Magnaporthe grisea infection triggers RNA variation and antisense transcript expression in rice.

Rice blast disease, caused by the fungal pathogen Magnaporthe grisea, is an excellent model system to study plant-fungal interactions and host defense responses. In this study, comprehensive analysis of the rice (Oryza sativa) transcriptome after M. grisea infection was conducted using robust-long serial analysis of gene expression.

Use of robust-long serial analysis of gene expression to identify novel fungal and plant genes involved in host-pathogen interactions.

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-long serial analysis of gene expression (RL-SAGE) for deep transcriptome analysis of fungal and plant genomes. Using this method, we made 10 RL-SAGE libraries from two plant species (Oryza sativa and Zea maize) and one fungal pathogen (Magnaporthe grisea).

MGOS: A resource for studying Magnaporthe grisea and Oryza sativa interactions.

The MGOS (Magnaporthe grisea Oryza sativa) web-based database contains data from Oryza sativa and Magnaporthe grisea interaction experiments in which M. grisea is the fungal pathogen that causes the rice blast disease. In order to study the interactions, a consortium of fungal and rice geneticists was formed to construct a comprehensive set of experiments that would elucidate information about the gene expression of both rice and M.

FPC Web tools for rice, maize, and distribution.

Many clone-based physical maps have been built with the FingerPrinted Contig (FPC) software, which is written in C and runs locally for fast and flexible analysis. If the maps were viewable only from FPC, they would not be as useful to the whole community since FPC must be installed on the user machine and the database downloaded. Hence, we have created a set of Web tools so users can easily view the FPC data and perform salient queries with standard browsers.

'PACLIMS': a component LIM system for high-throughput functional genomic analysis.

Background: Recent advances in sequencing techniques leading to cost reduction have resulted in the generation of a growing number of sequenced eukaryotic genomes. Computational tools greatly assist in defining open reading frames and assigning tentative annotations. However, gene functions cannot be asserted without biological support through, among other things, mutational analysis.