,000 ESTs will be genetically mapped (Phase II).

The sequences of EST 3' UTRs are of great value in distinguishing members of gene families, as potential sources of IDPs and for producing the gene-specific probes needed for the Mapping Array. The project team will sequence the 3' ends of 20,000 of the ESTs being isolated by the NSF-supported maize genome project headquartered at Stanford University. In addition, the insert size associated with each of these 20,000 clones will be ascertained.
Because the proposed research will occur at the interface between molecular and computational genetics, it will provide important cross-disciplinary training opportunities for graduate students and post-doctoral scientists.

Rice genome sequencing projects are defining thousands of new rice genes. However, the critical tools required to determine the functions of these genes are not yet available to public-sector researchers. To remedy this deficiency, novel approaches to generate collections of insertion mutants and to detect mutated genes in a complementary set of rice deletion mutants will be developed. The first objective is to develop and test vectors for creating activation-tagged rice mutants using the Ac/Ds system. Activation tagging allows the identification of gene knockout mutants and mutants exhibiting gene over-expression. The vectors will contain chemical-induced promoters that will allow the controlled transposition of the Ds element. The second objective is to develop and optimize a high throughput protocol for the detection of mutated genes in an existing deletion mutant collection. The strategy is to use polymerase chain reaction (PCR) to detect the mutated genes rapidly in pools of DNA from many mutants. The rapid detection of mutated genes by this protocol will allow the identification of mutants with deletions in specific genes of interest. The international dimension of the project will enrich the training experience of the postdoctoral fellows and extend the impact of the research.

ABSTRACT


The maize genome is thought to comprise islands of lightly methylated genes in a sea of highly methylated, repetitive DNA. Complete sequencing and assembly of the whole maize genome is currently not feasible because of the repetitive DNA. Methods are beginning to be developed that will allow the regions of the maize genome that contain the genes to be filtered out for sequencing. It is expected that once validated in maize, such methods could be used for similar sequencing projects in other crop species having complex genomes.

A novel expression vector system has been developed to rescue directly open reading frames (ORFs) from genomic DNA. Preliminary data suggest that this "ORF Rescue" vector system is a potentially powerful tool for gene discovery in complex crop genomes. Experiments will be performed to validate the utility of this vector system for preparing filtered template DNA for genome sequencing. The three specific aims of the project are to:

1] evaluate the ability of the ORF Rescue vector to filter genes from maize genomic DNA;
2] evaluate the ability of the ORF Rescue vector to filter genes from maize DNA cloned into Bacterial Artificial Chromosomes (BACs);
3] determine whether novel ORFs define monocot-specific genes.

This work merits funding as a Small Grant for Exploratory Research (SGER) because it represents work on an untested and novel idea that is needed urgently.

To facilitate scientific advances and crop improvement, the thousands of maize genes being discovered during the maize genome sequencing project must be ordered relative to a genetic map. This project builds on prior NSF investments in maize genomics to genetically map 4,500 genes. The resulting high-density transcript map will enhance our understanding of the organization and evolution of the maize genome. These thousands of mapped genes will also provide the necessary sequence-based cross-links to facilitate the alignment of the rice physical map with the maize genetic map. This resource will provide a means to determine the linear order on maize chromosomes of maize genes. One thousand co-dominant genetic markers (Insertion Deletion Polymorphisms [IDP]s) suitable for use in a wide variety of genetic experiments will also be generated and distributed to the public.

The research plan provides numerous, interdisciplinary training opportunities for a diverse group of undergraduate and graduate students at the interface between molecular and computational genomics. The project will use a variety of mechanisms to involve undergraduate students from a research university, an undergraduate college and underserved institutions. These trainees will be provided with intensive mentored research experiences. To help prepare them for scientific careers in a global environment, undergraduate students will visit to the International Maize and Wheat Improvement Center (Spanish acronym CIMMYT) in Mexico. Trainees will also be encouraged to return to the high schools from which they graduated to make short presentations regarding their research activities. Other outreach activities will include a program conducted in partnership with the Reiman Gardens at Iowa State University (http://www.reimangardens.iastate.edu) to expose elementary school students to plant biology.

Deliverables:

4,500 mapped genes and 1000 co-dominant genetic markers (Insertion Deletion Polymorphisms [IDP]s) will be generated and distributed to the public via the project database
(http://pslab.agron.iastate.edu/research/genomics/htp_est/idps.shtml) as well as public databases such as PlantGDB (http://www.zmdb.iastate.edu/PlantGDB/), MaizeGDB (http://www.maizegdb.org/), and Gramene (http://www.gramene.org/).

All above ground organs of higher plants are ultimately derived from specialized organogenic structures called shoot apical meristems (SAMs). The SAM exhibits distinctive structural organization, marked by tissue zonation and cell layering. The structure of plant SAMs is correlated with their function, such that new leaves are initiated from the peripheral zone of the SAM and the central zone replenishes new meristematic cells that are lost during organogenesis. Establishment and maintenance of these structurally and functionally distinct zones of the SAM is achieved by differential expression patterns and interactions of thousands of plant genes. Breakthrough technologies have emerged that enable the identification and analysis of genes required for meristem function in maize. Laser dissection microscopy (LCM) is a powerful technique that permits the isolation of RNA from specific cell types within fixed plant tissues immobilized on slides. RNA collected from 1,000-10,000 cells is sufficient for use in microarray analyses of gene expression, which permit the simultaneous examination of expression profiles of 15,000 to 30,000 genes. The relatively large size of the maize vegetative meristem, approximately 250 meristematic cells are recruited into the incipient maize leaf, renders this plant especially tractable for this experimental system. The laser-capture microdissection/microarray technique will be used to capture cells from specific domains of the maize meristem and newly formed leaf primordia for use in comparative analyses of global gene expression. The differential expression patterns of candidate genes will be verified by more traditional analyses (RT-PCR, RNA gel blot hybridization and in situ hybridization) of transcript accumulation in maize tissues. These experiments will microdissect gene expression patterns in meristems and leaf primordia, and will provide novel insight into mechanisms of plant development.

This research project will generate the following set of deliverables:
1. LCM will be optimized and improved for use on maize SAMs. Information will be available on a project web site at ISU and in publications.
2. Microarray profiles of global gene expression in the SAM will be deposited in public web sites, maizeGDB.
3. An EST collection from meristem-enriched cDNA library will be developed beginning in the second year of the project, depending on need. If developed, all EST sequences will be deposited in Genbank and in maizeGDB.
4. Sequence generated by students mining for maize equivalents to already known genes from other plants will be sent directly to Genbank and maizeGDB.

Outreach and training will be enhanced in three ways:
1. Undergraduate students from Truman State University will engage in cutting edge genomics research.
2. PI Scanlon will deliver guest lectures at local high schools with predominantly minority enrollment.
3. The University of Georgia and the CSHL Dolan DNA Learning Center (DNALC) will train teachers from underrepresented minority high schools and university settings in plant genomics and bioinformatics.

Homologous recombination between chromosomes is a fundamental source of genetic variation and the basis of nearly all genetics and breeding. Meiotic recombination also permits construction of genetic maps, important for applications ranging from marker-assisted selection to map-based cloning of valuable genes. Remarkably, despite its central importance to genetics, little is known about the process of homologous recombination in higher plants, and even less about how it is controlled. This is a comprehensive study of the meiotic recombination process in maize, the largest agricultural crop in the U.S. and a uniquely suited model system for these studies. The study is broad in scope, from discovering new genes involved in the process to mechanisms of crossing over and electron microscopy of protein complexes carrying out recombination events. This research will directly enhance understanding of how plant genomes generate and maintain diversity, how plant genomes are organized and how plant genome organization can influence recombination outcomes. The goal is also to develop a resource on which the community can base future studies of recombination in all plants.

Very long chain fatty acids (VLCFAs), those that contain 20 or more carbon units, are incorporated into a wide variety of physiologically significant phytochemicals, including cuticular waxes, cutin, suberin, sphingolipids, and some phospholipids and seed oils. Genes that encode several of the enzymes involved in the production of VLCFAs have been isolated. Based on the finding that maize mutants that block the production of one of these enzymes are embryo lethal, VLCFAs must be essential during maize embryogenesis. Mutants that block the accumulation of essential VLCFAs therefore provide an ideal experimental system to determine the essential role of VLCFA-derived phytochemicals in plant development.
The proposed project will define the essential developmental role(s) of VLCFAs, by determining the morphological differences that distinguish the development of mutant embryos from normal embryos. It will also establish which VLCFA-derived compound(s) are essential during embryo development, by comparing the accumulation of VLCFA-derived compounds in normal and mutant embryos. Finally, mutants that overcome the embryo lethality associated with genetic blocks in the production of VLCFAs will be isolated. In the long term, these mutants can be used to dissect the diverse network of biochemical pathways that utilize VLCFAs.
The proposed studies meet the challenge of understanding the complex interplay between biochemistry and development by continuing to exploit the synergy inherent in a long-term research collaboration that integrates genetic and biochemical approaches. This project will provide excellent cross-disciplinary training experiences to a diverse group of early-career scientists because it will use a combination of hypothesis-driven and genome-wide analyses, and apply cutting-edge technologies to address significant questions in plant biology that have proven recalcitrant to a mono-disciplinary approach.

ABSTRACT

Storage of biomolecular sequences, and accessing them to determine sequence homologies is central to the current revolution in bioinformatics and computational biology. Besides search tools, the large size of biological data used by some important applications underscores the need for developing efficient out-of-core algorithms. The goal of the project is to design storage structures, algorithmic techniques, and software for disk-resident sequence data, and apply it to important applications in computational biology. To achieve this
goal, a three-pronged strategy is used: Firstly, application requirements identified in collaboration with domain experts are being used to design fundamental storage structures for sequence data. This research spans the development of efficient out-of-core algorithms for well-known in-core data structures and also the design of new data structures suitable for targeted applications. Secondly, efficient algorithms for queries on disk-resident sequence data are being developed. Finally, the out-of-core techniques developed are integrated with application software in computational genomics such as EST clustering and fragment assembly. The goal is to develop faster algorithms, reduce the exorbitant main-memory requirements, or enable solution of larger problem instances, as appropriate.

The results of the research will be made accessible to computer scientists
in the form of software libraries and molecular biologists in the form of application software. Efforts are being made to integrate the results of this research into popular tools used by molecular biologists. The interdisciplinary nature of the project is providing unique training opportunities for graduate students.

This project, supporting interdisciplinary computational projects in bioinformatics and computational biology, computational genomics, and plant sciences, enables several research projects, including:
-Assembly, validation, and annotation of the maize genome,
-Large-scale Express Sequence Tag (EST) clustering with applications to gene identification,
-Detection of functional and regulatory clusters of proteins through whole organism protein network analysis, and
-Design and simulation of FPGA-based engines for computational genomics.
Enabling solution of large-scale applications of current relevance, the infrastructure contributes to develop, demonstrate, and disseminate high performance computing techniques and comprehensive software systems capable of solving such applications. The team consists of researchers with expertise in parallel algorithms, architectures, high-performance software development, bioinformatics and computational biology, molecular biology, functional genomics, maize genetics, and protein structural biology. The instrumentation will be used to perform clustering of the largest-scale human and mouse EST collections, and perform EST-based gene discovery at unprecedented sale and speed. Whole organism protein systems biology studies will be used to uncover functional and regulatory clusters of proteins. Design simulation of FPGAs for computational genomics applications will enable other researchers to solve large-scale problems with modest size cluster equipped with FPGAs.

Broader Impact: The project contributes web-based community resources and/or infusion of new knowledge into existing web-based community resources. The instrumentation benefits research and educational activities in the areas addressed. Collaborative ties with New Mexico State University, a Hispanic serving doctoral extensive institution ensures involvement of underrepresented students. Iowa offers Women in Science and Engineering summer program for high school female students

PI: Richard K. Wilson, Ph.D., Washington University
Co-PIs: Dr. Sandra Clifton, Ph.D., Washington University St. Louis
Dr. Rod Wing, Ph.D.,University of Arizona
Dr. Patrick Schnable, Ph.D.,Iowa State University
Dr. Srinivas Aluru, Ph.D.,Iowa State University
Dr. Lincoln Stein, M.D. Ph.D., Cold Spring Harbor Laboratory
Dr. Doreen Ware, Ph.D., Cold Spring Harbor Laboratory
Dr. W. Richard McCombie, Ph.D., Cold Spring Harbor Laboratory
Dr. Robert Martienssen, Ph.D., Cold Spring Harbor Laboratory


Maize is an important biological research system with a long and rich history. Over the past century, an active research community has grown up around the available extensive genetic tools and diverse germplasm. In this time, maize has become a leading system for addressing fundamental questions in genetics such as the impact of domestication on genome structure, the molecular basis of heterosis (hybrid vigor), and role of transposons in genome evolution. Research on maize has led to major advances in our understanding of fundamental life processes in plants such as reproduction, seed formation, germination, photosynthesis, and biosynthesis of primary metabolites including amino acids, carbohydrates and fatty acids. A genome sequence is a logical next step to enable the best use of maize as an experimental system and in order to translate research advances into improved crops.

At 2.6 billion base pairs, the maize genome is about the same size as the human genome. However, its organization is far more complex. More than 80% of the genome is made up of a complex mixture of repetitive DNA that includes several classes of retrotransposon. Only about 20% of the genome comprises the genes and these are scattered throughout the 10 chromosomes in small islands. A detailed physical map that is linked to the genetic map has been developed that covers more than 90% of the genome. Based on this detailed knowledge about the genome structure and organization, the maize research community has developed a description of the "gold standard" for a genome sequence (http://www.maizegdb.org/genome/goldstandard.pdf). The gold standard maize genome is defined as containing the complete sequence and structures of all maize genes and their locations (in linear order) on both the genetic and physical maps of maize, using B73 as the reference genome.

This project is designed to provide a maize genome sequence as close to the gold standard as possible with the currently available technology. Bacterial Artificial Chromosome (BAC) clones of known location along the physical map will be sequenced to 6x coverage, and integrated with the available sequence information obtained through prior sequencing of Expressed Sequence Tags (ESTs), transposon insertion sites, gene-enriched genomic DNA and whole genome shotgun libraries. The resulting genome sequence will contain high quality sequence (fewer than one error per 100,000 bases) of the non-repetitive regions that include the genes and regulatory elements, anchored to the genetic and physical maps. The sequence will be annotated for gene models, predicted exon/intron structures, EST and full-length cDNA data, gene ontologies, and homologies with sequences from other organisms.
Educational activities that focus on maize and the genome sequence and aimed at K-12 students and their parents will be developed at Washington University St. Louis Genome Sequencing Center in collaboration with the St. Louis Science Center. Training resources, tutorials and workshops for end-users of the maize sequence appropriate for researchers, students, and breeders will be developed and made available through Gramene (http://www.gramene.org) located at the Cold Spring Harbor Laboratory.

All primary and assembled sequences over 2,000 base pairs in length will be deposited in GenBank (http://www.ncbi.nlm.nih.gov/Genbank/) within 24 hours of generation. Trace files will be deposited in the NCBI Trace Repository (http://www.ncbi.nlm.nih.gov/Traces/trace.cgi) within one week of production. Assembled, finished BAC clone sequences will be deposited in GenBank as soon as the finished sequence has passed all quality analysis tests and has been approved for submission by the quality analysis team. Project information and data will be available through a web site accessible through the Washington University Genome Sequencing Center (http://www.genome.wustl.edu). Maize sequence assemblies and maize genetic resources will be incorporated into Gramene (http://www.gramene.org) and MaizeGDB (http://www.maizegdb.org) on a quarterly basis.

Maize (corn) is one of the most economically important plants in the US. In 2004, 80.9 million acres of corn were planted with a production value of over $22 billion. While corn is grown in the U.S. for food and feed, it is also converted into a myriad of processed food products and serves as an important material for many industrial products. The maize genome sequence will be a key resource for continued use of maize as an experimental system for advancing fundamental biology as well as for the development of new and improved maize varieties in the public and private sector.

This project was funded as part of the Maize Genome Sequencing Project: An NSF/DOE/USDA Joint Program.

PI: David Hannapel Proposal # 0518902
"The 7th Plant Science Symposium, Meristems 2005"
June 2-5, 2005, Iowa State University, Ames, IA

Abstract

The 7th Plant Science Symposium, Meristems 2005, will focus on meristem biology. Meristems are the growth centers of plants and embody many exciting concepts for plant developmental biologists. Meristems are small but dynamic structures, where processes of cell signaling and coordination in growth occur. As the centers of plant growth, meristems are also of great importance to agriculture and crop production. For example, most of our current knowledge about the mechanisms that control flowering in plants has been obtained from studies of meristems. During this Symposium, the world's leading experts in the biology of meristems will gather on the Iowa State University campus to discuss the latest discoveries in plant development. In a venue designed for discussion and interaction, the symposium will focus on the developmental and genetic processes that encompass growth activity throughout the plant. The symposium will include keynote presentations by internationally recognized speakers, poster sessions, and workshops. This meeting will facilitate opportunities for plant biologists to meet, discuss and interact in an informal and open setting. The program also addresses the current and potential applications of meristem biology for gene discovery and crop improvement. Significant participation and support from researchers in the biotechnology industry is expected. Because of the important role that meristems play in overall growth and crop production, it is expected that scientists from a wide array of disciplines will be attracted. By bringing together researchers interested in various aspects of plant development, this meeting will facilitate the exchange of information and ideas and will encourage participants to reach out in new directions in their future research.

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).

Most of what is known about the genetic and genomic consequences of polyploidy (genome doubling) is derived from the study of crops, synthetic polyploids, and model plants. To better understand how polyploidy shapes genome evolution and gene function, this project will investigate naturally occurring polyploids using genomic tools. Tragopogon (sunflower family) provides the unique opportunity to study the genetic and genomic changes that occur across a continuum from F1 hybrids to synthetic polyploids to recently and recurrently formed natural populations of T. mirus and T. miscellus that are only 60-80 years post-formation. Through analysis of hundreds of gene loci, the relative frequency of gene loss and gene expression changes in natural polyploid populations of T. mirus and T. miscellus of independent origin and in multiple lines of synthetic polyploids will be assessed.

Understanding polyploid evolution is central to understanding the origin and diversification of most lineages of life, particularly flowering plants, a group in which polyploidy is common (e.g., most crops and weeds are polyploid). This study will be the first application of the proposed genomics methodology to non-model species and serves as a model for use of genomic tools to study species for which limited genetic and genomic resources are available. The project includes many training and mentoring activities. It will provide interdisciplinary research-based training for a postdoctoral scientist, 3 technicians, 2 graduate students and 2 undergraduate students. It will organize symposia and provide mentoring for women in science and for undergraduate researchers. The project also will sponsor workshop for pre-college teachers on genomics and evolution and 2 workshops at professional scientific meetings on use of genomics tools.

PI: Patrick S. Schnable (Iowa State University)

CoPIs: Brent Buckner (Truman State University), Carolyn J. Lawrence (Iowa State University/USDA-ARS) and Dan Nettleton (Iowa State University)

Maize exhibits levels of structural variation (SV) of non-repeat sequences that are unprecedented among higher eukaryotes. This SV includes hundreds of copy number variants (CNVs) and thousands of presence/absence variants (PAVs). Many of the PAVs contain intact, expressed, single-copy genes that are present in one haplotype but absent from another. The goal of this project is to test the hypothesis that differences in gene copy number (both gains and losses) contribute to the extraordinary phenotypic diversity and plasticity of maize. Maize is a good model for these studies because it exhibits a rapid decay of linkage disequilibrium (LD) and because a draft genome sequence of the B73 inbred and mapping populations are available. In this project, the "Zeanome", a near-complete set of genes present in B73, other maize lines and the wild ancestor of maize (teosinte), will be defined using existing genomic sequence data and newly generated transcriptomic data. SV among maize and teosinte lines will be identified relative to the Zeanome. By mapping these CNVs and PAVs to phenotyped RILs it will be possible to test whether SV contributes to phenotypic variation. The hypothesis that SV contributed to the domestication of maize and the success of long-term selection will be tested.

The studies will inform crop improvement. A finding that changes in gene copy number contribute to genetic gain would be transformative to the breeding industry. To help adapt crops to climate change it may be desirable to reintroduce into breeding germplasm stress resistance genes (PAVs) and genetic diversity inadvertently lost during domestication. Enhanced understanding of PAVs may help breeders develop improved hybrids. The analyses of yield components may lead to the discovery of genes with relevance to crop improvement. The resulting understanding of domestication may assist with the domestication of new bioenergy crops. The annotated "Zeanome" may be used to address a wide variety of biological investigations. All sequence data will be deposited in GenBank. All project data will be made available to the community via several existing databases, including MaizeGDB (www.maizegdb.org), Gramene (www.gramene.org) and Panzea (www.panzea.org). Tools will be developed so that MaizeGDB can better serve the community. Consistent with the current 5 year plan for the National Plant Genome Initiative (NPGI), the research will provide numerous and diverse training opportunities that will contribute to the development of an internationally competitive scientific workforce. Because the activities will occur at the interface of plant genomics, bioinformatics, and statistics, they will provide cross-disciplinary training. A variety of proven mechanisms will be used to provide large numbers of undergraduates from research universities, predominately undergraduate institutions (PUI) and underserved institutions with mentored research experiences. A teacher-scholar from a PUI will spend one month immersed in a laboratory at a research-intensive university. A postdoctoral teacher-scholar will be mentored in skills needed to excel at a PUI. Additional outreach will occur in partnership with an NSF-funded video-sharing community and via a novel video-based approach to inform students and the public about the lives of scientists.

Much of what is known about the evolutionary fate of genes following genome doubling (polyploidy) is based on several crops that are thousands of years old and synthetic polyploids that are only several generations old. This project will investigate the very surprising finding that homeologous genes are lost rapidly after polyploidization, rather than being silenced or other regulatory changes. They want to know if there are general rules about which genes or larger pieces of genomes are preferentially lost. This is important because previous findings were from synthetic polyploids and recent natural polyploids (<100 years). The plant genus Tragopogon (goatsbeard, in the sunflower family) provides a unique opportunity to investigate changes that occur following polyploidy in nature across a range of ages. Tragopogon contains diploids, natural polyploids that are only 60-80 years old, synthetic polyploids, and older Eurasian polyploids. This study will investigate an older (0.8 -2.8 million years ago, mya) natural polyploid for comparison with the recently formed polyploids; it also extends to an ancient polyploidy event (>30 mya) early in the history of the sunflower family.


Genome doubling (polyploidy) has generated much of the diversity of life. All vertebrates and flowering plants experienced at least one episode of ancient polyploidy, and many crops are polyploids. Hence, elucidating the consequences of polyploidy will enhance our understanding of the diversification of life and provide fundamental knowledge about crop genomes. Results will provide important insights into the processes that generated much of life and most crops. In addition, the project will train undergraduate and graduate students, and a post-doctoral scholar, as well as including outreach activities to the general public.

High-throughput next-generation DNA sequencing technologies (NGS) are causing a major revolution in life sciences research by allowing rapid and cost-effective sampling of genomes and transcriptomes (expressed genomic sequences). Assembly of genomes and transcriptomes from billions of such randomly sampled sequences is an important problem in computational biology. While significant strides have been made, much work remains in addressing the diverse and rapidly emerging platforms, improving assembly quality, and scaling to both large-scale data sizes and large genomes.

This project will harness the power of high performance computing to develop effective solutions for sequence assembly. It will lead to the development of scalable, efficient parallel algorithms and a parallel integrated software framework for genome and transcriptome assembly. The project seeks to advance the state of the art by targeting important unsolved problems such as hybrid assembly of sequences from multiple NGS platforms, making fundamental algorithmic advances to improve assembly quality, and conducting an in-depth effort at parallel algorithms development for the entire gamut of problems that arise in connection with assembly. It will be carried out by an interdisciplinary team of investigators, in partnership with leading NGS manufacturers and academicians involved in large plant genome sequencing projects.

The project will lead to the release of a scalable parallel software package for sequence assembly that will be made available to the scientific community. Postdoctoral and graduate students will be trained in computer science driven interdisciplinary research and in writing efficient high performance computing software. The project will influence curriculum development and will lead to educational materials in bioinformatics for next-generation sequencing.

PI: Michael Scanlon (Cornell University)

Co-PIs: Diane Janick-Buckner and Brent Buckner (Truman State University), Gary Muehlbauer (University of Minnesota-Twin Cities), Thomas Owens (Cornell University), Patrick Schnable (Iowa State University), Marja Timmermans (Cold Spring Harbor Laboratory), Jianming Yu (Kansas State University) and Xiaoyu Zhang (University of Georgia)

Collaborators: Carolyn Lawrence (USDA-ARS/Iowa State University) and Dan Nettleton (Iowa State University)

The shoot apical meristem (SAM) is responsible for development of all above ground organs in the plant. SAM structure and function correlates with agronomically-important adult traits in the maize plant, and is also affected by planting density and shade stresses induced by agricultural environments. The ultimate goal of this project is to increase understanding of the regulatory networks controlling SAM structure and function and the responses of these networks to environmental stresses. The specific objectives are to: 1) describe the SAM allometric space in maize and its relatives using nanoscale computer tomographic scanning to provide 3-dimensional images of the phenotypic diversity of SAM structure and identify adult plant traits correlated with SAM structure; 2) identify differentially expressed genes in SAM size/shape outliers and mutants with abnormal SAM structures and generate a co-expression network of key genes implicated during SAM structure and function; 3) perform quantitative genetic analyses to identify specific variations within genes that correlate with variations in SAM structure/function and adult plant traits, and test functions of 40 key genes using reverse genetic aaproaches; 4) analyze the shade avoidance response and its effects on SAM structure and function; and 5) investigate epigenetic changes of SAM functional domains in response to shade avoidance using novel protocols that distinguish the stem cell organizing regions from the organogenic domains in the maize SAM.

These studies will provide the framework for scientific training and the public release of original data. Undergraduates at Truman State University, a small liberal arts institution, will be trained in morphological and LM-RNAseq analyses of maize mutants. REU students and undergraduates enrolled in Plant Physiology courses at Cornell University will participate in physiological experiments. This project will generate extensive transcriptomic data and vector constructs for tissue-specific epigenetic analyses which will be available to the scientific research community. Molecular markers and phenotypic data for diverse maize lines will be supplied to Panzea (http://www.panzea.org/). Genetic mapping associations, physiological shade-avoidance response data, transcriptomic and phenotypic data will be curated at MaizeGDB (http://www.maizegdb.org/), and seed stocks for maize shoot mutants and SAM size variants will be released through the Maize Genetics Cooperation Stock Center (http://maizecoop.cropsci.uiuc.edu/).

The recent emergence of a variety of high-throughput DNA sequencing instrumentation, and the concomitant rapid decline in the cost per base, is causing severe data deluge in all areas of life sciences. The heterogeneity of sequencing instrumentation and the vast diversity of applications enabled by them are creating numerous analytics problems for the bioinformatics community to address. In addition, the conventional serial algorithms that have been the mainstay of bioinformatics research are severely challenged by the ever increasing data sets. The goal of the proposed project is to develop core techniques and software libraries to enable scalable, efficient, high performance computing solutions for high-throughput DNA sequencing, also known as next-generation sequencing (NGS). To empower the larger community, the project seeks to 1) identify a set of core functionalities that frequently occur in many types of high-throughput sequencing applications, 2) develop efficient parallel algorithms and high performance implementations for them, 3) pursue mapping to HPC architectures including clusters, multicores, and GPUs, 4) develop software libraries encapsulating these functionalities with the goal of enabling the bioinformatics community to exploit HPC architectures, and 5) design a domain specific language to enable bioinformatics researchers unfamiliar with parallel processing to benefit from this work through automatic generation of parallel codes. The research will be conducted in the context of challenging problems in human genetics and metagenomics, in collaboration with domain specialists.

This project is focused on a key capacity building activity to facilitate pervasive use of parallelism by NGS bioinformatics researchers and practitioners. The goal is to empower the broader community to benefit from clever parallel algorithms, highly tuned implementations, and specialized HPC hardware, without requiring expertise in any of these. The software libraries will be released as open source for use, further development, enhancements, and incorporation by the community. The project will provide opportunities for training postdoctoral and graduate students in bigdata analytics and computer science driven interdisciplinary research. Diverse existing mechanisms at the partner institutions will be leveraged to advance goals of minority and women recruitment, undergraduate participation in research, and K-12 outreach.

NRT- DESE: Predictive Phenomics of Plants (P3)

New methods to increase crop productivity are required to meet anticipated demands for food, feed, fiber, and fuel. Using modern sensors and data analysis techniques, it is now feasible to develop methods to predict plant growth and productivity based on information about their genome and environment. However, doing so requires expertise in plant sciences as well as computational sciences and engineering. This National Science Foundation Research Traineeship (NRT) award to Iowa State University will bring together students with diverse backgrounds, including plant sciences, statistics, and engineering, and provide them with data-enabled science and engineering training. The collaborative spirit required for students to thrive in this unique intellectual environment will be strengthened through the establishment of a community of practice to support collective learning. This traineeship anticipates preparing forty-eight (48) master's and doctoral students, including twenty-eight (28) funded doctoral students, with the understanding and tools to design and construct crops with desired traits that can thrive in a changing environment.

Understanding how particular genetic traits result in given plant characteristics under specific environmental conditions is a core goal of modern biology that will facilitate the efficient development of crops with commercially useful characteristics. Plant characteristics are influenced by genetics and a wide range of environmental factors, including, for example, rainfall, temperature and soil types. Developing methods to effectively integrate these diverse inputs that take advantage of existing biological, statistical, and engineering knowledge will be a key area in this research and training program that will bring together faculty from eight departments. Trainees will engage in cutting-edge research and development areas involving direct data collection and analysis from living plants, including sensor development, high throughput robotic technology, and biological feature extraction through image analysis. This traineeship will use the T-training model to provide students with training across a broad range of disciplines while developing a deep technical expertise in one area. This expertise, in combination with soft skills development, will enable the trainees to work across organizational and cultural boundaries as well as scientific disciplines. To develop understanding of how to share knowledge with diverse groups, the program will provide students with training beyond traditional coursework and research through activities that will develop advanced communication and entrepreneurship skills. Additionally, internship opportunities in industry, national labs, and other settings will equip trainees to choose among the diverse career paths available to scientists and engineers.

The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new, potentially transformative, and scalable models for STEM graduate education training. The Traineeship Track is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas, through the comprehensive traineeship model that is innovative, evidence-based, and aligned with changing workforce and research needs.

An award is made to Iowa State University to develop and deploy PhenoNet - an integrated robotic network for field-based studies of genotype crossed with environment (GxE) interactions. The core component of PhenoNet is a set of PhenoBots; lightweight robots that are able to autonomously navigate between crop rows using GPS and local range sensors while employing advanced sensing technologies to phenotype crop plants. The PhenoBots can measure indicators such as stalk size, plant height, leaf angle and tassel/inflorescence properties over time. The robots will be optimized for maize research and can be easily adapted for other row crops. The network (PhenoNet) is a universal platform which enables comprehensive field-based research on genotype and environment interactions. The broader impacts of this project are threefold. First, PhenoNet will have an important impact on society as understanding genome X environment interactions will help address the need for sufficient food, feed, and fiber for the planet's growing population, which is vital in an ever-changing environment. PhenoNet will bring "big data" more deeply into agriculture by cementing connections between plant scientists and engineers in their efforts to reach this goal. Second, this project is synergistic with the NSF-NRT project, "Predictive Phenomics of Plants", recently awarded to Iowa State University. The research and engineering outlined in this Major Research Instrumentation project will provide an outstanding opportunity for students from engineering disciplines, computer science, statistics, and agronomy to collaborate and engage in state-of-the-art interdisciplinary research. This project will also advance the training of current engineers and plant scientists who are experienced with networking, robotics and agronomy. Third, this project will reach out to underrepresented groups by targeting minority-serving institutions for student recruitment and will work with the Society of Women Engineers and other similar groups in seeking women participants to help meet the NSF-NRT award's efforts to broaden participation.

The PhenoBots are an important and essential advancement in the fields of agriculture and technology because they more efficiently characterize tall plants over time to their maturity. Previous technology and platforms are either incapable of, or are greatly hindered by various constraints. The design improvements of the Phenobots enable the robots to be more robust, stable, lightweight, integrated and economical. This creates a pathway for transformative research as it enables in situ, non-invasive monitoring of the traits of tall crops, like maize, over time. PhenoNet will consist of a network of four PhenoBots, which will be deployed by plant scientists in Iowa, Kansas, Minnesota, Nebraska, and Wisconsin. The data generated from high throughput phenotyping will address whether it is possible to predict the phenotype of a given genotype in a specified environment.

Understanding how gene by environment interactions result in specific phenotypes is a core goal of modern biology and has real-world impacts on such things as crop management. Developing and managing successful crop practices is a goal that is fundamentally tied to our national food security. By applying novel computational visual analytical methods, this project seeks to identify and unravel the complex web of interactions linking genotypes, environments and phenotypes. These methods will first need to be designed and developed into usable software applications that can handle large volumes of crop phenomics data. High-throughput sensing technologies collect large volumes of field data for many plant traits, such as flowering time, related to crop development and production. The maize cultivars used here come from multiple genotypes that have been grown under a variety of environmental conditions, in order to give the widest range of conditions for understanding the interactions. The resulting data sets are growing quickly, both in size and complexity, but the analytical tools needed to extract knowledge and catalyze scientific discoveries have significantly lagged behind. The methodologies to be developed in this project represent a systematic attempt at bridging this rapidly widening divide. The project is inherently interdisciplinary, involving close research partnerships among computer scientists, plant scientists, and mathematicians. The research outcomes will be tightly integrated with education using a multipronged approach that includes, among others, postdoctoral and student training (graduates and undergraduates), curriculum development for a new campus-wide interdisciplinary undergraduate degree in Data Analytics, conference tutorials for training phenomics data practitioners, and contribution to the recruitment and retention of underrepresented minorities (particularly women) in STEM fields through the Pacific Northwest Louis Stokes Alliance for Minority Participation.


This project will lead to the design and development of a new, scalable, visual analytics platform suitable for hypothesis extraction and refinement from complex phenomics data sets. Focus on hypothesis extraction is critical in the context of phenomics data sets because much of the high-throughput sensing data being generated in crop fields are generated in the absence of specifically formulated hypotheses. Extracting plausible hypotheses from the data represents an important but tedious task. To this end, this project will apply and develop new capabilities using emerging advanced algorithmic principles, particularly from the branch of mathematics called algebraic topology that studies shapes and structure of complex data. The research objectives are three-fold. First, the project will employ and extend emerging algorithmic techniques from algebraic topology to decode the structure of large, complex phenomics data. Second, an interactive visual analytic platform will be developed to facilitate knowledge discovery using the extracted topological structures. Lastly, the quality and validity of a new visual analytic platform designed by this team will be tested using real-world maize data sets as well as simulated inputs as testbeds. The developed framework will encode functions for scientists to delineate hypotheses of three kinds: i) genetic characterization of single complex traits; ii) genetic characterization of multiple traits that share potentially pleiotropic effects; and iii) decoding and detailed characterization of genotype-by-environmental interactions, in particular, through a collaborative pilot study of maize flowering and growth traits. The expected significance of the proposed work is that biologists will be able to extract different types of testable hypotheses from plant phenomics data sets by employing a new class of visual analytic tools, and thus obtain a deeper understanding of the interactions among genotypes, environments and phenotypes. The project is potentially transformative in two ways: i) it will introduce advanced mathematical and computational principles into mainstream phenomic data analysis; and ii) it will usher in a new era where biologists spearhead data-driven hypothesis extraction and discovery with the aid of interactive, informative, and intuitive tools. The project will have a direct impact on the state of software in phenomics for fundamental data-driven discovery. To facilitate broader community adoption, the project will integrate the tools into the CyVerse Institute, and to a community phenomics software outlet. It will also lead to the development of automated scientific workflows. Project website: http://tdaphenomics.eecs.wsu.edu/

Imaging and other sensor-based understanding of plant behavior is becoming key
to new discoveries in plant genotypes leading to more productive and environment-friendly farming.
Similarly, distributed sensing is seen as a key component of a safe, efficient, and sustainable autonomous transportation systems.
Existing research and education in agriculture and transportation systems are constrained by the lack of connectivity between field-deployed testbed equipment and computing infrastructure. To realize that connectivity, this project proposes to deploy CyNet wireless networks to
connect experimental science testbeds to high-performance cloud computing infrastructures.

The CyNet project will:
1) deploy Predictable, Reliable, Real-time, and high-Throughput (PRRT) wireless networking solutions using the standards-compliant, open-source Open Air Interface software framework and commodity Universal Software Radio Peripheral (USRP) hardware;
2) integrate these wireless networks with software defined networks to seamlessly integrate outdoor cameras, sensors, and autonomous vehicles, and connect these components to high performance cloud computing systems;
3) implement an infrastructure virtualization system that partitions CyNet into programmable, isolated experiments; and
4) create an infrastructure management system that performs admission and access control and establishes specified resource allocation policies.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.