AMG-2: DEVELOPMENT OF GENOME INTEGRATED PHYSICAL MAPS OF PATHOGENIC FUNGI BY FINGERPRINTING

AMG-2   Agricultural Microbes Genome 2 Conference

Town & Country Hotel, San Diego, CA, January 17-19, 2001.


Poster: Microbial Genome Sequencing/Programs
P01_04.html

DEVELOPMENT OF GENOME INTEGRATED PHYSICAL MAPS OF PATHOGENIC FUNGI BY FINGERPRINTING

Jeffry Shultz1, Hong Bin Zhang2, Khalid Meksem1, DAVID LIGHTFOOT1

1 Department of Plant Soil and General Agriculture, Southern Illinois University at Carbondale, Carbondale, IL 62901-4415 USA
2 Department of Soil and Crop Sciences, Crop Biotechnology Center, Texas A & M University, College Station, TX 77843-2123 USA

Genomic methods for isolating economically important genes by DNA sequencing are in their infancy. Problems to be addressed derive from the difficulty of physical mapping, the difficulty of finishing repeat DNA sequence, and the incomplete methods for gene identification based on sequence. The development of robust techniques for physical mapping of entire microbial genomes provides an improved strategy for genome sequencing and gene identification. Using a PAGE fingerprinting method and HindIII Hae III digestion we developed physical maps of several fungal genomes, and integrated them with genetic maps, portions of EST libraries and genome sequencing projects. A physical map of Ustilago maydis was used to validate the techniques. Stringency values in the range of e-7 to e-12 provided consistent physical maps with clones per contig reducing from 41 to 12 in a linear manner over this range. Below e-7 extensive branching of maps was observed as judged by comparison to a map generated by hybridization and validated by genome sequencing. The technique was also used for Fusarium solani f. sp. glycines causative agent of soybean sudden death syndrome. Physical maps can be used to increase the efficiency of whole genome sequencing by providing a minimum tile path for DNA sequencing, bridging gaps in shotgun genomic sequence, identifying gaps in a genomic clone libraries and identifying regions with high gene content for selective sequencing. We will describe automated methods for physical map generation, minimum tile sequencing and the identification of ESTs with BAC clones and contigs.


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