P0520
The Teosinte (Zea mays ssp. parviglumis) de novo Genome Assembly

Date: Monday, January 12, 2015
Room:
Arun S. Seetharam , Iowa State University, Ames, IA
Zhihong Lang , School of Life Science and Engineering, Southwest University of Science and Technology, Mianyang, China, Biotechnology Research Institute/The National Key Facility for Crop Gene Resources and Genetic Improvement,, Beijing, China
Zachary Lemmon , Genetics Department, University of Wisconsin, Madison, WI
Andrew J. Severin , Iowa State University, Ames, IA
John Doebley , University of Wisconsin, Madison, WI
Jinsheng Lai , State Key Lab of Agrobiotechnology, China Agricultural University, Beijing, China
Matthew B. Hufford , Iowa State University, Ames, IA
Maize (Zea mays ssp. mays) was domesticated from the wild grass teosinte (Zea mays ssp. parviglumis) approximately 9,000 years ago in the Balsas River Basin of southwest Mexico (Matsuoka et al., 2002). Insights regarding gene loss, gene gain, and large-scale structural evolution during domestication can be discovered by comparing the genomes of teosinte and maize. Characterization of these phenomena will improve our understanding of the domesticated phenotypes plant breeders fine-tune to improve maize production. However, to effectively compare maize and teosinte, high-quality teosinte genome assemblies must first be generated. Here we report our assembly efforts to produce draft genomes for two teosinte inbred lines (TIL01 and TIL11). We used three different de novo assemblers (RAY, ALLPATHS-LG and MaSuRCA) on the XSEDE High Performance Computing clusters to generate the draft assembly. Our preliminary results indicate that MaSuRCA has performed better in terms of total genome assembly length, CEGMA genes coverage and other assembly metrics. The assembled scaffolds for TIL01 (1.2 gigabases ) and TIL11 (1.8 gigabases) cover approximately 50% and 75% of the teosinte genome. Estimated repetitive content of these genomes ranges from 80 to 85 per cent. We plan to use additional long reads (PacBio) and RNA-seq data to improve this assembly.