NSF Award Abstract - #0321437

AWSFL008-DS3

Deep Transcriptional Profiling of Rice Using Signature Sequencing

Abstract

Rice is important both as a crop plant that feeds more than half of the world's population and as a model for cereal crops of great economic importance in the US. The recent availability of the genomic sequence facilitates functional analysis and molecular studies of the rice genes. However, most of these genes are as yet defined only by computational and not experimental approaches; computational gene predictions may not identify all RNA transcripts within the chromosomal sequence.

This project will use a transcriptional profiling technology called "massively parallel signature sequencing" (MPSS) to characterize the diversity and expression patterns of rice transcripts. Defining the patterns and levels of gene expression in the rice genome will advance our understanding of rice molecular biology and genetic factors controlling important agronomical traits. This analysis of rice has broad practical implications for the improvement of other economically important cereals, such as corn, wheat, sorghum and barley, because nearly all genes present in these species are likely to have homologs in rice. The MPSS data will be used to identify genes missed by computational approaches and will provide data that validate many genes previously predicted but never confirmed experimentally. MPSS will be used to assess gene expression under the following conditions:

-In diverse untreated rice tissues and a subset of these tissues under cold, drought and salt stress.

-In fungal- or bacterial-infected rice tissues in both susceptible and resistant plants. A series of timepoints will be assayed during the infection or resistance response.

-In an 'Indica' rice background and in a hybrid of Indica x Japonica; this experiment will measure differences in gene expression between the two rice varieties for which genomic sequence is available.

The novel transcripts identified by the MPSS technology will be validate by microarray analysis and by sequencing more than 500 novel transcripts. All of these data (MPSS, microarray, and sequence validation of transcripts) will be made publicly available through a project webpage. This web site will include query and analysis tools to facilitate public use of the rice MPSS data and will display the abundance and chromosomal locations of rice MPSS signatures. This website will be similar to that which we have developed for the model dicot, Arabidopsis (http://www.dbi.udel.edu/mpss).