ARS | Publication request: Use of robust-long serial analysis of gene expression to identify novel fungal and plant genes.

Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: June 1, 2006
Publication Date: October 1, 2006
Citation: Gowda, M., Varichannarayappa, V., Jia, Y., Stahlberg, E., Pampanwar, V., Soderlund, C., Want, G. 2006. Use of rl-sage analysis to identify novel fungal and plant genes involved in host-pathogen interactions. In: Ronold, P.C,, editor. Methods and Protocols, Humann press, Inc., Totowa, New Jersey Humann Press, Inc. Vol. 354:131-144.

Interpretive Summary: Rice functional genomics is used to determine the biological function of rice DNA sequences for improving productivity of rice crops. Identification of important genes from fungal pathogens and host plants is indispensable for full understanding of the molecular events occurring during fungal-plant interactions. An improved long range serial analysis of gene expression method called Robust-LongSAGE (RL-SAGE) for detailed transcriptional analysis of fungal and plant genomes is developed to facilitate the functional study of host-parasite interactions. Ten RL-SAGE libraries from two plant species (Oryza sativa and Zea maize) and one fungal pathogen (Magnaporthe grisea) were generated using this method. Many of the transcripts were novel in comparison with their corresponding expression sequence tag collections. Statistical analysis tools and databases for analyzing the RL-SAGE data were also developed. Our results demonstrate that RL-SAGE is an effective approach for large-scale identification of expressed genes in fungal and plant genomes.

Technical Abstract: Identification of important transcripts from fungal pathogens and host plants is indispensable for full understanding the molecular events occurring during fungal - plant interactions. Recently, we developed an improved LongSAGE method called Robust-LongSAGE (RL-SAGE) for deep transcriptome analysis of fungal and plant genomes. Using this method, we made ten RL-SAGE libraries from two plant species (Oryza sativa and Zea maize) and one fungal pathogen (Magnaporthe grisea). Many of the transcripts identified from these libraries were novel in comparison with their corresponding EST collections. Bioinformatic tools and databases for analyzing the RL-SAGE data were developed. Our results demonstrate that RL-SAGE is an effective approach for large-scale identification of expressed genes in fungal and plant genomes.