OMAP: The Oryza Map Alignment Project

Rice is the world's leading food crop, feeding over 50% of the world's population. As populations grow, increased rice production is necessary. In the next 20-25 years, the world's population that depends on rice is expected to double. To meet this demand, genetic improvement of rice cultivars is essential. Wild rice species have diverged independently and offer an untapped resource of genes that could be used in the improvement of rice. The genome sequence of cultivated rice, Oryza sativa, is now available and provides a framework for doing comparative studies between wild rice species and cultivated rice. By examining the regions that are highly conserved or rapidly evolving between the genomes we can find genes that may be important in solving some of the world's rice production issues including yield, drought and salt tolerance and disease and insect resistance. <\p>

To better understand the wild relatives of rice and take advantage of the recently sequenced rice genome, a comparative genome project was funded by the National Science Foundation entitled the Oryza Map Alignment Project (OMAP) (Wing et al. 2006; Wing et al. 2005). The key goals of this project are to:


  1. Construct DNA fingerprint/BAC-end sequence physical maps from 11 deep coverage BAC libraries that represent the 11 wild genomes of Oryza (830,000 fingerprints; 1,659,000 BAC ends)
  2. Align the 11 physical maps with the sequenced reference subspecies japonica and indica
  3. Construct high-resolution physical maps of rice chromosomes 1, 3 and 10 across the 11 wild genomes using a combination of hybridization and in silico anchoring strategies
  4. Provide convenient bioinformatics research and educational tools (FPC and web-based) to rapidly access and understand the collective Oryza genome
  5. Release data publically through Gramene and peer reviewed journals


Participants

Dr. Lincoln Stein
Principal Investigator, Cold Spring Harbor Laboratory
Dr. Doreen Ware
Principal Investigator, Cold Spring Harbor Laboratory
Bonnie Hurwitz
Scientific Consultant, Cold Spring Harbor Laboratory

Collaborators

Arizona Genomics Institute
Rod Wing PI
Dave Kudrna Project Manager
Yeisoo Yu Senior Personnel
HyeRan Kim Senior Personnel
Kiran Rao Computational Support
Arizona Genomics Computational Lab
Cari Soderlund PI
William Nelson Systems Programmer
Lomax Boyd Systems Support
Purdue University
Scott Jackson PI
Phillip SanMiguel Senior Personnel
Jianxin Ma Senior Personnel
National Center of Gene Research, China
Bin Han PI

Proposal

OMAP NSF Award
The long term goal of our collaboration is to develop an experimentally tractable and closed model system to globally unravel and understand the evolution, physiology and biochemistry of the genus Oryza. The specific objectives of this proposal are to: 1) Construct DNA fingerprint/BAC-end sequence physical maps from 11 deep coverage BAC libraries that represent the 11 wild genomes of Oryza (830,000 fingerprints; 1,659,000 BAC ends); 2) align the 11 physical maps with the sequenced reference subspecies japonica and indica; 3) construct high-resolution physical maps of rice chromosomes 1, 3 and 10 across the 11 wild genomes using a combination of hybridization and in silico anchoring strategies, and; 4) provide convenient bioinformatics research and educational tools (FPC and web-based) to rapidly access and understand the collective Oryza genome.

Broader Impacts


The research proposed will provide the first ever closed experimental system to understand the evolution, physiology and biochemistry of a single genus in plants or animals. We will align representatives of eleven wild genomes of rice, including both diploids and tetraploids, to the sequenced and finished O. sativa ssp. japonica AA diploid genome. Such a system will empower the scientific community to address complex scientific questions on a whole genome scale. For example, one would be able to determine the majority of genome rearrangements leading to the present day wild species as compared with the sequenced cultivated rice. Such data can be used to study the dynamics of the evolution of a genus and the impacts of domestication. Another example is that one could move vertically across genomes to explore the diversity and evolution of disease resistance gene clusters and their cis regulatory elements. Such data could be used to rapidly identify new and useful disease resistance genes as well as to define conserved regulatory sequences. This research will not only impact rice genomics but will be useful for understanding monocot biology in general and can serve as a model to establish similar systems in both plants and animals.